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157653
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"""Integration tests for the langchain tracer module."""
import asyncio
import os
from aiohttp import ClientSession
from langchain_core.callbacks.manager import atrace_as_chain_group, trace_as_chain_group
from langchain_core.prompts import PromptTemplate
from langchain_core.tracers.context import tracing_v2_enabled
from langchain_community.chat_models import ChatOpenAI
from langchain_community.llms import OpenAI
questions = [
(
"Who won the US Open men's final in 2019? "
"What is his age raised to the 0.334 power?"
),
(
"Who is Olivia Wilde's boyfriend? "
"What is his current age raised to the 0.23 power?"
),
(
"Who won the most recent formula 1 grand prix? "
"What is their age raised to the 0.23 power?"
),
(
"Who won the US Open women's final in 2019? "
"What is her age raised to the 0.34 power?"
),
("Who is Beyonce's husband? " "What is his age raised to the 0.19 power?"),
]
def test_tracing_sequential() -> None:
from langchain.agents import AgentType, initialize_agent, load_tools
os.environ["LANGCHAIN_TRACING"] = "true"
for q in questions[:3]:
llm = OpenAI(temperature=0)
tools = load_tools(["llm-math", "serpapi"], llm=llm)
agent = initialize_agent(
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
agent.run(q)
def test_tracing_session_env_var() -> None:
from langchain.agents import AgentType, initialize_agent, load_tools
os.environ["LANGCHAIN_TRACING"] = "true"
os.environ["LANGCHAIN_SESSION"] = "my_session"
llm = OpenAI(temperature=0)
tools = load_tools(["llm-math", "serpapi"], llm=llm)
agent = initialize_agent(
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
agent.run(questions[0])
if "LANGCHAIN_SESSION" in os.environ:
del os.environ["LANGCHAIN_SESSION"]
async def test_tracing_concurrent() -> None:
from langchain.agents import AgentType, initialize_agent, load_tools
os.environ["LANGCHAIN_TRACING"] = "true"
aiosession = ClientSession()
llm = OpenAI(temperature=0)
async_tools = load_tools(["llm-math", "serpapi"], llm=llm, aiosession=aiosession)
agent = initialize_agent(
async_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
tasks = [agent.arun(q) for q in questions[:3]]
await asyncio.gather(*tasks)
await aiosession.close()
async def test_tracing_concurrent_bw_compat_environ() -> None:
from langchain.agents import AgentType, initialize_agent, load_tools
os.environ["LANGCHAIN_HANDLER"] = "langchain"
if "LANGCHAIN_TRACING" in os.environ:
del os.environ["LANGCHAIN_TRACING"]
aiosession = ClientSession()
llm = OpenAI(temperature=0)
async_tools = load_tools(["llm-math", "serpapi"], llm=llm, aiosession=aiosession)
agent = initialize_agent(
async_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
tasks = [agent.arun(q) for q in questions[:3]]
await asyncio.gather(*tasks)
await aiosession.close()
if "LANGCHAIN_HANDLER" in os.environ:
del os.environ["LANGCHAIN_HANDLER"]
async def test_tracing_v2_environment_variable() -> None:
from langchain.agents import AgentType, initialize_agent, load_tools
os.environ["LANGCHAIN_TRACING_V2"] = "true"
aiosession = ClientSession()
llm = OpenAI(temperature=0)
async_tools = load_tools(["llm-math", "serpapi"], llm=llm, aiosession=aiosession)
agent = initialize_agent(
async_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
tasks = [agent.arun(q) for q in questions[:3]]
await asyncio.gather(*tasks)
await aiosession.close()
def test_tracing_v2_context_manager() -> None:
from langchain.agents import AgentType, initialize_agent, load_tools
llm = ChatOpenAI(temperature=0)
tools = load_tools(["llm-math", "serpapi"], llm=llm)
agent = initialize_agent(
tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
if "LANGCHAIN_TRACING_V2" in os.environ:
del os.environ["LANGCHAIN_TRACING_V2"]
with tracing_v2_enabled():
agent.run(questions[0]) # this should be traced
agent.run(questions[0]) # this should not be traced
def test_tracing_v2_chain_with_tags() -> None:
from langchain.chains.constitutional_ai.base import ConstitutionalChain
from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
from langchain.chains.llm import LLMChain
llm = OpenAI(temperature=0)
chain = ConstitutionalChain.from_llm(
llm,
chain=LLMChain.from_string(llm, "Q: {question} A:"),
tags=["only-root"],
constitutional_principles=[
ConstitutionalPrinciple(
critique_request="Tell if this answer is good.",
revision_request="Give a better answer.",
)
],
)
if "LANGCHAIN_TRACING_V2" in os.environ:
del os.environ["LANGCHAIN_TRACING_V2"]
with tracing_v2_enabled():
chain.run("what is the meaning of life", tags=["a-tag"])
def test_tracing_v2_agent_with_metadata() -> None:
from langchain.agents import AgentType, initialize_agent, load_tools
os.environ["LANGCHAIN_TRACING_V2"] = "true"
llm = OpenAI(temperature=0)
chat = ChatOpenAI(temperature=0)
tools = load_tools(["llm-math", "serpapi"], llm=llm)
agent = initialize_agent(
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
chat_agent = initialize_agent(
tools, chat, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
agent.run(questions[0], tags=["a-tag"], metadata={"a": "b", "c": "d"})
chat_agent.run(questions[0], tags=["a-tag"], metadata={"a": "b", "c": "d"})
async def test_tracing_v2_async_agent_with_metadata() -> None:
from langchain.agents import AgentType, initialize_agent, load_tools
os.environ["LANGCHAIN_TRACING_V2"] = "true"
llm = OpenAI(temperature=0, metadata={"f": "g", "h": "i"})
chat = ChatOpenAI(temperature=0, metadata={"f": "g", "h": "i"})
async_tools = load_tools(["llm-math", "serpapi"], llm=llm)
agent = initialize_agent(
async_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
chat_agent = initialize_agent(
async_tools,
chat,
agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
)
await agent.arun(questions[0], tags=["a-tag"], metadata={"a": "b", "c": "d"})
await chat_agent.arun(questions[0], tags=["a-tag"], metadata={"a": "b", "c": "d"})
| |
157675
|
"""Test AzureChatOpenAI wrapper."""
import os
from typing import Any
import pytest
from langchain_core.callbacks import CallbackManager
from langchain_core.messages import BaseMessage, HumanMessage
from langchain_core.outputs import ChatGeneration, ChatResult, LLMResult
from langchain_community.chat_models import AzureChatOpenAI
from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
OPENAI_API_VERSION = os.environ.get("AZURE_OPENAI_API_VERSION", "")
OPENAI_API_BASE = os.environ.get("AZURE_OPENAI_API_BASE", "")
OPENAI_API_KEY = os.environ.get("AZURE_OPENAI_API_KEY", "")
DEPLOYMENT_NAME = os.environ.get(
"AZURE_OPENAI_DEPLOYMENT_NAME",
os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME", ""),
)
def _get_llm(**kwargs: Any) -> AzureChatOpenAI:
return AzureChatOpenAI( # type: ignore[call-arg]
deployment_name=DEPLOYMENT_NAME,
openai_api_version=OPENAI_API_VERSION,
azure_endpoint=OPENAI_API_BASE,
openai_api_key=OPENAI_API_KEY,
**kwargs,
)
@pytest.mark.scheduled
@pytest.fixture
def llm() -> AzureChatOpenAI:
return _get_llm(
max_tokens=10,
)
def test_chat_openai(llm: AzureChatOpenAI) -> None:
"""Test AzureChatOpenAI wrapper."""
message = HumanMessage(content="Hello")
response = llm.invoke([message])
assert isinstance(response, BaseMessage)
assert isinstance(response.content, str)
@pytest.mark.scheduled
def test_chat_openai_generate() -> None:
"""Test AzureChatOpenAI wrapper with generate."""
chat = _get_llm(max_tokens=10, n=2)
message = HumanMessage(content="Hello")
response = chat.generate([[message], [message]])
assert isinstance(response, LLMResult)
assert len(response.generations) == 2
for generations in response.generations:
assert len(generations) == 2
for generation in generations:
assert isinstance(generation, ChatGeneration)
assert isinstance(generation.text, str)
assert generation.text == generation.message.content
@pytest.mark.scheduled
def test_chat_openai_multiple_completions() -> None:
"""Test AzureChatOpenAI wrapper with multiple completions."""
chat = _get_llm(max_tokens=10, n=5)
message = HumanMessage(content="Hello")
response = chat._generate([message])
assert isinstance(response, ChatResult)
assert len(response.generations) == 5
for generation in response.generations:
assert isinstance(generation.message, BaseMessage)
assert isinstance(generation.message.content, str)
@pytest.mark.scheduled
def test_chat_openai_streaming() -> None:
"""Test that streaming correctly invokes on_llm_new_token callback."""
callback_handler = FakeCallbackHandler()
callback_manager = CallbackManager([callback_handler])
chat = _get_llm(
max_tokens=10,
streaming=True,
temperature=0,
callback_manager=callback_manager,
verbose=True,
)
message = HumanMessage(content="Hello")
response = chat.invoke([message])
assert callback_handler.llm_streams > 0
assert isinstance(response, BaseMessage)
@pytest.mark.scheduled
def test_chat_openai_streaming_generation_info() -> None:
"""Test that generation info is preserved when streaming."""
class _FakeCallback(FakeCallbackHandler):
saved_things: dict = {}
def on_llm_end(
self,
*args: Any,
**kwargs: Any,
) -> Any:
# Save the generation
self.saved_things["generation"] = args[0]
callback = _FakeCallback()
callback_manager = CallbackManager([callback])
chat = _get_llm(
max_tokens=2,
temperature=0,
callback_manager=callback_manager,
)
list(chat.stream("hi"))
generation = callback.saved_things["generation"]
# `Hello!` is two tokens, assert that that is what is returned
assert generation.generations[0][0].text == "Hello!"
@pytest.mark.scheduled
async def test_async_chat_openai() -> None:
"""Test async generation."""
chat = _get_llm(max_tokens=10, n=2)
message = HumanMessage(content="Hello")
response = await chat.agenerate([[message], [message]])
assert isinstance(response, LLMResult)
assert len(response.generations) == 2
for generations in response.generations:
assert len(generations) == 2
for generation in generations:
assert isinstance(generation, ChatGeneration)
assert isinstance(generation.text, str)
assert generation.text == generation.message.content
@pytest.mark.scheduled
async def test_async_chat_openai_streaming() -> None:
"""Test that streaming correctly invokes on_llm_new_token callback."""
callback_handler = FakeCallbackHandler()
callback_manager = CallbackManager([callback_handler])
chat = _get_llm(
max_tokens=10,
streaming=True,
temperature=0,
callback_manager=callback_manager,
verbose=True,
)
message = HumanMessage(content="Hello")
response = await chat.agenerate([[message], [message]])
assert callback_handler.llm_streams > 0
assert isinstance(response, LLMResult)
assert len(response.generations) == 2
for generations in response.generations:
assert len(generations) == 1
for generation in generations:
assert isinstance(generation, ChatGeneration)
assert isinstance(generation.text, str)
assert generation.text == generation.message.content
@pytest.mark.scheduled
def test_openai_streaming(llm: AzureChatOpenAI) -> None:
"""Test streaming tokens from OpenAI."""
for token in llm.stream("I'm Pickle Rick"):
assert isinstance(token.content, str)
@pytest.mark.scheduled
async def test_openai_astream(llm: AzureChatOpenAI) -> None:
"""Test streaming tokens from OpenAI."""
async for token in llm.astream("I'm Pickle Rick"):
assert isinstance(token.content, str)
@pytest.mark.scheduled
async def test_openai_abatch(llm: AzureChatOpenAI) -> None:
"""Test streaming tokens from AzureChatOpenAI."""
result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
for token in result:
assert isinstance(token.content, str)
@pytest.mark.scheduled
async def test_openai_abatch_tags(llm: AzureChatOpenAI) -> None:
"""Test batch tokens from AzureChatOpenAI."""
result = await llm.abatch(
["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
)
for token in result:
assert isinstance(token.content, str)
@pytest.mark.scheduled
def test_openai_batch(llm: AzureChatOpenAI) -> None:
"""Test batch tokens from AzureChatOpenAI."""
result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
for token in result:
assert isinstance(token.content, str)
@pytest.mark.scheduled
async def test_openai_ainvoke(llm: AzureChatOpenAI) -> None:
"""Test invoke tokens from AzureChatOpenAI."""
result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
assert isinstance(result.content, str)
@pytest.mark.scheduled
def test_openai_invoke(llm: AzureChatOpenAI) -> None:
"""Test invoke tokens from AzureChatOpenAI."""
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
assert isinstance(result.content, str)
| |
157826
|
def test_chroma_update_document() -> None:
"""Test the update_document function in the Chroma class."""
# Make a consistent embedding
embedding = ConsistentFakeEmbeddings()
# Initial document content and id
initial_content = "foo"
document_id = "doc1"
# Create an instance of Document with initial content and metadata
original_doc = Document(page_content=initial_content, metadata={"page": "0"})
# Initialize a Chroma instance with the original document
docsearch = Chroma.from_documents(
collection_name="test_collection",
documents=[original_doc],
embedding=embedding,
ids=[document_id],
)
old_embedding = docsearch._collection.peek()["embeddings"][
docsearch._collection.peek()["ids"].index(document_id)
]
# Define updated content for the document
updated_content = "updated foo"
# Create a new Document instance with the updated content and the same id
updated_doc = Document(page_content=updated_content, metadata={"page": "0"})
# Update the document in the Chroma instance
docsearch.update_document(document_id=document_id, document=updated_doc)
# Perform a similarity search with the updated content
output = docsearch.similarity_search(updated_content, k=1)
# Assert that the updated document is returned by the search
assert output == [Document(page_content=updated_content, metadata={"page": "0"})]
# Assert that the new embedding is correct
new_embedding = docsearch._collection.peek()["embeddings"][
docsearch._collection.peek()["ids"].index(document_id)
]
assert new_embedding == embedding.embed_documents([updated_content])[0]
assert new_embedding != old_embedding
def test_chroma_with_relevance_score() -> None:
"""Test to make sure the relevance score is scaled to 0-1."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = Chroma.from_texts(
collection_name="test_collection",
texts=texts,
embedding=FakeEmbeddings(),
metadatas=metadatas,
collection_metadata={"hnsw:space": "l2"},
)
output = docsearch.similarity_search_with_relevance_scores("foo", k=3)
assert output == [
(Document(page_content="foo", metadata={"page": "0"}), 1.0),
(Document(page_content="bar", metadata={"page": "1"}), 0.8),
(Document(page_content="baz", metadata={"page": "2"}), 0.5),
]
def test_chroma_with_relevance_score_custom_normalization_fn() -> None:
"""Test searching with relevance score and custom normalization function."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = Chroma.from_texts(
collection_name="test_collection",
texts=texts,
embedding=FakeEmbeddings(),
metadatas=metadatas,
relevance_score_fn=lambda d: d * 0,
collection_metadata={"hnsw:space": "l2"},
)
output = docsearch.similarity_search_with_relevance_scores("foo", k=3)
assert output == [
(Document(page_content="foo", metadata={"page": "0"}), -0.0),
(Document(page_content="bar", metadata={"page": "1"}), -0.0),
(Document(page_content="baz", metadata={"page": "2"}), -0.0),
]
def test_init_from_client() -> None:
import chromadb
client = chromadb.Client(chromadb.config.Settings())
Chroma(client=client)
def test_init_from_client_settings() -> None:
import chromadb
client_settings = chromadb.config.Settings()
Chroma(client_settings=client_settings)
def test_chroma_add_documents_no_metadata() -> None:
db = Chroma(embedding_function=FakeEmbeddings())
db.add_documents([Document(page_content="foo")])
def test_chroma_add_documents_mixed_metadata() -> None:
db = Chroma(embedding_function=FakeEmbeddings())
docs = [
Document(page_content="foo"),
Document(page_content="bar", metadata={"baz": 1}),
]
ids = ["0", "1"]
actual_ids = db.add_documents(docs, ids=ids)
assert actual_ids == ids
search = db.similarity_search("foo bar")
assert sorted(search, key=lambda d: d.page_content) == sorted(
docs, key=lambda d: d.page_content
)
def is_api_accessible(url: str) -> bool:
try:
response = requests.get(url)
return response.status_code == 200
except Exception:
return False
def batch_support_chroma_version() -> bool:
try:
import chromadb
except Exception:
return False
major, minor, patch = chromadb.__version__.split(".")
if int(major) == 0 and int(minor) >= 4 and int(patch) >= 10:
return True
return False
@pytest.mark.requires("chromadb")
@pytest.mark.skipif(
not is_api_accessible("http://localhost:8000/api/v1/heartbeat"),
reason="API not accessible",
)
@pytest.mark.skipif(
not batch_support_chroma_version(),
reason="ChromaDB version does not support batching",
)
def test_chroma_large_batch() -> None:
import chromadb
client = chromadb.HttpClient()
embedding_function = Fak(size=255)
col = client.get_or_create_collection(
"my_collection",
embedding_function=embedding_function.embed_documents, # type: ignore
)
docs = ["This is a test document"] * (client.max_batch_size + 100)
Chroma.from_texts(
client=client,
collection_name=col.name,
texts=docs,
embedding=embedding_function,
ids=[str(uuid.uuid4()) for _ in range(len(docs))],
)
@pytest.mark.requires("chromadb")
@pytest.mark.skipif(
not is_api_accessible("http://localhost:8000/api/v1/heartbeat"),
reason="API not accessible",
)
@pytest.mark.skipif(
not batch_support_chroma_version(),
reason="ChromaDB version does not support batching",
)
def test_chroma_large_batch_update() -> None:
import chromadb
client = chromadb.HttpClient()
embedding_function = Fak(size=255)
col = client.get_or_create_collection(
"my_collection",
embedding_function=embedding_function.embed_documents, # type: ignore
)
docs = ["This is a test document"] * (client.max_batch_size + 100)
ids = [str(uuid.uuid4()) for _ in range(len(docs))]
db = Chroma.from_texts(
client=client,
collection_name=col.name,
texts=docs,
embedding=embedding_function,
ids=ids,
)
new_docs = [
Document(
page_content="This is a new test document", metadata={"doc_id": f"{i}"}
)
for i in range(len(docs) - 10)
]
new_ids = [_id for _id in ids[: len(new_docs)]]
db.update_documents(ids=new_ids, documents=new_docs)
@pytest.mark.requires("chromadb")
@pytest.mark.skipif(
not is_api_accessible("http://localhost:8000/api/v1/heartbeat"),
reason="API not accessible",
)
@pytest.mark.skipif(
batch_support_chroma_version(), reason="ChromaDB version does not support batching"
)
def test_chroma_legacy_batching() -> None:
import chromadb
client = chromadb.HttpClient()
embedding_function = Fak(size=255)
col = client.get_or_create_collection(
"my_collection",
embedding_function=embedding_function.embed_documents, # type: ignore
)
docs = ["This is a test document"] * 100
Chroma.from_texts(
client=client,
collection_name=col.name,
texts=docs,
embedding=embedding_function,
ids=[str(uuid.uuid4()) for _ in range(len(docs))],
)
| |
157835
|
import importlib
import os
import time
import uuid
from typing import TYPE_CHECKING, List
import numpy as np
import pytest
from langchain_core.documents import Document
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores.pinecone import Pinecone
if TYPE_CHECKING:
import pinecone
index_name = "langchain-test-index" # name of the index
namespace_name = "langchain-test-namespace" # name of the namespace
dimension = 1536 # dimension of the embeddings
def reset_pinecone() -> None:
assert os.environ.get("PINECONE_API_KEY") is not None
assert os.environ.get("PINECONE_ENVIRONMENT") is not None
import pinecone
importlib.reload(pinecone)
pinecone.init(
api_key=os.environ.get("PINECONE_API_KEY"),
environment=os.environ.get("PINECONE_ENVIRONMENT"),
)
class TestPinecone:
index: "pinecone.Index"
@classmethod
def setup_class(cls) -> None:
import pinecone
reset_pinecone()
cls.index = pinecone.Index(index_name)
if index_name in pinecone.list_indexes():
index_stats = cls.index.describe_index_stats()
if index_stats["dimension"] == dimension:
# delete all the vectors in the index if the dimension is the same
# from all namespaces
index_stats = cls.index.describe_index_stats()
for _namespace_name in index_stats["namespaces"].keys():
cls.index.delete(delete_all=True, namespace=_namespace_name)
else:
pinecone.delete_index(index_name)
pinecone.create_index(name=index_name, dimension=dimension)
else:
pinecone.create_index(name=index_name, dimension=dimension)
# insure the index is empty
index_stats = cls.index.describe_index_stats()
assert index_stats["dimension"] == dimension
if index_stats["namespaces"].get(namespace_name) is not None:
assert index_stats["namespaces"][namespace_name]["vector_count"] == 0
@classmethod
def teardown_class(cls) -> None:
index_stats = cls.index.describe_index_stats()
for _namespace_name in index_stats["namespaces"].keys():
cls.index.delete(delete_all=True, namespace=_namespace_name)
reset_pinecone()
@pytest.fixture(autouse=True)
def setup(self) -> None:
# delete all the vectors in the index
index_stats = self.index.describe_index_stats()
for _namespace_name in index_stats["namespaces"].keys():
self.index.delete(delete_all=True, namespace=_namespace_name)
reset_pinecone()
@pytest.mark.vcr()
def test_from_texts(
self, texts: List[str], embedding_openai: OpenAIEmbeddings
) -> None:
"""Test end to end construction and search."""
unique_id = uuid.uuid4().hex
needs = f"foobuu {unique_id} booo"
texts.insert(0, needs)
docsearch = Pinecone.from_texts(
texts=texts,
embedding=embedding_openai,
index_name=index_name,
namespace=namespace_name,
)
output = docsearch.similarity_search(unique_id, k=1, namespace=namespace_name)
assert output == [Document(page_content=needs)]
@pytest.mark.vcr()
def test_from_texts_with_metadatas(
self, texts: List[str], embedding_openai: OpenAIEmbeddings
) -> None:
"""Test end to end construction and search."""
unique_id = uuid.uuid4().hex
needs = f"foobuu {unique_id} booo"
texts.insert(0, needs)
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = Pinecone.from_texts(
texts,
embedding_openai,
index_name=index_name,
metadatas=metadatas,
namespace=namespace_name,
)
output = docsearch.similarity_search(needs, k=1, namespace=namespace_name)
# TODO: why metadata={"page": 0.0}) instead of {"page": 0}?
assert output == [Document(page_content=needs, metadata={"page": 0.0})]
@pytest.mark.vcr()
def test_from_texts_with_scores(self, embedding_openai: OpenAIEmbeddings) -> None:
"""Test end to end construction and search with scores and IDs."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = Pinecone.from_texts(
texts,
embedding_openai,
index_name=index_name,
metadatas=metadatas,
namespace=namespace_name,
)
output = docsearch.similarity_search_with_score(
"foo", k=3, namespace=namespace_name
)
docs = [o[0] for o in output]
scores = [o[1] for o in output]
sorted_documents = sorted(docs, key=lambda x: x.metadata["page"])
# TODO: why metadata={"page": 0.0}) instead of {"page": 0}, etc???
assert sorted_documents == [
Document(page_content="foo", metadata={"page": 0.0}),
Document(page_content="bar", metadata={"page": 1.0}),
Document(page_content="baz", metadata={"page": 2.0}),
]
assert scores[0] > scores[1] > scores[2]
def test_from_existing_index_with_namespaces(
self, embedding_openai: OpenAIEmbeddings
) -> None:
"""Test that namespaces are properly handled."""
# Create two indexes with the same name but different namespaces
texts_1 = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts_1))]
Pinecone.from_texts(
texts_1,
embedding_openai,
index_name=index_name,
metadatas=metadatas,
namespace=f"{index_name}-1",
)
texts_2 = ["foo2", "bar2", "baz2"]
metadatas = [{"page": i} for i in range(len(texts_2))]
Pinecone.from_texts(
texts_2,
embedding_openai,
index_name=index_name,
metadatas=metadatas,
namespace=f"{index_name}-2",
)
# Search with namespace
docsearch = Pinecone.from_existing_index(
index_name=index_name,
embedding=embedding_openai,
namespace=f"{index_name}-1",
)
output = docsearch.similarity_search("foo", k=20, namespace=f"{index_name}-1")
# check that we don't get results from the other namespace
page_contents = sorted(set([o.page_content for o in output]))
assert all(content in ["foo", "bar", "baz"] for content in page_contents)
assert all(content not in ["foo2", "bar2", "baz2"] for content in page_contents)
def test_add_documents_with_ids(
self, texts: List[str], embedding_openai: OpenAIEmbeddings
) -> None:
ids = [uuid.uuid4().hex for _ in range(len(texts))]
Pinecone.from_texts(
texts=texts,
ids=ids,
embedding=embedding_openai,
index_name=index_name,
namespace=index_name,
)
index_stats = self.index.describe_index_stats()
assert index_stats["namespaces"][index_name]["vector_count"] == len(texts)
ids_1 = [uuid.uuid4().hex for _ in range(len(texts))]
Pinecone.from_texts(
texts=texts,
ids=ids_1,
embedding=embedding_openai,
index_name=index_name,
namespace=index_name,
)
index_stats = self.index.describe_index_stats()
assert index_stats["namespaces"][index_name]["vector_count"] == len(texts) * 2
assert index_stats["total_vector_count"] == len(texts) * 2
@pytest.mark.vcr()
def test_relevance_score_bound(self, embedding_openai: OpenAIEmbeddings) -> None:
"""Ensures all relevance scores are between 0 and 1."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = Pinecone.from_texts(
texts,
embedding_openai,
index_name=index_name,
metadatas=metadatas,
)
# wait for the index to be ready
time.sleep(20)
output = docsearch.similarity_search_with_relevance_scores("foo", k=3)
assert all(
(1 >= score or np.isclose(score, 1)) and score >= 0 for _, score in output
)
| |
157882
|
@pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
def test_singlestoredb_filter_metadata_7(texts: List[str]) -> None:
"""Test filtering by float"""
table_name = "test_singlestoredb_filter_metadata_7"
drop(table_name)
docs = [
Document(
page_content=t,
metadata={"index": i, "category": "budget", "score": i + 0.5},
)
for i, t in enumerate(texts)
]
docsearch = SingleStoreDB.from_documents(
docs,
FakeEmbeddings(),
distance_strategy=DistanceStrategy.EUCLIDEAN_DISTANCE,
table_name=table_name,
host=TEST_SINGLESTOREDB_URL,
)
output = docsearch.similarity_search(
"bar", k=1, filter={"category": "budget", "score": 2.5}
)
assert output == [
Document(
page_content="baz",
metadata={"index": 2, "category": "budget", "score": 2.5},
)
]
docsearch.drop()
@pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
def test_singlestoredb_as_retriever(texts: List[str]) -> None:
table_name = "test_singlestoredb_8"
drop(table_name)
docsearch = SingleStoreDB.from_texts(
texts,
FakeEmbeddings(),
distance_strategy=DistanceStrategy.EUCLIDEAN_DISTANCE,
table_name=table_name,
host=TEST_SINGLESTOREDB_URL,
)
retriever = docsearch.as_retriever(search_kwargs={"k": 2})
output = retriever.invoke("foo")
assert output == [
Document(
page_content="foo",
),
Document(
page_content="bar",
),
]
docsearch.drop()
@pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
def test_singlestoredb_add_image(texts: List[str]) -> None:
"""Test adding images"""
table_name = "test_singlestoredb_add_image"
drop(table_name)
docsearch = SingleStoreDB(
RandomEmbeddings(),
table_name=table_name,
host=TEST_SINGLESTOREDB_URL,
)
temp_files = []
for _ in range(3):
temp_file = tempfile.NamedTemporaryFile(delete=False)
temp_file.write(b"foo")
temp_file.close()
temp_files.append(temp_file.name)
docsearch.add_images(temp_files)
output = docsearch.similarity_search("foo", k=1)
assert output[0].page_content in temp_files
docsearch.drop()
@pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@pytest.mark.skipif(
not langchain_experimental_installed, reason="langchain_experimental not installed"
)
def test_singestoredb_add_image2() -> None:
table_name = "test_singlestoredb_add_images"
drop(table_name)
docsearch = SingleStoreDB(
OpenCLIPEmbeddings(),
table_name=table_name,
host=TEST_SINGLESTOREDB_URL,
)
image_uris = sorted(
[
os.path.join(TEST_IMAGES_DIR, image_name)
for image_name in os.listdir(TEST_IMAGES_DIR)
if image_name.endswith(".jpg")
]
)
docsearch.add_images(image_uris)
output = docsearch.similarity_search("horse", k=1)
assert "horse" in output[0].page_content
docsearch.drop()
@pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
def test_singlestoredb_text_only_search(snow_rain_docs: List[Document]) -> None:
table_name = "test_singlestoredb_text_only_search"
drop(table_name)
docsearch = SingleStoreDB(
RandomEmbeddings(),
table_name=table_name,
use_full_text_search=True,
host=TEST_SINGLESTOREDB_URL,
)
docsearch.add_documents(snow_rain_docs)
output = docsearch.similarity_search(
"rainstorm in parched desert",
k=3,
filter={"count": "1"},
search_strategy=SingleStoreDB.SearchStrategy.TEXT_ONLY,
)
assert len(output) == 2
assert (
"In the parched desert, a sudden rainstorm brought relief,"
in output[0].page_content
)
assert (
"Blanketing the countryside in a soft, pristine layer" in output[1].page_content
)
output = docsearch.similarity_search(
"snowfall in countryside",
k=3,
search_strategy=SingleStoreDB.SearchStrategy.TEXT_ONLY,
)
assert len(output) == 3
assert (
"Blanketing the countryside in a soft, pristine layer,"
in output[0].page_content
)
docsearch.drop()
@pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
def test_singlestoredb_filter_by_text_search(snow_rain_docs: List[Document]) -> None:
table_name = "test_singlestoredb_filter_by_text_search"
drop(table_name)
embeddings = IncrementalEmbeddings()
docsearch = SingleStoreDB.from_documents(
snow_rain_docs,
embeddings,
table_name=table_name,
use_full_text_search=True,
use_vector_index=True,
vector_size=2,
host=TEST_SINGLESTOREDB_URL,
)
output = docsearch.similarity_search(
"rainstorm in parched desert",
k=1,
search_strategy=SingleStoreDB.SearchStrategy.FILTER_BY_TEXT,
filter_threshold=0,
)
assert len(output) == 1
assert (
"In the parched desert, a sudden rainstorm brought relief"
in output[0].page_content
)
drop(table_name)
@pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
def test_singlestoredb_filter_by_vector_search1(snow_rain_docs: List[Document]) -> None:
table_name = "test_singlestoredb_filter_by_vector_search1"
drop(table_name)
embeddings = IncrementalEmbeddings()
docsearch = SingleStoreDB.from_documents(
snow_rain_docs,
embeddings,
table_name=table_name,
use_full_text_search=True,
use_vector_index=True,
vector_size=2,
host=TEST_SINGLESTOREDB_URL,
)
output = docsearch.similarity_search(
"rainstorm in parched desert, rain",
k=1,
filter={"category": "rain"},
search_strategy=SingleStoreDB.SearchStrategy.FILTER_BY_VECTOR,
filter_threshold=-0.2,
)
assert len(output) == 1
assert (
"High in the mountains, the rain transformed into a delicate"
in output[0].page_content
)
docsearch.drop()
@pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
def test_singlestoredb_filter_by_vector_search2(snow_rain_docs: List[Document]) -> None:
table_name = "test_singlestoredb_filter_by_vector_search2"
drop(table_name)
embeddings = IncrementalEmbeddings()
docsearch = SingleStoreDB.from_documents(
snow_rain_docs,
embeddings,
distance_strategy=DistanceStrategy.EUCLIDEAN_DISTANCE,
table_name=table_name,
use_full_text_search=True,
use_vector_index=True,
vector_size=2,
host=TEST_SINGLESTOREDB_URL,
)
output = docsearch.similarity_search(
"rainstorm in parched desert, rain",
k=1,
filter={"group": "a"},
search_strategy=SingleStoreDB.SearchStrategy.FILTER_BY_VECTOR,
filter_threshold=1.5,
)
assert len(output) == 1
assert (
"Amidst the bustling cityscape, the rain fell relentlessly"
in output[0].page_content
)
docsearch.drop()
@pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
def test_singlestoredb_weighted_sum_search_unsupported_strategy(
snow_rain_docs: List[Document],
) -> None:
table_name = "test_singlestoredb_waighted_sum_search_unsupported_strategy"
drop(table_name)
embeddings = IncrementalEmbeddings()
docsearch = SingleStoreDB.from_documents(
snow_rain_docs,
embeddings,
table_name=table_name,
use_full_text_search=True,
use_vector_index=True,
vector_size=2,
host=TEST_SINGLESTOREDB_URL,
distance_strategy=DistanceStrategy.EUCLIDEAN_DISTANCE,
)
try:
docsearch.similarity_search(
"rainstorm in parched desert, rain",
k=1,
search_strategy=SingleStoreDB.SearchStrategy.WEIGHTED_SUM,
)
except ValueError as e:
assert "Search strategy WEIGHTED_SUM is not" in str(e)
docsearch.drop()
| |
157899
|
def test_add_texts_with_given_embedding(self, weaviate_url: str) -> None:
texts = ["foo", "bar", "baz"]
embedding = FakeEmbeddings()
docsearch = Weaviate.from_texts(
texts, embedding=embedding, weaviate_url=weaviate_url
)
docsearch.add_texts(["foo"])
output = docsearch.similarity_search_by_vector(
embedding.embed_query("foo"), k=2
)
assert output == [
Document(page_content="foo"),
Document(page_content="foo"),
]
def test_add_texts_with_given_uuids(self, weaviate_url: str) -> None:
texts = ["foo", "bar", "baz"]
embedding = FakeEmbeddings()
uuids = [uuid.uuid5(uuid.NAMESPACE_DNS, text) for text in texts]
docsearch = Weaviate.from_texts(
texts,
embedding=embedding,
weaviate_url=weaviate_url,
uuids=uuids,
)
# Weaviate replaces the object if the UUID already exists
docsearch.add_texts(["foo"], uuids=[uuids[0]])
output = docsearch.similarity_search_by_vector(
embedding.embed_query("foo"), k=2
)
assert output[0] == Document(page_content="foo")
assert output[1] != Document(page_content="foo")
| |
157903
|
"""Test Deep Lake functionality."""
import pytest
from langchain_core.documents import Document
from pytest import FixtureRequest
from langchain_community.vectorstores import DeepLake
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
@pytest.fixture
def deeplake_datastore() -> DeepLake: # type: ignore[misc]
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = DeepLake.from_texts(
dataset_path="./test_path",
texts=texts,
metadatas=metadatas,
embedding_function=FakeEmbeddings(),
overwrite=True,
)
yield docsearch
docsearch.delete_dataset()
@pytest.fixture(params=["L1", "L2", "max", "cos"])
def distance_metric(request: FixtureRequest) -> str:
return request.param
def test_deeplake() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = DeepLake.from_texts(
dataset_path="mem://test_path", texts=texts, embedding=FakeEmbeddings()
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
def test_deeplake_with_metadatas() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = DeepLake.from_texts(
dataset_path="mem://test_path",
texts=texts,
embedding=FakeEmbeddings(),
metadatas=metadatas,
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"page": "0"})]
def test_deeplake_with_persistence(deeplake_datastore) -> None: # type: ignore[no-untyped-def]
"""Test end to end construction and search, with persistence."""
output = deeplake_datastore.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"page": "0"})]
# Get a new VectorStore from the persisted directory
docsearch = DeepLake(
dataset_path=deeplake_datastore.vectorstore.dataset_handler.path,
embedding_function=FakeEmbeddings(),
)
output = docsearch.similarity_search("foo", k=1)
# Clean up
docsearch.delete_dataset()
# Persist doesn't need to be called again
# Data will be automatically persisted on object deletion
# Or on program exit
def test_deeplake_overwrite_flag(deeplake_datastore) -> None: # type: ignore[no-untyped-def]
"""Test overwrite behavior"""
dataset_path = deeplake_datastore.vectorstore.dataset_handler.path
output = deeplake_datastore.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"page": "0"})]
# Get a new VectorStore from the persisted directory, with no overwrite (implicit)
docsearch = DeepLake(
dataset_path=dataset_path,
embedding_function=FakeEmbeddings(),
)
output = docsearch.similarity_search("foo", k=1)
# assert page still present
assert output == [Document(page_content="foo", metadata={"page": "0"})]
# Get a new VectorStore from the persisted directory, with no overwrite (explicit)
docsearch = DeepLake(
dataset_path=dataset_path,
embedding_function=FakeEmbeddings(),
overwrite=False,
)
output = docsearch.similarity_search("foo", k=1)
# assert page still present
assert output == [Document(page_content="foo", metadata={"page": "0"})]
# Get a new VectorStore from the persisted directory, with overwrite
docsearch = DeepLake(
dataset_path=dataset_path,
embedding_function=FakeEmbeddings(),
overwrite=True,
)
with pytest.raises(ValueError):
output = docsearch.similarity_search("foo", k=1)
def test_similarity_search(deeplake_datastore) -> None: # type: ignore[no-untyped-def]
"""Test similarity search."""
distance_metric = "cos"
output = deeplake_datastore.similarity_search(
"foo", k=1, distance_metric=distance_metric
)
assert output == [Document(page_content="foo", metadata={"page": "0"})]
tql_query = (
f"SELECT * WHERE "
f"id=='{deeplake_datastore.vectorstore.dataset.id[0].numpy()[0]}'"
)
output = deeplake_datastore.similarity_search(
query="foo", tql_query=tql_query, k=1, distance_metric=distance_metric
)
assert len(output) == 1
def test_similarity_search_by_vector(
deeplake_datastore: DeepLake, distance_metric: str
) -> None:
"""Test similarity search by vector."""
embeddings = FakeEmbeddings().embed_documents(["foo", "bar", "baz"])
output = deeplake_datastore.similarity_search_by_vector(
embeddings[1], k=1, distance_metric=distance_metric
)
assert output == [Document(page_content="bar", metadata={"page": "1"})]
deeplake_datastore.delete_dataset()
def test_similarity_search_with_score(
deeplake_datastore: DeepLake, distance_metric: str
) -> None:
"""Test similarity search with score."""
deeplake_datastore.vectorstore.summary()
output, score = deeplake_datastore.similarity_search_with_score(
"foo", k=1, distance_metric=distance_metric
)[0]
assert output == Document(page_content="foo", metadata={"page": "0"})
if distance_metric == "cos":
assert score == 1.0
else:
assert score == 0.0
deeplake_datastore.delete_dataset()
def test_similarity_search_with_filter(
deeplake_datastore: DeepLake, distance_metric: str
) -> None:
"""Test similarity search."""
output = deeplake_datastore.similarity_search(
"foo",
k=1,
distance_metric=distance_metric,
filter={"metadata": {"page": "1"}},
)
assert output == [Document(page_content="bar", metadata={"page": "1"})]
deeplake_datastore.delete_dataset()
def test_max_marginal_relevance_search(deeplake_datastore: DeepLake) -> None:
"""Test max marginal relevance search by vector."""
output = deeplake_datastore.max_marginal_relevance_search("foo", k=1, fetch_k=2)
assert output == [Document(page_content="foo", metadata={"page": "0"})]
embeddings = FakeEmbeddings().embed_documents(["foo", "bar", "baz"])
output = deeplake_datastore.max_marginal_relevance_search_by_vector(
embeddings[0], k=1, fetch_k=2
)
assert output == [Document(page_content="foo", metadata={"page": "0"})]
deeplake_datastore.delete_dataset()
def test_delete_dataset_by_ids(deeplake_datastore: DeepLake) -> None:
"""Test delete dataset."""
id = deeplake_datastore.vectorstore.dataset.id.data()["value"][0]
deeplake_datastore.delete(ids=[id])
assert (
deeplake_datastore.similarity_search(
"foo", k=1, filter={"metadata": {"page": "0"}}
)
== []
)
assert len(deeplake_datastore.vectorstore) == 2
deeplake_datastore.delete_dataset()
def test_delete_dataset_by_filter(deeplake_datastore: DeepLake) -> None:
"""Test delete dataset."""
deeplake_datastore.delete(filter={"metadata": {"page": "1"}})
assert (
deeplake_datastore.similarity_search(
"bar", k=1, filter={"metadata": {"page": "1"}}
)
== []
)
assert len(deeplake_datastore.vectorstore.dataset) == 2
deeplake_datastore.delete_dataset()
def test_delete_by_path(deeplake_datastore: DeepLake) -> None:
"""Test delete dataset."""
import deeplake
path = deeplake_datastore.dataset_path
DeepLake.force_delete_by_path(path)
assert not deeplake.exists(path)
| |
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|
"""Test HuggingFace Pipeline wrapper."""
from pathlib import Path
from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
from langchain_community.llms.loading import load_llm
from tests.integration_tests.llms.utils import assert_llm_equality
def test_huggingface_pipeline_text_generation() -> None:
"""Test valid call to HuggingFace text generation model."""
llm = HuggingFacePipeline.from_model_id(
model_id="gpt2", task="text-generation", pipeline_kwargs={"max_new_tokens": 10}
)
output = llm.invoke("Say foo:")
assert isinstance(output, str)
def test_huggingface_pipeline_text2text_generation() -> None:
"""Test valid call to HuggingFace text2text generation model."""
llm = HuggingFacePipeline.from_model_id(
model_id="google/flan-t5-small", task="text2text-generation"
)
output = llm.invoke("Say foo:")
assert isinstance(output, str)
def test_huggingface_pipeline_device_map() -> None:
"""Test pipelines specifying the device map parameter."""
llm = HuggingFacePipeline.from_model_id(
model_id="gpt2",
task="text-generation",
device_map="auto",
pipeline_kwargs={"max_new_tokens": 10},
)
output = llm.invoke("Say foo:")
assert isinstance(output, str)
def text_huggingface_pipeline_summarization() -> None:
"""Test valid call to HuggingFace summarization model."""
llm = HuggingFacePipeline.from_model_id(
model_id="facebook/bart-large-cnn", task="summarization"
)
output = llm.invoke("Say foo:")
assert isinstance(output, str)
def test_saving_loading_llm(tmp_path: Path) -> None:
"""Test saving/loading an HuggingFaceHub LLM."""
llm = HuggingFacePipeline.from_model_id(
model_id="gpt2", task="text-generation", pipeline_kwargs={"max_new_tokens": 10}
)
llm.save(file_path=tmp_path / "hf.yaml")
loaded_llm = load_llm(tmp_path / "hf.yaml")
assert_llm_equality(llm, loaded_llm)
def test_init_with_pipeline() -> None:
"""Test initialization with a HF pipeline."""
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline(
"text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
)
llm = HuggingFacePipeline(pipeline=pipe)
output = llm.invoke("Say foo:")
assert isinstance(output, str)
def test_huggingface_pipeline_runtime_kwargs() -> None:
"""Test pipelines specifying the device map parameter."""
llm = HuggingFacePipeline.from_model_id(
model_id="gpt2",
task="text-generation",
)
prompt = "Say foo:"
output = llm.invoke(prompt, pipeline_kwargs={"max_new_tokens": 2})
assert len(output) < 10
ov_config = {"PERFORMANCE_HINT": "LATENCY", "NUM_STREAMS": "1", "CACHE_DIR": ""}
def test_huggingface_pipeline_text_generation_ov() -> None:
"""Test valid call to HuggingFace text generation model with openvino."""
llm = HuggingFacePipeline.from_model_id(
model_id="gpt2",
task="text-generation",
backend="openvino",
model_kwargs={"device": "CPU", "ov_config": ov_config},
pipeline_kwargs={"max_new_tokens": 64},
)
output = llm.invoke("Say foo:")
assert isinstance(output, str)
def test_huggingface_pipeline_text2text_generation_ov() -> None:
"""Test valid call to HuggingFace text2text generation model with openvino."""
llm = HuggingFacePipeline.from_model_id(
model_id="google/flan-t5-small",
task="text2text-generation",
backend="openvino",
model_kwargs={"device": "CPU", "ov_config": ov_config},
pipeline_kwargs={"max_new_tokens": 64},
)
output = llm.invoke("Say foo:")
assert isinstance(output, str)
def text_huggingface_pipeline_summarization_ov() -> None:
"""Test valid call to HuggingFace summarization model with openvino."""
llm = HuggingFacePipeline.from_model_id(
model_id="facebook/bart-large-cnn",
task="summarization",
backend="openvino",
model_kwargs={"device": "CPU", "ov_config": ov_config},
pipeline_kwargs={"max_new_tokens": 64},
)
output = llm.invoke("Say foo:")
assert isinstance(output, str)
| |
158221
|
def test_write_retrieve_keywords() -> None:
from langchain_openai import OpenAIEmbeddings
greetings = Document(
id="greetings",
page_content="Typical Greetings",
metadata={
METADATA_LINKS_KEY: [
Link.incoming(kind="parent", tag="parent"),
],
},
)
doc1 = Document(
id="doc1",
page_content="Hello World",
metadata={
METADATA_LINKS_KEY: [
Link.outgoing(kind="parent", tag="parent"),
Link.bidir(kind="kw", tag="greeting"),
Link.bidir(kind="kw", tag="world"),
],
},
)
doc2 = Document(
id="doc2",
page_content="Hello Earth",
metadata={
METADATA_LINKS_KEY: [
Link.outgoing(kind="parent", tag="parent"),
Link.bidir(kind="kw", tag="greeting"),
Link.bidir(kind="kw", tag="earth"),
],
},
)
store = _get_graph_store(OpenAIEmbeddings, [greetings, doc1, doc2])
# Doc2 is more similar, but World and Earth are similar enough that doc1 also
# shows up.
results: Iterable[Document] = store.similarity_search("Earth", k=2)
assert _result_ids(results) == ["doc2", "doc1"]
results = store.similarity_search("Earth", k=1)
assert _result_ids(results) == ["doc2"]
results = store.traversal_search("Earth", k=2, depth=0)
assert _result_ids(results) == ["doc2", "doc1"]
results = store.traversal_search("Earth", k=2, depth=1)
assert _result_ids(results) == ["doc2", "doc1", "greetings"]
# K=1 only pulls in doc2 (Hello Earth)
results = store.traversal_search("Earth", k=1, depth=0)
assert _result_ids(results) == ["doc2"]
# K=1 only pulls in doc2 (Hello Earth). Depth=1 traverses to parent and via
# keyword edge.
results = store.traversal_search("Earth", k=1, depth=1)
assert set(_result_ids(results)) == {"doc2", "doc1", "greetings"}
def test_metadata() -> None:
store = _get_graph_store(FakeEmbeddings)
store.add_documents(
[
Document(
id="a",
page_content="A",
metadata={
METADATA_LINKS_KEY: [
Link.incoming(kind="hyperlink", tag="http://a"),
Link.bidir(kind="other", tag="foo"),
],
"other": "some other field",
},
)
]
)
results = store.similarity_search("A")
assert len(results) == 1
assert results[0].id == "a"
metadata = results[0].metadata
assert metadata["other"] == "some other field"
assert set(metadata[METADATA_LINKS_KEY]) == {
Link.incoming(kind="hyperlink", tag="http://a"),
Link.bidir(kind="other", tag="foo"),
}
| |
158282
|
class AzureCosmosDBSemanticCache(BaseCache):
"""Cache that uses Cosmos DB Mongo vCore vector-store backend"""
DEFAULT_DATABASE_NAME = "CosmosMongoVCoreCacheDB"
DEFAULT_COLLECTION_NAME = "CosmosMongoVCoreCacheColl"
def __init__(
self,
cosmosdb_connection_string: str,
database_name: str,
collection_name: str,
embedding: Embeddings,
*,
cosmosdb_client: Optional[Any] = None,
num_lists: int = 100,
similarity: CosmosDBSimilarityType = CosmosDBSimilarityType.COS,
kind: CosmosDBVectorSearchType = CosmosDBVectorSearchType.VECTOR_IVF,
dimensions: int = 1536,
m: int = 16,
ef_construction: int = 64,
ef_search: int = 40,
score_threshold: Optional[float] = None,
application_name: str = "LANGCHAIN_CACHING_PYTHON",
):
"""
Args:
cosmosdb_connection_string: Cosmos DB Mongo vCore connection string
cosmosdb_client: Cosmos DB Mongo vCore client
embedding (Embedding): Embedding provider for semantic encoding and search.
database_name: Database name for the CosmosDBMongoVCoreSemanticCache
collection_name: Collection name for the CosmosDBMongoVCoreSemanticCache
num_lists: This integer is the number of clusters that the
inverted file (IVF) index uses to group the vector data.
We recommend that numLists is set to documentCount/1000
for up to 1 million documents and to sqrt(documentCount)
for more than 1 million documents.
Using a numLists value of 1 is akin to performing
brute-force search, which has limited performance
dimensions: Number of dimensions for vector similarity.
The maximum number of supported dimensions is 2000
similarity: Similarity metric to use with the IVF index.
Possible options are:
- CosmosDBSimilarityType.COS (cosine distance),
- CosmosDBSimilarityType.L2 (Euclidean distance), and
- CosmosDBSimilarityType.IP (inner product).
kind: Type of vector index to create.
Possible options are:
- vector-ivf
- vector-hnsw: available as a preview feature only,
to enable visit https://learn.microsoft.com/en-us/azure/azure-resource-manager/management/preview-features
m: The max number of connections per layer (16 by default, minimum
value is 2, maximum value is 100). Higher m is suitable for datasets
with high dimensionality and/or high accuracy requirements.
ef_construction: the size of the dynamic candidate list for constructing
the graph (64 by default, minimum value is 4, maximum
value is 1000). Higher ef_construction will result in
better index quality and higher accuracy, but it will
also increase the time required to build the index.
ef_construction has to be at least 2 * m
ef_search: The size of the dynamic candidate list for search
(40 by default). A higher value provides better
recall at the cost of speed.
score_threshold: Maximum score used to filter the vector search documents.
application_name: Application name for the client for tracking and logging
"""
self._validate_enum_value(similarity, CosmosDBSimilarityType)
self._validate_enum_value(kind, CosmosDBVectorSearchType)
if not cosmosdb_connection_string:
raise ValueError(" CosmosDB connection string can be empty.")
self.cosmosdb_connection_string = cosmosdb_connection_string
self.cosmosdb_client = cosmosdb_client
self.embedding = embedding
self.database_name = database_name or self.DEFAULT_DATABASE_NAME
self.collection_name = collection_name or self.DEFAULT_COLLECTION_NAME
self.num_lists = num_lists
self.dimensions = dimensions
self.similarity = similarity
self.kind = kind
self.m = m
self.ef_construction = ef_construction
self.ef_search = ef_search
self.score_threshold = score_threshold
self._cache_dict: Dict[str, AzureCosmosDBVectorSearch] = {}
self.application_name = application_name
def _index_name(self, llm_string: str) -> str:
hashed_index = _hash(llm_string)
return f"cache:{hashed_index}"
def _get_llm_cache(self, llm_string: str) -> AzureCosmosDBVectorSearch:
index_name = self._index_name(llm_string)
namespace = self.database_name + "." + self.collection_name
# return vectorstore client for the specific llm string
if index_name in self._cache_dict:
return self._cache_dict[index_name]
# create new vectorstore client for the specific llm string
if self.cosmosdb_client:
collection = self.cosmosdb_client[self.database_name][self.collection_name]
self._cache_dict[index_name] = AzureCosmosDBVectorSearch(
collection=collection,
embedding=self.embedding,
index_name=index_name,
)
else:
self._cache_dict[index_name] = (
AzureCosmosDBVectorSearch.from_connection_string(
connection_string=self.cosmosdb_connection_string,
namespace=namespace,
embedding=self.embedding,
index_name=index_name,
application_name=self.application_name,
)
)
# create index for the vectorstore
vectorstore = self._cache_dict[index_name]
if not vectorstore.index_exists():
vectorstore.create_index(
self.num_lists,
self.dimensions,
self.similarity,
self.kind,
self.m,
self.ef_construction,
)
return vectorstore
def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
"""Look up based on prompt and llm_string."""
llm_cache = self._get_llm_cache(llm_string)
generations: List = []
# Read from a Hash
results = llm_cache.similarity_search(
query=prompt,
k=1,
kind=self.kind,
ef_search=self.ef_search,
score_threshold=self.score_threshold, # type: ignore[arg-type]
)
if results:
for document in results:
try:
generations.extend(loads(document.metadata["return_val"]))
except Exception:
logger.warning(
"Retrieving a cache value that could not be deserialized "
"properly. This is likely due to the cache being in an "
"older format. Please recreate your cache to avoid this "
"error."
)
# In a previous life we stored the raw text directly
# in the table, so assume it's in that format.
generations.extend(
_load_generations_from_json(document.metadata["return_val"])
)
return generations if generations else None
def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
"""Update cache based on prompt and llm_string."""
for gen in return_val:
if not isinstance(gen, Generation):
raise ValueError(
"CosmosDBMongoVCoreSemanticCache only supports caching of "
f"normal LLM generations, got {type(gen)}"
)
llm_cache = self._get_llm_cache(llm_string)
metadata = {
"llm_string": llm_string,
"prompt": prompt,
"return_val": dumps([g for g in return_val]),
}
llm_cache.add_texts(texts=[prompt], metadatas=[metadata])
def clear(self, **kwargs: Any) -> None:
"""Clear semantic cache for a given llm_string."""
index_name = self._index_name(kwargs["llm_string"])
if index_name in self._cache_dict:
self._cache_dict[index_name].get_collection().delete_many({})
# self._cache_dict[index_name].clear_collection()
@staticmethod
def _validate_enum_value(value: Any, enum_type: Type[Enum]) -> None:
if not isinstance(value, enum_type):
raise ValueError(f"Invalid enum value: {value}. Expected {enum_type}.")
| |
158284
|
class SingleStoreDBSemanticCache(BaseCache):
"""Cache that uses SingleStore DB as a backend"""
def __init__(
self,
embedding: Embeddings,
*,
cache_table_prefix: str = "cache_",
search_threshold: float = 0.2,
**kwargs: Any,
):
"""Initialize with necessary components.
Args:
embedding (Embeddings): A text embedding model.
cache_table_prefix (str, optional): Prefix for the cache table name.
Defaults to "cache_".
search_threshold (float, optional): The minimum similarity score for
a search result to be considered a match. Defaults to 0.2.
Following arguments pertrain to the SingleStoreDB vector store:
distance_strategy (DistanceStrategy, optional):
Determines the strategy employed for calculating
the distance between vectors in the embedding space.
Defaults to DOT_PRODUCT.
Available options are:
- DOT_PRODUCT: Computes the scalar product of two vectors.
This is the default behavior
- EUCLIDEAN_DISTANCE: Computes the Euclidean distance between
two vectors. This metric considers the geometric distance in
the vector space, and might be more suitable for embeddings
that rely on spatial relationships. This metric is not
compatible with the WEIGHTED_SUM search strategy.
content_field (str, optional): Specifies the field to store the content.
Defaults to "content".
metadata_field (str, optional): Specifies the field to store metadata.
Defaults to "metadata".
vector_field (str, optional): Specifies the field to store the vector.
Defaults to "vector".
id_field (str, optional): Specifies the field to store the id.
Defaults to "id".
use_vector_index (bool, optional): Toggles the use of a vector index.
Works only with SingleStoreDB 8.5 or later. Defaults to False.
If set to True, vector_size parameter is required to be set to
a proper value.
vector_index_name (str, optional): Specifies the name of the vector index.
Defaults to empty. Will be ignored if use_vector_index is set to False.
vector_index_options (dict, optional): Specifies the options for
the vector index. Defaults to {}.
Will be ignored if use_vector_index is set to False. The options are:
index_type (str, optional): Specifies the type of the index.
Defaults to IVF_PQFS.
For more options, please refer to the SingleStoreDB documentation:
https://docs.singlestore.com/cloud/reference/sql-reference/vector-functions/vector-indexing/
vector_size (int, optional): Specifies the size of the vector.
Defaults to 1536. Required if use_vector_index is set to True.
Should be set to the same value as the size of the vectors
stored in the vector_field.
Following arguments pertain to the connection pool:
pool_size (int, optional): Determines the number of active connections in
the pool. Defaults to 5.
max_overflow (int, optional): Determines the maximum number of connections
allowed beyond the pool_size. Defaults to 10.
timeout (float, optional): Specifies the maximum wait time in seconds for
establishing a connection. Defaults to 30.
Following arguments pertain to the database connection:
host (str, optional): Specifies the hostname, IP address, or URL for the
database connection. The default scheme is "mysql".
user (str, optional): Database username.
password (str, optional): Database password.
port (int, optional): Database port. Defaults to 3306 for non-HTTP
connections, 80 for HTTP connections, and 443 for HTTPS connections.
database (str, optional): Database name.
Additional optional arguments provide further customization over the
database connection:
pure_python (bool, optional): Toggles the connector mode. If True,
operates in pure Python mode.
local_infile (bool, optional): Allows local file uploads.
charset (str, optional): Specifies the character set for string values.
ssl_key (str, optional): Specifies the path of the file containing the SSL
key.
ssl_cert (str, optional): Specifies the path of the file containing the SSL
certificate.
ssl_ca (str, optional): Specifies the path of the file containing the SSL
certificate authority.
ssl_cipher (str, optional): Sets the SSL cipher list.
ssl_disabled (bool, optional): Disables SSL usage.
ssl_verify_cert (bool, optional): Verifies the server's certificate.
Automatically enabled if ``ssl_ca`` is specified.
ssl_verify_identity (bool, optional): Verifies the server's identity.
conv (dict[int, Callable], optional): A dictionary of data conversion
functions.
credential_type (str, optional): Specifies the type of authentication to
use: auth.PASSWORD, auth.JWT, or auth.BROWSER_SSO.
autocommit (bool, optional): Enables autocommits.
results_type (str, optional): Determines the structure of the query results:
tuples, namedtuples, dicts.
results_format (str, optional): Deprecated. This option has been renamed to
results_type.
Examples:
Basic Usage:
.. code-block:: python
import langchain
from langchain.cache import SingleStoreDBSemanticCache
from langchain.embeddings import OpenAIEmbeddings
langchain.llm_cache = SingleStoreDBSemanticCache(
embedding=OpenAIEmbeddings(),
host="https://user:password@127.0.0.1:3306/database"
)
Advanced Usage:
.. code-block:: python
import langchain
from langchain.cache import SingleStoreDBSemanticCache
from langchain.embeddings import OpenAIEmbeddings
langchain.llm_cache = = SingleStoreDBSemanticCache(
embeddings=OpenAIEmbeddings(),
use_vector_index=True,
host="127.0.0.1",
port=3306,
user="user",
password="password",
database="db",
table_name="my_custom_table",
pool_size=10,
timeout=60,
)
"""
self._cache_dict: Dict[str, SingleStoreDB] = {}
self.embedding = embedding
self.cache_table_prefix = cache_table_prefix
self.search_threshold = search_threshold
# Pass the rest of the kwargs to the connection.
self.connection_kwargs = kwargs
def _index_name(self, llm_string: str) -> str:
hashed_index = _hash(llm_string)
return f"{self.cache_table_prefix}{hashed_index}"
def _get_llm_cache(self, llm_string: str) -> SingleStoreDB:
index_name = self._index_name(llm_string)
# return vectorstore client for the specific llm string
if index_name not in self._cache_dict:
self._cache_dict[index_name] = SingleStoreDB(
embedding=self.embedding,
table_name=index_name,
**self.connection_kwargs,
)
return self._cache_dict[index_name]
def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
"""Look up based on prompt and llm_string."""
llm_cache = self._get_llm_cache(llm_string)
generations: List = []
# Read from a Hash
results = llm_cache.similarity_search_with_score(
query=prompt,
k=1,
)
if results:
for document_score in results:
if (
document_score[1] > self.search_threshold
and llm_cache.distance_strategy == DistanceStrategy.DOT_PRODUCT
) or (
document_score[1] < self.search_threshold
and llm_cache.distance_strategy
== DistanceStrategy.EUCLIDEAN_DISTANCE
):
generations.extend(loads(document_score[0].metadata["return_val"]))
return generations if generations else None
def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
"""Update cache based on prompt and llm_string."""
for gen in return_val:
if not isinstance(gen, Generation):
raise ValueError(
"SingleStoreDBSemanticCache only supports caching of "
f"normal LLM generations, got {type(gen)}"
)
llm_cache = self._get_llm_cache(llm_string)
metadata = {
"llm_string": llm_string,
"prompt": prompt,
"return_val": dumps([g for g in return_val]),
}
llm_cache.add_texts(texts=[prompt], metadatas=[metadata])
def clear(self, **kwargs: Any) -> None:
"""Clear semantic cache for a given llm_string."""
index_name = self._index_name(kwargs["llm_string"])
if index_name in self._cache_dict:
self._cache_dict[index_name].drop()
del self._cache_dict[index_name]
| |
158298
|
from typing import List, Optional
import aiohttp
import requests
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
class RemoteLangChainRetriever(BaseRetriever):
"""`LangChain API` retriever."""
url: str
"""URL of the remote LangChain API."""
headers: Optional[dict] = None
"""Headers to use for the request."""
input_key: str = "message"
"""Key to use for the input in the request."""
response_key: str = "response"
"""Key to use for the response in the request."""
page_content_key: str = "page_content"
"""Key to use for the page content in the response."""
metadata_key: str = "metadata"
"""Key to use for the metadata in the response."""
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
response = requests.post(
self.url, json={self.input_key: query}, headers=self.headers
)
result = response.json()
return [
Document(
page_content=r[self.page_content_key], metadata=r[self.metadata_key]
)
for r in result[self.response_key]
]
async def _aget_relevant_documents(
self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
) -> List[Document]:
async with aiohttp.ClientSession() as session:
async with session.request(
"POST", self.url, headers=self.headers, json={self.input_key: query}
) as response:
result = await response.json()
return [
Document(
page_content=r[self.page_content_key], metadata=r[self.metadata_key]
)
for r in result[self.response_key]
]
| |
158301
|
"""Wrapper around Embedchain Retriever."""
from __future__ import annotations
from typing import Any, Iterable, List, Optional
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
class EmbedchainRetriever(BaseRetriever):
"""`Embedchain` retriever."""
client: Any
"""Embedchain Pipeline."""
@classmethod
def create(cls, yaml_path: Optional[str] = None) -> EmbedchainRetriever:
"""
Create a EmbedchainRetriever from a YAML configuration file.
Args:
yaml_path: Path to the YAML configuration file. If not provided,
a default configuration is used.
Returns:
An instance of EmbedchainRetriever.
"""
from embedchain import Pipeline
# Create an Embedchain Pipeline instance
if yaml_path:
client = Pipeline.from_config(yaml_path=yaml_path)
else:
client = Pipeline()
return cls(client=client)
def add_texts(
self,
texts: Iterable[str],
) -> List[str]:
"""Run more texts through the embeddings and add to the retriever.
Args:
texts: Iterable of strings/URLs to add to the retriever.
Returns:
List of ids from adding the texts into the retriever.
"""
ids = []
for text in texts:
_id = self.client.add(text)
ids.append(_id)
return ids
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
res = self.client.search(query)
docs = []
for r in res:
docs.append(
Document(
page_content=r["context"],
metadata={
"source": r["metadata"]["url"],
"document_id": r["metadata"]["doc_id"],
},
)
)
return docs
| |
158307
|
from typing import Any, List, Optional
import aiohttp
import requests
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
class ChaindeskRetriever(BaseRetriever):
"""`Chaindesk API` retriever."""
datastore_url: str
top_k: Optional[int]
api_key: Optional[str]
def __init__(
self,
datastore_url: str,
top_k: Optional[int] = None,
api_key: Optional[str] = None,
):
self.datastore_url = datastore_url
self.api_key = api_key
self.top_k = top_k
def _get_relevant_documents(
self,
query: str,
*,
run_manager: CallbackManagerForRetrieverRun,
**kwargs: Any,
) -> List[Document]:
response = requests.post(
self.datastore_url,
json={
"query": query,
**({"topK": self.top_k} if self.top_k is not None else {}),
},
headers={
"Content-Type": "application/json",
**(
{"Authorization": f"Bearer {self.api_key}"}
if self.api_key is not None
else {}
),
},
)
data = response.json()
return [
Document(
page_content=r["text"],
metadata={"source": r["source"], "score": r["score"]},
)
for r in data["results"]
]
async def _aget_relevant_documents(
self,
query: str,
*,
run_manager: AsyncCallbackManagerForRetrieverRun,
**kwargs: Any,
) -> List[Document]:
async with aiohttp.ClientSession() as session:
async with session.request(
"POST",
self.datastore_url,
json={
"query": query,
**({"topK": self.top_k} if self.top_k is not None else {}),
},
headers={
"Content-Type": "application/json",
**(
{"Authorization": f"Bearer {self.api_key}"}
if self.api_key is not None
else {}
),
},
) as response:
data = await response.json()
return [
Document(
page_content=r["text"],
metadata={"source": r["source"], "score": r["score"]},
)
for r in data["results"]
]
| |
158309
|
class WebResearchRetriever(BaseRetriever):
"""`Google Search API` retriever."""
# Inputs
vectorstore: VectorStore = Field(
..., description="Vector store for storing web pages"
)
llm_chain: LLMChain
search: GoogleSearchAPIWrapper = Field(..., description="Google Search API Wrapper")
num_search_results: int = Field(1, description="Number of pages per Google search")
text_splitter: TextSplitter = Field(
RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=50),
description="Text splitter for splitting web pages into chunks",
)
url_database: List[str] = Field(
default_factory=list, description="List of processed URLs"
)
trust_env: bool = Field(
False,
description="Whether to use the http_proxy/https_proxy env variables or "
"check .netrc for proxy configuration",
)
allow_dangerous_requests: bool = False
"""A flag to force users to acknowledge the risks of SSRF attacks when using
this retriever.
Users should set this flag to `True` if they have taken the necessary precautions
to prevent SSRF attacks when using this retriever.
For example, users can run the requests through a properly configured
proxy and prevent the crawler from accidentally crawling internal resources.
"""
def __init__(self, **kwargs: Any) -> None:
"""Initialize the retriever."""
allow_dangerous_requests = kwargs.get("allow_dangerous_requests", False)
if not allow_dangerous_requests:
raise ValueError(
"WebResearchRetriever crawls URLs surfaced through "
"the provided search engine. It is possible that some of those URLs "
"will end up pointing to machines residing on an internal network, "
"leading"
"to an SSRF (Server-Side Request Forgery) attack. "
"To protect yourself against that risk, you can run the requests "
"through a proxy and prevent the crawler from accidentally crawling "
"internal resources."
"If've taken the necessary precautions, you can set "
"`allow_dangerous_requests` to `True`."
)
super().__init__(**kwargs)
@classmethod
def from_llm(
cls,
vectorstore: VectorStore,
llm: BaseLLM,
search: GoogleSearchAPIWrapper,
prompt: Optional[BasePromptTemplate] = None,
num_search_results: int = 1,
text_splitter: RecursiveCharacterTextSplitter = RecursiveCharacterTextSplitter(
chunk_size=1500, chunk_overlap=150
),
trust_env: bool = False,
allow_dangerous_requests: bool = False,
) -> "WebResearchRetriever":
"""Initialize from llm using default template.
Args:
vectorstore: Vector store for storing web pages
llm: llm for search question generation
search: GoogleSearchAPIWrapper
prompt: prompt to generating search questions
num_search_results: Number of pages per Google search
text_splitter: Text splitter for splitting web pages into chunks
trust_env: Whether to use the http_proxy/https_proxy env variables
or check .netrc for proxy configuration
allow_dangerous_requests: A flag to force users to acknowledge
the risks of SSRF attacks when using this retriever
Returns:
WebResearchRetriever
"""
if not prompt:
QUESTION_PROMPT_SELECTOR = ConditionalPromptSelector(
default_prompt=DEFAULT_SEARCH_PROMPT,
conditionals=[
(lambda llm: isinstance(llm, LlamaCpp), DEFAULT_LLAMA_SEARCH_PROMPT)
],
)
prompt = QUESTION_PROMPT_SELECTOR.get_prompt(llm)
# Use chat model prompt
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
output_parser=QuestionListOutputParser(),
)
return cls(
vectorstore=vectorstore,
llm_chain=llm_chain,
search=search,
num_search_results=num_search_results,
text_splitter=text_splitter,
trust_env=trust_env,
allow_dangerous_requests=allow_dangerous_requests,
)
def clean_search_query(self, query: str) -> str:
# Some search tools (e.g., Google) will
# fail to return results if query has a
# leading digit: 1. "LangCh..."
# Check if the first character is a digit
if query[0].isdigit():
# Find the position of the first quote
first_quote_pos = query.find('"')
if first_quote_pos != -1:
# Extract the part of the string after the quote
query = query[first_quote_pos + 1 :]
# Remove the trailing quote if present
if query.endswith('"'):
query = query[:-1]
return query.strip()
def search_tool(self, query: str, num_search_results: int = 1) -> List[dict]:
"""Returns num_search_results pages per Google search."""
query_clean = self.clean_search_query(query)
result = self.search.results(query_clean, num_search_results)
return result
def _get_relevant_documents(
self,
query: str,
*,
run_manager: CallbackManagerForRetrieverRun,
) -> List[Document]:
"""Search Google for documents related to the query input.
Args:
query: user query
Returns:
Relevant documents from all various urls.
"""
# Get search questions
logger.info("Generating questions for Google Search ...")
result = self.llm_chain({"question": query})
logger.info(f"Questions for Google Search (raw): {result}")
questions = result["text"]
logger.info(f"Questions for Google Search: {questions}")
# Get urls
logger.info("Searching for relevant urls...")
urls_to_look = []
for query in questions:
# Google search
search_results = self.search_tool(query, self.num_search_results)
logger.info("Searching for relevant urls...")
logger.info(f"Search results: {search_results}")
for res in search_results:
if res.get("link", None):
urls_to_look.append(res["link"])
# Relevant urls
urls = set(urls_to_look)
# Check for any new urls that we have not processed
new_urls = list(urls.difference(self.url_database))
logger.info(f"New URLs to load: {new_urls}")
# Load, split, and add new urls to vectorstore
if new_urls:
loader = AsyncHtmlLoader(
new_urls, ignore_load_errors=True, trust_env=self.trust_env
)
html2text = Html2TextTransformer()
logger.info("Indexing new urls...")
docs = loader.load()
docs = list(html2text.transform_documents(docs))
docs = self.text_splitter.split_documents(docs)
self.vectorstore.add_documents(docs)
self.url_database.extend(new_urls)
# Search for relevant splits
# TODO: make this async
logger.info("Grabbing most relevant splits from urls...")
docs = []
for query in questions:
docs.extend(self.vectorstore.similarity_search(query))
# Get unique docs
unique_documents_dict = {
(doc.page_content, tuple(sorted(doc.metadata.items()))): doc for doc in docs
}
unique_documents = list(unique_documents_dict.values())
return unique_documents
async def _aget_relevant_documents(
self,
query: str,
*,
run_manager: AsyncCallbackManagerForRetrieverRun,
) -> List[Document]:
raise NotImplementedError
| |
158310
|
from typing import Any, Dict, List, cast
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from pydantic import Field
class LlamaIndexRetriever(BaseRetriever):
"""`LlamaIndex` retriever.
It is used for the question-answering with sources over
an LlamaIndex data structure."""
index: Any = None
"""LlamaIndex index to query."""
query_kwargs: Dict = Field(default_factory=dict)
"""Keyword arguments to pass to the query method."""
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
"""Get documents relevant for a query."""
try:
from llama_index.core.base.response.schema import Response
from llama_index.core.indices.base import BaseGPTIndex
except ImportError:
raise ImportError(
"You need to install `pip install llama-index` to use this retriever."
)
index = cast(BaseGPTIndex, self.index)
response = index.query(query, **self.query_kwargs)
response = cast(Response, response)
# parse source nodes
docs = []
for source_node in response.source_nodes:
metadata = source_node.metadata or {}
docs.append(
Document(page_content=source_node.get_content(), metadata=metadata)
)
return docs
class LlamaIndexGraphRetriever(BaseRetriever):
"""`LlamaIndex` graph data structure retriever.
It is used for question-answering with sources over an LlamaIndex
graph data structure."""
graph: Any = None
"""LlamaIndex graph to query."""
query_configs: List[Dict] = Field(default_factory=list)
"""List of query configs to pass to the query method."""
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
"""Get documents relevant for a query."""
try:
from llama_index.core.base.response.schema import Response
from llama_index.core.composability.base import (
QUERY_CONFIG_TYPE,
ComposableGraph,
)
except ImportError:
raise ImportError(
"You need to install `pip install llama-index` to use this retriever."
)
graph = cast(ComposableGraph, self.graph)
# for now, inject response_mode="no_text" into query configs
for query_config in self.query_configs:
query_config["response_mode"] = "no_text"
query_configs = cast(List[QUERY_CONFIG_TYPE], self.query_configs)
response = graph.query(query, query_configs=query_configs)
response = cast(Response, response)
# parse source nodes
docs = []
for source_node in response.source_nodes:
metadata = source_node.metadata or {}
docs.append(
Document(page_content=source_node.get_content(), metadata=metadata)
)
return docs
| |
158315
|
from typing import List, Optional
import aiohttp
import requests
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
class DataberryRetriever(BaseRetriever):
"""`Databerry API` retriever."""
datastore_url: str
top_k: Optional[int]
api_key: Optional[str]
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
response = requests.post(
self.datastore_url,
json={
"query": query,
**({"topK": self.top_k} if self.top_k is not None else {}),
},
headers={
"Content-Type": "application/json",
**(
{"Authorization": f"Bearer {self.api_key}"}
if self.api_key is not None
else {}
),
},
)
data = response.json()
return [
Document(
page_content=r["text"],
metadata={"source": r["source"], "score": r["score"]},
)
for r in data["results"]
]
async def _aget_relevant_documents(
self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
) -> List[Document]:
async with aiohttp.ClientSession() as session:
async with session.request(
"POST",
self.datastore_url,
json={
"query": query,
**({"topK": self.top_k} if self.top_k is not None else {}),
},
headers={
"Content-Type": "application/json",
**(
{"Authorization": f"Bearer {self.api_key}"}
if self.api_key is not None
else {}
),
},
) as response:
data = await response.json()
return [
Document(
page_content=r["text"],
metadata={"source": r["source"], "score": r["score"]},
)
for r in data["results"]
]
| |
158322
|
from typing import Any, List
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from langchain_community.utilities import YouSearchAPIWrapper
class YouRetriever(BaseRetriever, YouSearchAPIWrapper):
"""You.com Search API retriever.
It wraps results() to get_relevant_documents
It uses all YouSearchAPIWrapper arguments without any change.
"""
def _get_relevant_documents(
self,
query: str,
*,
run_manager: CallbackManagerForRetrieverRun,
**kwargs: Any,
) -> List[Document]:
return self.results(query, run_manager=run_manager.get_child(), **kwargs)
async def _aget_relevant_documents(
self,
query: str,
*,
run_manager: AsyncCallbackManagerForRetrieverRun,
**kwargs: Any,
) -> List[Document]:
results = await self.results_async(
query, run_manager=run_manager.get_child(), **kwargs
)
return results
| |
158324
|
import os
import re
from typing import Any, Dict, List, Literal, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
class AskNewsRetriever(BaseRetriever):
"""AskNews retriever."""
k: int = 10
offset: int = 0
start_timestamp: Optional[int] = None
end_timestamp: Optional[int] = None
method: Literal["nl", "kw"] = "nl"
categories: List[
Literal[
"All",
"Business",
"Crime",
"Politics",
"Science",
"Sports",
"Technology",
"Military",
"Health",
"Entertainment",
"Finance",
"Culture",
"Climate",
"Environment",
"World",
]
] = ["All"]
historical: bool = False
similarity_score_threshold: float = 0.5
kwargs: Optional[Dict[str, Any]] = {}
client_id: Optional[str] = None
client_secret: Optional[str] = None
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
"""Get documents relevant to a query.
Args:
query: String to find relevant documents for
run_manager: The callbacks handler to use
Returns:
List of relevant documents
"""
try:
from asknews_sdk import AskNewsSDK
except ImportError:
raise ImportError(
"AskNews python package not found. "
"Please install it with `pip install asknews`."
)
an_client = AskNewsSDK(
client_id=self.client_id or os.environ["ASKNEWS_CLIENT_ID"],
client_secret=self.client_secret or os.environ["ASKNEWS_CLIENT_SECRET"],
scopes=["news"],
)
response = an_client.news.search_news(
query=query,
n_articles=self.k,
start_timestamp=self.start_timestamp,
end_timestamp=self.end_timestamp,
method=self.method,
categories=self.categories,
historical=self.historical,
similarity_score_threshold=self.similarity_score_threshold,
offset=self.offset,
doc_start_delimiter="<doc>",
doc_end_delimiter="</doc>",
return_type="both",
**self.kwargs,
)
return self._extract_documents(response)
async def _aget_relevant_documents(
self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
) -> List[Document]:
"""Asynchronously get documents relevant to a query.
Args:
query: String to find relevant documents for
run_manager: The callbacks handler to use
Returns:
List of relevant documents
"""
try:
from asknews_sdk import AsyncAskNewsSDK
except ImportError:
raise ImportError(
"AskNews python package not found. "
"Please install it with `pip install asknews`."
)
an_client = AsyncAskNewsSDK(
client_id=self.client_id or os.environ["ASKNEWS_CLIENT_ID"],
client_secret=self.client_secret or os.environ["ASKNEWS_CLIENT_SECRET"],
scopes=["news"],
)
response = await an_client.news.search_news(
query=query,
n_articles=self.k,
start_timestamp=self.start_timestamp,
end_timestamp=self.end_timestamp,
method=self.method,
categories=self.categories,
historical=self.historical,
similarity_score_threshold=self.similarity_score_threshold,
offset=self.offset,
return_type="both",
doc_start_delimiter="<doc>",
doc_end_delimiter="</doc>",
**self.kwargs,
)
return self._extract_documents(response)
def _extract_documents(self, response: Any) -> List[Document]:
"""Extract documents from an api response."""
from asknews_sdk.dto.news import SearchResponse
sr: SearchResponse = response
matches = re.findall(r"<doc>(.*?)</doc>", sr.as_string, re.DOTALL)
docs = [
Document(
page_content=matches[i].strip(),
metadata={
"title": sr.as_dicts[i].title,
"source": str(sr.as_dicts[i].article_url)
if sr.as_dicts[i].article_url
else None,
"images": sr.as_dicts[i].image_url,
},
)
for i in range(len(matches))
]
return docs
| |
158326
|
from __future__ import annotations
import json
from typing import Any, Dict, List, Optional
import aiohttp
import requests
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from langchain_core.utils import get_from_dict_or_env, get_from_env
from pydantic import ConfigDict, model_validator
DEFAULT_URL_SUFFIX = "search.windows.net"
"""Default URL Suffix for endpoint connection - commercial cloud"""
class AzureAISearchRetriever(BaseRetriever):
"""`Azure AI Search` service retriever.
Setup:
See here for more detail: https://python.langchain.com/docs/integrations/retrievers/azure_ai_search/
We will need to install the below dependencies and set the required
environment variables:
.. code-block:: bash
pip install -U langchain-community azure-identity azure-search-documents
export AZURE_AI_SEARCH_SERVICE_NAME="<YOUR_SEARCH_SERVICE_NAME>"
export AZURE_AI_SEARCH_INDEX_NAME="<YOUR_SEARCH_INDEX_NAME>"
export AZURE_AI_SEARCH_API_KEY="<YOUR_API_KEY>"
Key init args:
content_key: str
top_k: int
index_name: str
Instantiate:
.. code-block:: python
from langchain_community.retrievers import AzureAISearchRetriever
retriever = AzureAISearchRetriever(
content_key="content", top_k=1, index_name="langchain-vector-demo"
)
Usage:
.. code-block:: python
retriever.invoke("here is my unstructured query string")
Use within a chain:
.. code-block:: python
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import AzureChatOpenAI
prompt = ChatPromptTemplate.from_template(
\"\"\"Answer the question based only on the context provided.
Context: {context}
Question: {question}\"\"\"
)
llm = AzureChatOpenAI(azure_deployment="gpt-35-turbo")
def format_docs(docs):
return "\\n\\n".join(doc.page_content for doc in docs)
chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
chain.invoke("...")
""" # noqa: E501
service_name: str = ""
"""Name of Azure AI Search service"""
index_name: str = ""
"""Name of Index inside Azure AI Search service"""
api_key: str = ""
"""API Key. Both Admin and Query keys work, but for reading data it's
recommended to use a Query key."""
api_version: str = "2023-11-01"
"""API version"""
aiosession: Optional[aiohttp.ClientSession] = None
"""ClientSession, in case we want to reuse connection for better performance."""
content_key: str = "content"
"""Key in a retrieved result to set as the Document page_content."""
top_k: Optional[int] = None
"""Number of results to retrieve. Set to None to retrieve all results."""
filter: Optional[str] = None
"""OData $filter expression to apply to the search query."""
model_config = ConfigDict(
arbitrary_types_allowed=True,
extra="forbid",
)
@model_validator(mode="before")
@classmethod
def validate_environment(cls, values: Dict) -> Any:
"""Validate that service name, index name and api key exists in environment."""
values["service_name"] = get_from_dict_or_env(
values, "service_name", "AZURE_AI_SEARCH_SERVICE_NAME"
)
values["index_name"] = get_from_dict_or_env(
values, "index_name", "AZURE_AI_SEARCH_INDEX_NAME"
)
values["api_key"] = get_from_dict_or_env(
values, "api_key", "AZURE_AI_SEARCH_API_KEY"
)
return values
def _build_search_url(self, query: str) -> str:
url_suffix = get_from_env("", "AZURE_AI_SEARCH_URL_SUFFIX", DEFAULT_URL_SUFFIX)
if url_suffix in self.service_name and "https://" in self.service_name:
base_url = f"{self.service_name}/"
elif url_suffix in self.service_name and "https://" not in self.service_name:
base_url = f"https://{self.service_name}/"
elif url_suffix not in self.service_name and "https://" in self.service_name:
base_url = f"{self.service_name}.{url_suffix}/"
elif (
url_suffix not in self.service_name and "https://" not in self.service_name
):
base_url = f"https://{self.service_name}.{url_suffix}/"
else:
# pass to Azure to throw a specific error
base_url = self.service_name
endpoint_path = f"indexes/{self.index_name}/docs?api-version={self.api_version}"
top_param = f"&$top={self.top_k}" if self.top_k else ""
filter_param = f"&$filter={self.filter}" if self.filter else ""
return base_url + endpoint_path + f"&search={query}" + top_param + filter_param
@property
def _headers(self) -> Dict[str, str]:
return {
"Content-Type": "application/json",
"api-key": self.api_key,
}
def _search(self, query: str) -> List[dict]:
search_url = self._build_search_url(query)
response = requests.get(search_url, headers=self._headers)
if response.status_code != 200:
raise Exception(f"Error in search request: {response}")
return json.loads(response.text)["value"]
async def _asearch(self, query: str) -> List[dict]:
search_url = self._build_search_url(query)
if not self.aiosession:
async with aiohttp.ClientSession() as session:
async with session.get(search_url, headers=self._headers) as response:
response_json = await response.json()
else:
async with self.aiosession.get(
search_url, headers=self._headers
) as response:
response_json = await response.json()
return response_json["value"]
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
search_results = self._search(query)
return [
Document(page_content=result.pop(self.content_key), metadata=result)
for result in search_results
]
async def _aget_relevant_documents(
self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
) -> List[Document]:
search_results = await self._asearch(query)
return [
Document(page_content=result.pop(self.content_key), metadata=result)
for result in search_results
]
# For backwards compatibility
class AzureCognitiveSearchRetriever(AzureAISearchRetriever):
"""`Azure Cognitive Search` service retriever.
This version of the retriever will soon be
depreciated. Please switch to AzureAISearchRetriever
"""
| |
158328
|
# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
class RetrieveResult(BaseModel, extra="allow"): # type: ignore[call-arg]
"""`Amazon Kendra Retrieve API` search result.
It is composed of:
* relevant passages or text excerpts given an input query.
"""
QueryId: str
"""The ID of the query."""
ResultItems: List[RetrieveResultItem]
"""The result items."""
KENDRA_CONFIDENCE_MAPPING = {
"NOT_AVAILABLE": 0.0,
"LOW": 0.25,
"MEDIUM": 0.50,
"HIGH": 0.75,
"VERY_HIGH": 1.0,
}
class AmazonKendraRetriever(BaseRetriever):
"""`Amazon Kendra Index` retriever.
Args:
index_id: Kendra index id
region_name: The aws region e.g., `us-west-2`.
Fallsback to AWS_DEFAULT_REGION env variable
or region specified in ~/.aws/config.
credentials_profile_name: The name of the profile in the ~/.aws/credentials
or ~/.aws/config files, which has either access keys or role information
specified. If not specified, the default credential profile or, if on an
EC2 instance, credentials from IMDS will be used.
top_k: No of results to return
attribute_filter: Additional filtering of results based on metadata
See: https://docs.aws.amazon.com/kendra/latest/APIReference
document_relevance_override_configurations: Overrides relevance tuning
configurations of fields/attributes set at the index level
See: https://docs.aws.amazon.com/kendra/latest/APIReference
page_content_formatter: generates the Document page_content
allowing access to all result item attributes. By default, it uses
the item's title and excerpt.
client: boto3 client for Kendra
user_context: Provides information about the user context
See: https://docs.aws.amazon.com/kendra/latest/APIReference
Example:
.. code-block:: python
retriever = AmazonKendraRetriever(
index_id="c0806df7-e76b-4bce-9b5c-d5582f6b1a03"
)
"""
index_id: str
region_name: Optional[str] = None
credentials_profile_name: Optional[str] = None
top_k: int = 3
attribute_filter: Optional[Dict] = None
document_relevance_override_configurations: Optional[List[Dict]] = None
page_content_formatter: Callable[[ResultItem], str] = combined_text
client: Any
user_context: Optional[Dict] = None
min_score_confidence: Annotated[Optional[float], Field(ge=0.0, le=1.0)]
@validator("top_k")
def validate_top_k(cls, value: int) -> int:
if value < 0:
raise ValueError(f"top_k ({value}) cannot be negative.")
return value
@model_validator(mode="before")
@classmethod
def create_client(cls, values: Dict[str, Any]) -> Any:
top_k = values.get("top_k")
if top_k is not None and top_k < 0:
raise ValueError(f"top_k ({top_k}) cannot be negative.")
if values.get("client") is not None:
return values
try:
import boto3
if values.get("credentials_profile_name"):
session = boto3.Session(profile_name=values["credentials_profile_name"])
else:
# use default credentials
session = boto3.Session()
client_params = {}
if values.get("region_name"):
client_params["region_name"] = values["region_name"]
values["client"] = session.client("kendra", **client_params)
return values
except ImportError:
raise ImportError(
"Could not import boto3 python package. "
"Please install it with `pip install boto3`."
)
except Exception as e:
raise ValueError(
"Could not load credentials to authenticate with AWS client. "
"Please check that credentials in the specified "
"profile name are valid."
) from e
def _kendra_query(self, query: str) -> Sequence[ResultItem]:
kendra_kwargs = {
"IndexId": self.index_id,
# truncate the query to ensure that
# there is no validation exception from Kendra.
"QueryText": query.strip()[0:999],
"PageSize": self.top_k,
}
if self.attribute_filter is not None:
kendra_kwargs["AttributeFilter"] = self.attribute_filter
if self.document_relevance_override_configurations is not None:
kendra_kwargs["DocumentRelevanceOverrideConfigurations"] = (
self.document_relevance_override_configurations
)
if self.user_context is not None:
kendra_kwargs["UserContext"] = self.user_context
response = self.client.retrieve(**kendra_kwargs)
r_result = RetrieveResult.parse_obj(response)
if r_result.ResultItems:
return r_result.ResultItems
# Retrieve API returned 0 results, fall back to Query API
response = self.client.query(**kendra_kwargs)
q_result = QueryResult.parse_obj(response)
return q_result.ResultItems
def _get_top_k_docs(self, result_items: Sequence[ResultItem]) -> List[Document]:
top_docs = [
item.to_doc(self.page_content_formatter)
for item in result_items[: self.top_k]
]
return top_docs
def _filter_by_score_confidence(self, docs: List[Document]) -> List[Document]:
"""
Filter out the records that have a score confidence
greater than the required threshold.
"""
if not self.min_score_confidence:
return docs
filtered_docs = [
item
for item in docs
if (
item.metadata.get("score") is not None
and isinstance(item.metadata["score"], str)
and KENDRA_CONFIDENCE_MAPPING.get(item.metadata["score"], 0.0)
>= self.min_score_confidence
)
]
return filtered_docs
def _get_relevant_documents(
self,
query: str,
*,
run_manager: CallbackManagerForRetrieverRun,
) -> List[Document]:
"""Run search on Kendra index and get top k documents
Example:
.. code-block:: python
docs = retriever.invoke('This is my query')
"""
result_items = self._kendra_query(query)
top_k_docs = self._get_top_k_docs(result_items)
return self._filter_by_score_confidence(top_k_docs)
| |
158422
|
# flake8: noqa
QUERY_CHECKER = """
{query}
Double check the {dialect} query above for common mistakes, including:
- Using NOT IN with NULL values
- Using UNION when UNION ALL should have been used
- Using BETWEEN for exclusive ranges
- Data type mismatch in predicates
- Properly quoting identifiers
- Using the correct number of arguments for functions
- Casting to the correct data type
- Using the proper columns for joins
If there are any of the above mistakes, rewrite the query. If there are no mistakes, just reproduce the original query.
Output the final SQL query only.
SQL Query: """
| |
158610
|
"""HuggingFace sentence_transformer embedding models."""
from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
SentenceTransformerEmbeddings = HuggingFaceEmbeddings
| |
158647
|
@deprecated(
since="0.0.9",
removal="1.0",
alternative_import="langchain_openai.OpenAIEmbeddings",
)
class OpenAIEmbeddings(BaseModel, Embeddings):
"""OpenAI embedding models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key or pass it
as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain_community.embeddings import OpenAIEmbeddings
openai = OpenAIEmbeddings(openai_api_key="my-api-key")
In order to use the library with Microsoft Azure endpoints, you need to set
the OPENAI_API_TYPE, OPENAI_API_BASE, OPENAI_API_KEY and OPENAI_API_VERSION.
The OPENAI_API_TYPE must be set to 'azure' and the others correspond to
the properties of your endpoint.
In addition, the deployment name must be passed as the model parameter.
Example:
.. code-block:: python
import os
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"
os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
os.environ["OPENAI_API_VERSION"] = "2023-05-15"
os.environ["OPENAI_PROXY"] = "http://your-corporate-proxy:8080"
from langchain_community.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(
deployment="your-embeddings-deployment-name",
model="your-embeddings-model-name",
openai_api_base="https://your-endpoint.openai.azure.com/",
openai_api_type="azure",
)
text = "This is a test query."
query_result = embeddings.embed_query(text)
"""
client: Any = Field(default=None, exclude=True) #: :meta private:
async_client: Any = Field(default=None, exclude=True) #: :meta private:
model: str = "text-embedding-ada-002"
# to support Azure OpenAI Service custom deployment names
deployment: Optional[str] = model
# TODO: Move to AzureOpenAIEmbeddings.
openai_api_version: Optional[str] = Field(default=None, alias="api_version")
"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
# to support Azure OpenAI Service custom endpoints
openai_api_base: Optional[str] = Field(default=None, alias="base_url")
"""Base URL path for API requests, leave blank if not using a proxy or service
emulator."""
# to support Azure OpenAI Service custom endpoints
openai_api_type: Optional[str] = None
# to support explicit proxy for OpenAI
openai_proxy: Optional[str] = None
embedding_ctx_length: int = 8191
"""The maximum number of tokens to embed at once."""
openai_api_key: Optional[str] = Field(default=None, alias="api_key")
"""Automatically inferred from env var `OPENAI_API_KEY` if not provided."""
openai_organization: Optional[str] = Field(default=None, alias="organization")
"""Automatically inferred from env var `OPENAI_ORG_ID` if not provided."""
allowed_special: Union[Literal["all"], Set[str]] = set()
disallowed_special: Union[Literal["all"], Set[str], Sequence[str]] = "all"
chunk_size: int = 1000
"""Maximum number of texts to embed in each batch"""
max_retries: int = 2
"""Maximum number of retries to make when generating."""
request_timeout: Optional[Union[float, Tuple[float, float], Any]] = Field(
default=None, alias="timeout"
)
"""Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or
None."""
headers: Any = None
tiktoken_enabled: bool = True
"""Set this to False for non-OpenAI implementations of the embeddings API, e.g.
the `--extensions openai` extension for `text-generation-webui`"""
tiktoken_model_name: Optional[str] = None
"""The model name to pass to tiktoken when using this class.
Tiktoken is used to count the number of tokens in documents to constrain
them to be under a certain limit. By default, when set to None, this will
be the same as the embedding model name. However, there are some cases
where you may want to use this Embedding class with a model name not
supported by tiktoken. This can include when using Azure embeddings or
when using one of the many model providers that expose an OpenAI-like
API but with different models. In those cases, in order to avoid erroring
when tiktoken is called, you can specify a model name to use here."""
show_progress_bar: bool = False
"""Whether to show a progress bar when embedding."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
skip_empty: bool = False
"""Whether to skip empty strings when embedding or raise an error.
Defaults to not skipping."""
default_headers: Union[Mapping[str, str], None] = None
default_query: Union[Mapping[str, object], None] = None
# Configure a custom httpx client. See the
# [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
retry_min_seconds: int = 4
"""Min number of seconds to wait between retries"""
retry_max_seconds: int = 20
"""Max number of seconds to wait between retries"""
http_client: Union[Any, None] = None
"""Optional httpx.Client."""
model_config = ConfigDict(
populate_by_name=True, extra="forbid", protected_namespaces=()
)
@model_validator(mode="before")
@classmethod
def build_extra(cls, values: Dict[str, Any]) -> Any:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
if field_name not in all_required_field_names:
warnings.warn(
f"""WARNING! {field_name} is not default parameter.
{field_name} was transferred to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
if invalid_model_kwargs:
raise ValueError(
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
f"Instead they were passed in as part of `model_kwargs` parameter."
)
values["model_kwargs"] = extra
return values
| |
158648
|
@pre_init
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
values["openai_api_key"] = get_from_dict_or_env(
values, "openai_api_key", "OPENAI_API_KEY"
)
values["openai_api_base"] = values["openai_api_base"] or os.getenv(
"OPENAI_API_BASE"
)
values["openai_api_type"] = get_from_dict_or_env(
values,
"openai_api_type",
"OPENAI_API_TYPE",
default="",
)
values["openai_proxy"] = get_from_dict_or_env(
values,
"openai_proxy",
"OPENAI_PROXY",
default="",
)
if values["openai_api_type"] in ("azure", "azure_ad", "azuread"):
default_api_version = "2023-05-15"
# Azure OpenAI embedding models allow a maximum of 2048
# texts at a time in each batch
# See: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#embeddings
values["chunk_size"] = min(values["chunk_size"], 2048)
else:
default_api_version = ""
values["openai_api_version"] = get_from_dict_or_env(
values,
"openai_api_version",
"OPENAI_API_VERSION",
default=default_api_version,
)
# Check OPENAI_ORGANIZATION for backwards compatibility.
values["openai_organization"] = (
values["openai_organization"]
or os.getenv("OPENAI_ORG_ID")
or os.getenv("OPENAI_ORGANIZATION")
)
try:
import openai
except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
else:
if is_openai_v1():
if values["openai_api_type"] in ("azure", "azure_ad", "azuread"):
warnings.warn(
"If you have openai>=1.0.0 installed and are using Azure, "
"please use the `AzureOpenAIEmbeddings` class."
)
client_params = {
"api_key": values["openai_api_key"],
"organization": values["openai_organization"],
"base_url": values["openai_api_base"],
"timeout": values["request_timeout"],
"max_retries": values["max_retries"],
"default_headers": values["default_headers"],
"default_query": values["default_query"],
"http_client": values["http_client"],
}
if not values.get("client"):
values["client"] = openai.OpenAI(**client_params).embeddings
if not values.get("async_client"):
values["async_client"] = openai.AsyncOpenAI(
**client_params
).embeddings
elif not values.get("client"):
values["client"] = openai.Embedding
else:
pass
return values
@property
def _invocation_params(self) -> Dict[str, Any]:
if is_openai_v1():
openai_args: Dict = {"model": self.model, **self.model_kwargs}
else:
openai_args = {
"model": self.model,
"request_timeout": self.request_timeout,
"headers": self.headers,
"api_key": self.openai_api_key,
"organization": self.openai_organization,
"api_base": self.openai_api_base,
"api_type": self.openai_api_type,
"api_version": self.openai_api_version,
**self.model_kwargs,
}
if self.openai_api_type in ("azure", "azure_ad", "azuread"):
openai_args["engine"] = self.deployment
# TODO: Look into proxy with openai v1.
if self.openai_proxy:
try:
import openai
except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
openai.proxy = {
"http": self.openai_proxy,
"https": self.openai_proxy,
} # type: ignore[assignment]
return openai_args
# please refer to
# https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb
| |
158651
|
"""Azure OpenAI embeddings wrapper."""
from __future__ import annotations
import os
import warnings
from typing import Any, Callable, Dict, Optional, Union
from langchain_core._api.deprecation import deprecated
from langchain_core.utils import get_from_dict_or_env
from pydantic import Field, model_validator
from typing_extensions import Self
from langchain_community.embeddings.openai import OpenAIEmbeddings
from langchain_community.utils.openai import is_openai_v1
@deprecated(
since="0.0.9",
removal="1.0",
alternative_import="langchain_openai.AzureOpenAIEmbeddings",
)
class AzureOpenAIEmbeddings(OpenAIEmbeddings):
"""`Azure OpenAI` Embeddings API."""
azure_endpoint: Union[str, None] = None
"""Your Azure endpoint, including the resource.
Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided.
Example: `https://example-resource.azure.openai.com/`
"""
deployment: Optional[str] = Field(default=None, alias="azure_deployment")
"""A model deployment.
If given sets the base client URL to include `/deployments/{azure_deployment}`.
Note: this means you won't be able to use non-deployment endpoints.
"""
openai_api_key: Union[str, None] = Field(default=None, alias="api_key")
"""Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided."""
azure_ad_token: Union[str, None] = None
"""Your Azure Active Directory token.
Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided.
For more:
https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
"""
azure_ad_token_provider: Union[Callable[[], str], None] = None
"""A function that returns an Azure Active Directory token.
Will be invoked on every request.
"""
openai_api_version: Optional[str] = Field(default=None, alias="api_version")
"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
validate_base_url: bool = True
@model_validator(mode="before")
@classmethod
def validate_environment(cls, values: Dict) -> Any:
"""Validate that api key and python package exists in environment."""
# Check OPENAI_KEY for backwards compatibility.
# TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using
# other forms of azure credentials.
values["openai_api_key"] = (
values.get("openai_api_key")
or os.getenv("AZURE_OPENAI_API_KEY")
or os.getenv("OPENAI_API_KEY")
)
values["openai_api_base"] = values.get("openai_api_base") or os.getenv(
"OPENAI_API_BASE"
)
values["openai_api_version"] = values.get("openai_api_version") or os.getenv(
"OPENAI_API_VERSION", default="2023-05-15"
)
values["openai_api_type"] = get_from_dict_or_env(
values, "openai_api_type", "OPENAI_API_TYPE", default="azure"
)
values["openai_organization"] = (
values.get("openai_organization")
or os.getenv("OPENAI_ORG_ID")
or os.getenv("OPENAI_ORGANIZATION")
)
values["openai_proxy"] = get_from_dict_or_env(
values,
"openai_proxy",
"OPENAI_PROXY",
default="",
)
values["azure_endpoint"] = values.get("azure_endpoint") or os.getenv(
"AZURE_OPENAI_ENDPOINT"
)
values["azure_ad_token"] = values.get("azure_ad_token") or os.getenv(
"AZURE_OPENAI_AD_TOKEN"
)
# Azure OpenAI embedding models allow a maximum of 2048 texts
# at a time in each batch
# See: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#embeddings
values["chunk_size"] = min(values["chunk_size"], 2048)
try:
import openai # noqa: F401
except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
if is_openai_v1():
# For backwards compatibility. Before openai v1, no distinction was made
# between azure_endpoint and base_url (openai_api_base).
openai_api_base = values["openai_api_base"]
if openai_api_base and values["validate_base_url"]:
if "/openai" not in openai_api_base:
values["openai_api_base"] += "/openai"
warnings.warn(
"As of openai>=1.0.0, Azure endpoints should be specified via "
f"the `azure_endpoint` param not `openai_api_base` "
f"(or alias `base_url`). Updating `openai_api_base` from "
f"{openai_api_base} to {values['openai_api_base']}."
)
if values["deployment"]:
warnings.warn(
"As of openai>=1.0.0, if `deployment` (or alias "
"`azure_deployment`) is specified then "
"`openai_api_base` (or alias `base_url`) should not be. "
"Instead use `deployment` (or alias `azure_deployment`) "
"and `azure_endpoint`."
)
if values["deployment"] not in values["openai_api_base"]:
warnings.warn(
"As of openai>=1.0.0, if `openai_api_base` "
"(or alias `base_url`) is specified it is expected to be "
"of the form "
"https://example-resource.azure.openai.com/openai/deployments/example-deployment. " # noqa: E501
f"Updating {openai_api_base} to "
f"{values['openai_api_base']}."
)
values["openai_api_base"] += (
"/deployments/" + values["deployment"]
)
values["deployment"] = None
return values
@model_validator(mode="after")
def post_init_validator(self) -> Self:
"""Validate that the base url is set."""
import openai
if is_openai_v1():
client_params = {
"api_version": self.openai_api_version,
"azure_endpoint": self.azure_endpoint,
"azure_deployment": self.deployment,
"api_key": self.openai_api_key,
"azure_ad_token": self.azure_ad_token,
"azure_ad_token_provider": self.azure_ad_token_provider,
"organization": self.openai_organization,
"base_url": self.openai_api_base,
"timeout": self.request_timeout,
"max_retries": self.max_retries,
"default_headers": self.default_headers,
"default_query": self.default_query,
"http_client": self.http_client,
}
self.client = openai.AzureOpenAI(**client_params).embeddings
self.async_client = openai.AsyncAzureOpenAI(**client_params).embeddings
else:
self.client = openai.Embedding
return self
@property
def _llm_type(self) -> str:
return "azure-openai-chat"
| |
158655
|
# This file is adapted from
# https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/embeddings/huggingface.py
from typing import Any, Dict, List, Optional
from langchain_core.embeddings import Embeddings
from pydantic import BaseModel, ConfigDict, Field
DEFAULT_BGE_MODEL = "BAAI/bge-small-en-v1.5"
DEFAULT_QUERY_BGE_INSTRUCTION_EN = (
"Represent this question for searching relevant passages: "
)
DEFAULT_QUERY_BGE_INSTRUCTION_ZH = "为这个句子生成表示以用于检索相关文章："
class IpexLLMBgeEmbeddings(BaseModel, Embeddings):
"""Wrapper around the BGE embedding model
with IPEX-LLM optimizations on Intel CPUs and GPUs.
To use, you should have the ``ipex-llm``
and ``sentence_transformers`` package installed. Refer to
`here <https://python.langchain.com/v0.1/docs/integrations/text_embedding/ipex_llm/>`_
for installation on Intel CPU.
Example on Intel CPU:
.. code-block:: python
from langchain_community.embeddings import IpexLLMBgeEmbeddings
embedding_model = IpexLLMBgeEmbeddings(
model_name="BAAI/bge-large-en-v1.5",
model_kwargs={},
encode_kwargs={"normalize_embeddings": True},
)
Refer to
`here <https://python.langchain.com/v0.1/docs/integrations/text_embedding/ipex_llm_gpu/>`_
for installation on Intel GPU.
Example on Intel GPU:
.. code-block:: python
from langchain_community.embeddings import IpexLLMBgeEmbeddings
embedding_model = IpexLLMBgeEmbeddings(
model_name="BAAI/bge-large-en-v1.5",
model_kwargs={"device": "xpu"},
encode_kwargs={"normalize_embeddings": True},
)
"""
client: Any = None #: :meta private:
model_name: str = DEFAULT_BGE_MODEL
"""Model name to use."""
cache_folder: Optional[str] = None
"""Path to store models.
Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Keyword arguments to pass to the model."""
encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Keyword arguments to pass when calling the `encode` method of the model."""
query_instruction: str = DEFAULT_QUERY_BGE_INSTRUCTION_EN
"""Instruction to use for embedding query."""
embed_instruction: str = ""
"""Instruction to use for embedding document."""
def __init__(self, **kwargs: Any):
"""Initialize the sentence_transformer."""
super().__init__(**kwargs)
try:
import sentence_transformers
from ipex_llm.transformers.convert import _optimize_post, _optimize_pre
except ImportError as exc:
base_url = (
"https://python.langchain.com/v0.1/docs/integrations/text_embedding/"
)
raise ImportError(
"Could not import ipex_llm or sentence_transformers. "
f"Please refer to {base_url}/ipex_llm/ "
"for install required packages on Intel CPU. "
f"And refer to {base_url}/ipex_llm_gpu/ "
"for install required packages on Intel GPU. "
) from exc
# Set "cpu" as default device
if "device" not in self.model_kwargs:
self.model_kwargs["device"] = "cpu"
if self.model_kwargs["device"] not in ["cpu", "xpu"]:
raise ValueError(
"IpexLLMBgeEmbeddings currently only supports device to be "
f"'cpu' or 'xpu', but you have: {self.model_kwargs['device']}."
)
self.client = sentence_transformers.SentenceTransformer(
self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
)
# Add ipex-llm optimizations
self.client = _optimize_pre(self.client)
self.client = _optimize_post(self.client)
if self.model_kwargs["device"] == "xpu":
self.client = self.client.half().to("xpu")
if "-zh" in self.model_name:
self.query_instruction = DEFAULT_QUERY_BGE_INSTRUCTION_ZH
model_config = ConfigDict(extra="forbid", protected_namespaces=())
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a HuggingFace transformer model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
texts = [self.embed_instruction + t.replace("\n", " ") for t in texts]
embeddings = self.client.encode(texts, **self.encode_kwargs)
return embeddings.tolist()
def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace transformer model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
text = text.replace("\n", " ")
embedding = self.client.encode(
self.query_instruction + text, **self.encode_kwargs
)
return embedding.tolist()
| |
158657
|
import warnings
from typing import Any, Dict, List, Optional
import requests
from langchain_core._api import deprecated, warn_deprecated
from langchain_core.embeddings import Embeddings
from pydantic import BaseModel, ConfigDict, Field, SecretStr
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large"
DEFAULT_BGE_MODEL = "BAAI/bge-large-en"
DEFAULT_EMBED_INSTRUCTION = "Represent the document for retrieval: "
DEFAULT_QUERY_INSTRUCTION = (
"Represent the question for retrieving supporting documents: "
)
DEFAULT_QUERY_BGE_INSTRUCTION_EN = (
"Represent this question for searching relevant passages: "
)
DEFAULT_QUERY_BGE_INSTRUCTION_ZH = "为这个句子生成表示以用于检索相关文章："
@deprecated(
since="0.2.2",
removal="1.0",
alternative_import="langchain_huggingface.HuggingFaceEmbeddings",
)
class HuggingFaceEmbeddings(BaseModel, Embeddings):
"""HuggingFace sentence_transformers embedding models.
To use, you should have the ``sentence_transformers`` python package installed.
Example:
.. code-block:: python
from langchain_community.embeddings import HuggingFaceEmbeddings
model_name = "sentence-transformers/all-mpnet-base-v2"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': False}
hf = HuggingFaceEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
"""
client: Any = None #: :meta private:
model_name: str = DEFAULT_MODEL_NAME
"""Model name to use."""
cache_folder: Optional[str] = None
"""Path to store models.
Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Keyword arguments to pass to the Sentence Transformer model, such as `device`,
`prompts`, `default_prompt_name`, `revision`, `trust_remote_code`, or `token`.
See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer"""
encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Keyword arguments to pass when calling the `encode` method of the Sentence
Transformer model, such as `prompt_name`, `prompt`, `batch_size`, `precision`,
`normalize_embeddings`, and more.
See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode"""
multi_process: bool = False
"""Run encode() on multiple GPUs."""
show_progress: bool = False
"""Whether to show a progress bar."""
def __init__(self, **kwargs: Any):
"""Initialize the sentence_transformer."""
super().__init__(**kwargs)
if "model_name" not in kwargs:
since = "0.2.16"
removal = "0.4.0"
warn_deprecated(
since=since,
removal=removal,
message=f"Default values for {self.__class__.__name__}.model_name"
+ f" were deprecated in LangChain {since} and will be removed in"
+ f" {removal}. Explicitly pass a model_name to the"
+ f" {self.__class__.__name__} constructor instead.",
)
try:
import sentence_transformers
except ImportError as exc:
raise ImportError(
"Could not import sentence_transformers python package. "
"Please install it with `pip install sentence-transformers`."
) from exc
self.client = sentence_transformers.SentenceTransformer(
self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
)
model_config = ConfigDict(extra="forbid", protected_namespaces=())
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a HuggingFace transformer model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
import sentence_transformers
texts = list(map(lambda x: x.replace("\n", " "), texts))
if self.multi_process:
pool = self.client.start_multi_process_pool()
embeddings = self.client.encode_multi_process(texts, pool)
sentence_transformers.SentenceTransformer.stop_multi_process_pool(pool)
else:
embeddings = self.client.encode(
texts, show_progress_bar=self.show_progress, **self.encode_kwargs
)
return embeddings.tolist()
def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace transformer model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
return self.embed_documents([text])[0]
class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
"""Wrapper around sentence_transformers embedding models.
To use, you should have the ``sentence_transformers``
and ``InstructorEmbedding`` python packages installed.
Example:
.. code-block:: python
from langchain_community.embeddings import HuggingFaceInstructEmbeddings
model_name = "hkunlp/instructor-large"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': True}
hf = HuggingFaceInstructEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
"""
client: Any = None #: :meta private:
model_name: str = DEFAULT_INSTRUCT_MODEL
"""Model name to use."""
cache_folder: Optional[str] = None
"""Path to store models.
Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Keyword arguments to pass to the model."""
encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Keyword arguments to pass when calling the `encode` method of the model."""
embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
"""Instruction to use for embedding documents."""
query_instruction: str = DEFAULT_QUERY_INSTRUCTION
"""Instruction to use for embedding query."""
show_progress: bool = False
"""Whether to show a progress bar."""
def __init__(self, **kwargs: Any):
"""Initialize the sentence_transformer."""
super().__init__(**kwargs)
if "model_name" not in kwargs:
since = "0.2.16"
removal = "0.4.0"
warn_deprecated(
since=since,
removal=removal,
message=f"Default values for {self.__class__.__name__}.model_name"
+ f" were deprecated in LangChain {since} and will be removed in"
+ f" {removal}. Explicitly pass a model_name to the"
+ f" {self.__class__.__name__} constructor instead.",
)
try:
from InstructorEmbedding import INSTRUCTOR
self.client = INSTRUCTOR(
self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
)
except ImportError as e:
raise ImportError("Dependencies for InstructorEmbedding not found.") from e
if "show_progress_bar" in self.encode_kwargs:
warn_deprecated(
since="0.2.5",
removal="1.0",
name="encode_kwargs['show_progress_bar']",
alternative=f"the show_progress method on {self.__class__.__name__}",
)
if self.show_progress:
warnings.warn(
"Both encode_kwargs['show_progress_bar'] and show_progress are set;"
"encode_kwargs['show_progress_bar'] takes precedence"
)
self.show_progress = self.encode_kwargs.pop("show_progress_bar")
model_config = ConfigDict(extra="forbid", protected_namespaces=())
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a HuggingFace instruct model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
instruction_pairs = [[self.embed_instruction, text] for text in texts]
embeddings = self.client.encode(
instruction_pairs,
show_progress_bar=self.show_progress,
**self.encode_kwargs,
)
return embeddings.tolist()
def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace instruct model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
instruction_pair = [self.query_instruction, text]
embedding = self.client.encode(
[instruction_pair],
show_progress_bar=self.show_progress,
**self.encode_kwargs,
)[0]
return embedding.tolist()
class HuggingFaceBgeEmbeddings(Base
| |
158658
|
el, Embeddings):
"""HuggingFace sentence_transformers embedding models.
To use, you should have the ``sentence_transformers`` python package installed.
To use Nomic, make sure the version of ``sentence_transformers`` >= 2.3.0.
Bge Example:
.. code-block:: python
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-large-en-v1.5"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': True}
hf = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
Nomic Example:
.. code-block:: python
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
model_name = "nomic-ai/nomic-embed-text-v1"
model_kwargs = {
'device': 'cpu',
'trust_remote_code':True
}
encode_kwargs = {'normalize_embeddings': True}
hf = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
query_instruction = "search_query:",
embed_instruction = "search_document:"
)
"""
client: Any = None #: :meta private:
model_name: str = DEFAULT_BGE_MODEL
"""Model name to use."""
cache_folder: Optional[str] = None
"""Path to store models.
Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Keyword arguments to pass to the model."""
encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Keyword arguments to pass when calling the `encode` method of the model."""
query_instruction: str = DEFAULT_QUERY_BGE_INSTRUCTION_EN
"""Instruction to use for embedding query."""
embed_instruction: str = ""
"""Instruction to use for embedding document."""
show_progress: bool = False
"""Whether to show a progress bar."""
def __init__(self, **kwargs: Any):
"""Initialize the sentence_transformer."""
super().__init__(**kwargs)
if "model_name" not in kwargs:
since = "0.2.5"
removal = "0.4.0"
warn_deprecated(
since=since,
removal=removal,
message=f"Default values for {self.__class__.__name__}.model_name"
+ f" were deprecated in LangChain {since} and will be removed in"
+ f" {removal}. Explicitly pass a model_name to the"
+ f" {self.__class__.__name__} constructor instead.",
)
try:
import sentence_transformers
except ImportError as exc:
raise ImportError(
"Could not import sentence_transformers python package. "
"Please install it with `pip install sentence_transformers`."
) from exc
self.client = sentence_transformers.SentenceTransformer(
self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
)
if "-zh" in self.model_name:
self.query_instruction = DEFAULT_QUERY_BGE_INSTRUCTION_ZH
if "show_progress_bar" in self.encode_kwargs:
warn_deprecated(
since="0.2.5",
removal="1.0",
name="encode_kwargs['show_progress_bar']",
alternative=f"the show_progress method on {self.__class__.__name__}",
)
if self.show_progress:
warnings.warn(
"Both encode_kwargs['show_progress_bar'] and show_progress are set;"
"encode_kwargs['show_progress_bar'] takes precedence"
)
self.show_progress = self.encode_kwargs.pop("show_progress_bar")
model_config = ConfigDict(extra="forbid", protected_namespaces=())
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a HuggingFace transformer model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
texts = [self.embed_instruction + t.replace("\n", " ") for t in texts]
embeddings = self.client.encode(
texts, show_progress_bar=self.show_progress, **self.encode_kwargs
)
return embeddings.tolist()
def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace transformer model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
text = text.replace("\n", " ")
embedding = self.client.encode(
self.query_instruction + text,
show_progress_bar=self.show_progress,
**self.encode_kwargs,
)
return embedding.tolist()
class HuggingFaceInferenceAPIEmbeddings(BaseModel, Embeddings):
"""Embed texts using the HuggingFace API.
Requires a HuggingFace Inference API key and a model name.
"""
api_key: SecretStr
"""Your API key for the HuggingFace Inference API."""
model_name: str = "sentence-transformers/all-MiniLM-L6-v2"
"""The name of the model to use for text embeddings."""
api_url: Optional[str] = None
"""Custom inference endpoint url. None for using default public url."""
additional_headers: Dict[str, str] = {}
"""Pass additional headers to the requests library if needed."""
model_config = ConfigDict(extra="forbid", protected_namespaces=())
@property
def _api_url(self) -> str:
return self.api_url or self._default_api_url
@property
def _default_api_url(self) -> str:
return (
"https://api-inference.huggingface.co"
"/pipeline"
"/feature-extraction"
f"/{self.model_name}"
)
@property
def _headers(self) -> dict:
return {
"Authorization": f"Bearer {self.api_key.get_secret_value()}",
**self.additional_headers,
}
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Get the embeddings for a list of texts.
Args:
texts (Documents): A list of texts to get embeddings for.
Returns:
Embedded texts as List[List[float]], where each inner List[float]
corresponds to a single input text.
Example:
.. code-block:: python
from langchain_community.embeddings import (
HuggingFaceInferenceAPIEmbeddings,
)
hf_embeddings = HuggingFaceInferenceAPIEmbeddings(
api_key="your_api_key",
model_name="sentence-transformers/all-MiniLM-l6-v2"
)
texts = ["Hello, world!", "How are you?"]
hf_embeddings.embed_documents(texts)
""" # noqa: E501
response = requests.post(
self._api_url,
headers=self._headers,
json={
"inputs": texts,
"options": {"wait_for_model": True, "use_cache": True},
},
)
return response.json()
def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace transformer model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
return self.embed_documents([text])[0]
| |
158661
|
"""Wrapper around text2vec embedding models."""
from typing import Any, List, Optional
from langchain_core.embeddings import Embeddings
from pydantic import BaseModel
class Text2vecEmbeddings(Embeddings, BaseModel):
"""text2vec embedding models.
Install text2vec first, run 'pip install -U text2vec'.
The github repository for text2vec is : https://github.com/shibing624/text2vec
Example:
.. code-block:: python
from langchain_community.embeddings.text2vec import Text2vecEmbeddings
embedding = Text2vecEmbeddings()
embedding.embed_documents([
"This is a CoSENT(Cosine Sentence) model.",
"It maps sentences to a 768 dimensional dense vector space.",
])
embedding.embed_query(
"It can be used for text matching or semantic search."
)
"""
model_name_or_path: Optional[str] = None
encoder_type: Any = "MEAN"
max_seq_length: int = 256
device: Optional[str] = None
model: Any = None
def __init__(
self,
*,
model: Any = None,
model_name_or_path: Optional[str] = None,
**kwargs: Any,
):
try:
from text2vec import SentenceModel
except ImportError as e:
raise ImportError(
"Unable to import text2vec, please install with "
"`pip install -U text2vec`."
) from e
model_kwargs = {}
if model_name_or_path is not None:
model_kwargs["model_name_or_path"] = model_name_or_path
model = model or SentenceModel(**model_kwargs, **kwargs)
super().__init__(model=model, model_name_or_path=model_name_or_path, **kwargs)
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed documents using the text2vec embeddings model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
return self.model.encode(texts)
def embed_query(self, text: str) -> List[float]:
"""Embed a query using the text2vec embeddings model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
return self.model.encode(text)
| |
158675
|
from __future__ import annotations
import logging
from typing import Any, Dict, List, Optional
from langchain_core.embeddings import Embeddings
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
from pydantic import BaseModel, Field, SecretStr
logger = logging.getLogger(__name__)
class QianfanEmbeddingsEndpoint(BaseModel, Embeddings):
"""Baidu Qianfan Embeddings embedding models.
Setup:
To use, you should have the ``qianfan`` python package installed, and set
environment variables ``QIANFAN_AK``, ``QIANFAN_SK``.
.. code-block:: bash
pip install qianfan
export QIANFAN_AK="your-api-key"
export QIANFAN_SK="your-secret_key"
Instantiate:
.. code-block:: python
from langchain_community.embeddings import QianfanEmbeddingsEndpoint
embeddings = QianfanEmbeddingsEndpoint()
Embed:
.. code-block:: python
# embed the documents
vectors = embeddings.embed_documents([text1, text2, ...])
# embed the query
vectors = embeddings.embed_query(text)
# embed the documents with async
vectors = await embeddings.aembed_documents([text1, text2, ...])
# embed the query with async
vectors = await embeddings.aembed_query(text)
""" # noqa: E501
qianfan_ak: Optional[SecretStr] = Field(default=None, alias="api_key")
"""Qianfan application apikey"""
qianfan_sk: Optional[SecretStr] = Field(default=None, alias="secret_key")
"""Qianfan application secretkey"""
chunk_size: int = 16
"""Chunk size when multiple texts are input"""
model: Optional[str] = Field(default=None)
"""Model name
you could get from https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu
for now, we support Embedding-V1 and
- Embedding-V1 （默认模型）
- bge-large-en
- bge-large-zh
preset models are mapping to an endpoint.
`model` will be ignored if `endpoint` is set
"""
endpoint: str = ""
"""Endpoint of the Qianfan Embedding, required if custom model used."""
client: Any = None
"""Qianfan client"""
init_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""init kwargs for qianfan client init, such as `query_per_second` which is
associated with qianfan resource object to limit QPS"""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""extra params for model invoke using with `do`."""
@pre_init
def validate_environment(cls, values: Dict) -> Dict:
"""
Validate whether qianfan_ak and qianfan_sk in the environment variables or
configuration file are available or not.
init qianfan embedding client with `ak`, `sk`, `model`, `endpoint`
Args:
values: a dictionary containing configuration information, must include the
fields of qianfan_ak and qianfan_sk
Returns:
a dictionary containing configuration information. If qianfan_ak and
qianfan_sk are not provided in the environment variables or configuration
file,the original values will be returned; otherwise, values containing
qianfan_ak and qianfan_sk will be returned.
Raises:
ValueError: qianfan package not found, please install it with `pip install
qianfan`
"""
values["qianfan_ak"] = convert_to_secret_str(
get_from_dict_or_env(
values,
"qianfan_ak",
"QIANFAN_AK",
default="",
)
)
values["qianfan_sk"] = convert_to_secret_str(
get_from_dict_or_env(
values,
"qianfan_sk",
"QIANFAN_SK",
default="",
)
)
try:
import qianfan
params = {
**values.get("init_kwargs", {}),
"model": values["model"],
}
if values["qianfan_ak"].get_secret_value() != "":
params["ak"] = values["qianfan_ak"].get_secret_value()
if values["qianfan_sk"].get_secret_value() != "":
params["sk"] = values["qianfan_sk"].get_secret_value()
if values["endpoint"] is not None and values["endpoint"] != "":
params["endpoint"] = values["endpoint"]
values["client"] = qianfan.Embedding(**params)
except ImportError:
raise ImportError(
"qianfan package not found, please install it with "
"`pip install qianfan`"
)
return values
def embed_query(self, text: str) -> List[float]:
resp = self.embed_documents([text])
return resp[0]
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""
Embeds a list of text documents using the AutoVOT algorithm.
Args:
texts (List[str]): A list of text documents to embed.
Returns:
List[List[float]]: A list of embeddings for each document in the input list.
Each embedding is represented as a list of float values.
"""
text_in_chunks = [
texts[i : i + self.chunk_size]
for i in range(0, len(texts), self.chunk_size)
]
lst = []
for chunk in text_in_chunks:
resp = self.client.do(texts=chunk, **self.model_kwargs)
lst.extend([res["embedding"] for res in resp["data"]])
return lst
async def aembed_query(self, text: str) -> List[float]:
embeddings = await self.aembed_documents([text])
return embeddings[0]
async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
text_in_chunks = [
texts[i : i + self.chunk_size]
for i in range(0, len(texts), self.chunk_size)
]
lst = []
for chunk in text_in_chunks:
resp = await self.client.ado(texts=chunk, **self.model_kwargs)
for res in resp["data"]:
lst.extend([res["embedding"]])
return lst
| |
158711
|
"""Callback Handler for LLMonitor`.
#### Parameters:
- `app_id`: The app id of the app you want to report to. Defaults to
`None`, which means that `LLMONITOR_APP_ID` will be used.
- `api_url`: The url of the LLMonitor API. Defaults to `None`,
which means that either `LLMONITOR_API_URL` environment variable
or `https://app.llmonitor.com` will be used.
#### Raises:
- `ValueError`: if `app_id` is not provided either as an
argument or as an environment variable.
- `ConnectionError`: if the connection to the API fails.
#### Example:
```python
from langchain_community.llms import OpenAI
from langchain_community.callbacks import LLMonitorCallbackHandler
llmonitor_callback = LLMonitorCallbackHandler()
llm = OpenAI(callbacks=[llmonitor_callback],
metadata={"userId": "user-123"})
llm.invoke("Hello, how are you?")
```
"""
__api_url: str
__app_id: str
__verbose: bool
__llmonitor_version: str
__has_valid_config: bool
def __init__(
self,
app_id: Union[str, None] = None,
api_url: Union[str, None] = None,
verbose: bool = False,
) -> None:
super().__init__()
self.__has_valid_config = True
try:
import llmonitor
self.__llmonitor_version = importlib.metadata.version("llmonitor")
self.__track_event = llmonitor.track_event
except ImportError:
logger.warning(
"""[LLMonitor] To use the LLMonitor callback handler you need to
have the `llmonitor` Python package installed. Please install it
with `pip install llmonitor`"""
)
self.__has_valid_config = False
return
if parse(self.__llmonitor_version) < parse("0.0.32"):
logger.warning(
f"""[LLMonitor] The installed `llmonitor` version is
{self.__llmonitor_version}
but `LLMonitorCallbackHandler` requires at least version 0.0.32
upgrade `llmonitor` with `pip install --upgrade llmonitor`"""
)
self.__has_valid_config = False
self.__has_valid_config = True
self.__api_url = api_url or os.getenv("LLMONITOR_API_URL") or DEFAULT_API_URL
self.__verbose = verbose or bool(os.getenv("LLMONITOR_VERBOSE"))
_app_id = app_id or os.getenv("LLMONITOR_APP_ID")
if _app_id is None:
logger.warning(
"""[LLMonitor] app_id must be provided either as an argument or
as an environment variable"""
)
self.__has_valid_config = False
else:
self.__app_id = _app_id
if self.__has_valid_config is False:
return None
try:
res = requests.get(f"{self.__api_url}/api/app/{self.__app_id}")
if not res.ok:
raise ConnectionError()
except Exception:
logger.warning(
f"""[LLMonitor] Could not connect to the LLMonitor API at
{self.__api_url}"""
)
def on_llm_start(
self,
serialized: Dict[str, Any],
prompts: List[str],
*,
run_id: UUID,
parent_run_id: Union[UUID, None] = None,
tags: Union[List[str], None] = None,
metadata: Union[Dict[str, Any], None] = None,
**kwargs: Any,
) -> None:
if self.__has_valid_config is False:
return
try:
user_id = _get_user_id(metadata)
user_props = _get_user_props(metadata)
params = kwargs.get("invocation_params", {})
params.update(
serialized.get("kwargs", {})
) # Sometimes, for example with ChatAnthropic, `invocation_params` is empty
name = (
params.get("model")
or params.get("model_name")
or params.get("model_id")
)
if not name and "anthropic" in params.get("_type"):
name = "claude-2"
extra = {
param: params.get(param)
for param in PARAMS_TO_CAPTURE
if params.get(param) is not None
}
input = _parse_input(prompts)
self.__track_event(
"llm",
"start",
user_id=user_id,
run_id=str(run_id),
parent_run_id=str(parent_run_id) if parent_run_id else None,
name=name,
input=input,
tags=tags,
extra=extra,
metadata=metadata,
user_props=user_props,
app_id=self.__app_id,
)
except Exception as e:
warnings.warn(f"[LLMonitor] An error occurred in on_llm_start: {e}")
def on_chat_model_start(
self,
serialized: Dict[str, Any],
messages: List[List[BaseMessage]],
*,
run_id: UUID,
parent_run_id: Union[UUID, None] = None,
tags: Union[List[str], None] = None,
metadata: Union[Dict[str, Any], None] = None,
**kwargs: Any,
) -> Any:
if self.__has_valid_config is False:
return
try:
user_id = _get_user_id(metadata)
user_props = _get_user_props(metadata)
params = kwargs.get("invocation_params", {})
params.update(
serialized.get("kwargs", {})
) # Sometimes, for example with ChatAnthropic, `invocation_params` is empty
name = (
params.get("model")
or params.get("model_name")
or params.get("model_id")
)
if not name and "anthropic" in params.get("_type"):
name = "claude-2"
extra = {
param: params.get(param)
for param in PARAMS_TO_CAPTURE
if params.get(param) is not None
}
input = _parse_lc_messages(messages[0])
self.__track_event(
"llm",
"start",
user_id=user_id,
run_id=str(run_id),
parent_run_id=str(parent_run_id) if parent_run_id else None,
name=name,
input=input,
tags=tags,
extra=extra,
metadata=metadata,
user_props=user_props,
app_id=self.__app_id,
)
except Exception as e:
logger.error(f"[LLMonitor] An error occurred in on_chat_model_start: {e}")
def on_llm_end(
self,
response: LLMResult,
*,
run_id: UUID,
parent_run_id: Union[UUID, None] = None,
**kwargs: Any,
) -> None:
if self.__has_valid_config is False:
return
try:
token_usage = (response.llm_output or {}).get("token_usage", {})
parsed_output: Any = [
_parse_lc_message(generation.message)
if hasattr(generation, "message")
else generation.text
for generation in response.generations[0]
]
# if it's an array of 1, just parse the first element
if len(parsed_output) == 1:
parsed_output = parsed_output[0]
self.__track_event(
"llm",
"end",
run_id=str(run_id),
parent_run_id=str(parent_run_id) if parent_run_id else None,
output=parsed_output,
token_usage={
"prompt": token_usage.get("prompt_tokens"),
"completion": token_usage.get("completion_tokens"),
},
app_id=self.__app_id,
)
except Exception as e:
logger.error(f"[LLMonitor] An error occurred in on_llm_end: {e}")
def on_tool_start(
self,
serialized: Dict[str, Any],
input_str: str,
*,
run_id: UUID,
parent_run_id: Union[UUID, None] = None,
tags: Union[List[str], None] = None,
metadata: Union[Dict[str, Any], None] = None,
**kwargs: Any,
) -> None:
if self.__has_valid_config is False:
return
try:
user_id = _get_user_id(metadata)
user_props = _get_user_props(metadata)
name = serialized.get("name")
self.__track_event(
"tool",
"start",
user_id=user_id,
run_id=str(run_id),
parent_run_id=str(parent_run_id) if parent_run_id else None,
name=name,
input=input_str,
tags=tags,
metadata=metadata,
user_props=user_props,
app_id=self.__app_id,
)
except Exception as e:
logger.error(f"[LLMonitor] An error occurred in on_tool_start: {e}")
| |
158720
|
"""Callback handler for Context AI"""
import os
from typing import Any, Dict, List
from uuid import UUID
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.messages import BaseMessage
from langchain_core.outputs import LLMResult
from langchain_core.utils import guard_import
def import_context() -> Any:
"""Import the `getcontext` package."""
return (
guard_import("getcontext", pip_name="python-context"),
guard_import("getcontext.token", pip_name="python-context").Credential,
guard_import(
"getcontext.generated.models", pip_name="python-context"
).Conversation,
guard_import("getcontext.generated.models", pip_name="python-context").Message,
guard_import(
"getcontext.generated.models", pip_name="python-context"
).MessageRole,
guard_import("getcontext.generated.models", pip_name="python-context").Rating,
)
class ContextCallbackHandler(BaseCallbackHandler):
"""Callback Handler that records transcripts to the Context service.
(https://context.ai).
Keyword Args:
token (optional): The token with which to authenticate requests to Context.
Visit https://with.context.ai/settings to generate a token.
If not provided, the value of the `CONTEXT_TOKEN` environment
variable will be used.
Raises:
ImportError: if the `context-python` package is not installed.
Chat Example:
>>> from langchain_community.llms import ChatOpenAI
>>> from langchain_community.callbacks import ContextCallbackHandler
>>> context_callback = ContextCallbackHandler(
... token="<CONTEXT_TOKEN_HERE>",
... )
>>> chat = ChatOpenAI(
... temperature=0,
... headers={"user_id": "123"},
... callbacks=[context_callback],
... openai_api_key="API_KEY_HERE",
... )
>>> messages = [
... SystemMessage(content="You translate English to French."),
... HumanMessage(content="I love programming with LangChain."),
... ]
>>> chat.invoke(messages)
Chain Example:
>>> from langchain.chains import LLMChain
>>> from langchain_community.chat_models import ChatOpenAI
>>> from langchain_community.callbacks import ContextCallbackHandler
>>> context_callback = ContextCallbackHandler(
... token="<CONTEXT_TOKEN_HERE>",
... )
>>> human_message_prompt = HumanMessagePromptTemplate(
... prompt=PromptTemplate(
... template="What is a good name for a company that makes {product}?",
... input_variables=["product"],
... ),
... )
>>> chat_prompt_template = ChatPromptTemplate.from_messages(
... [human_message_prompt]
... )
>>> callback = ContextCallbackHandler(token)
>>> # Note: the same callback object must be shared between the
... LLM and the chain.
>>> chat = ChatOpenAI(temperature=0.9, callbacks=[callback])
>>> chain = LLMChain(
... llm=chat,
... prompt=chat_prompt_template,
... callbacks=[callback]
... )
>>> chain.run("colorful socks")
"""
def __init__(self, token: str = "", verbose: bool = False, **kwargs: Any) -> None:
(
self.context,
self.credential,
self.conversation_model,
self.message_model,
self.message_role_model,
self.rating_model,
) = import_context()
token = token or os.environ.get("CONTEXT_TOKEN") or ""
self.client = self.context.ContextAPI(credential=self.credential(token))
self.chain_run_id = None
self.llm_model = None
self.messages: List[Any] = []
self.metadata: Dict[str, str] = {}
def on_chat_model_start(
self,
serialized: Dict[str, Any],
messages: List[List[BaseMessage]],
*,
run_id: UUID,
**kwargs: Any,
) -> Any:
"""Run when the chat model is started."""
llm_model = kwargs.get("invocation_params", {}).get("model", None)
if llm_model is not None:
self.metadata["model"] = llm_model
if len(messages) == 0:
return
for message in messages[0]:
role = self.message_role_model.SYSTEM
if message.type == "human":
role = self.message_role_model.USER
elif message.type == "system":
role = self.message_role_model.SYSTEM
elif message.type == "ai":
role = self.message_role_model.ASSISTANT
self.messages.append(
self.message_model(
message=message.content,
role=role,
)
)
def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""Run when LLM ends."""
if len(response.generations) == 0 or len(response.generations[0]) == 0:
return
if not self.chain_run_id:
generation = response.generations[0][0]
self.messages.append(
self.message_model(
message=generation.text,
role=self.message_role_model.ASSISTANT,
)
)
self._log_conversation()
def on_chain_start(
self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
) -> None:
"""Run when chain starts."""
self.chain_run_id = kwargs.get("run_id", None)
def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
"""Run when chain ends."""
self.messages.append(
self.message_model(
message=outputs["text"],
role=self.message_role_model.ASSISTANT,
)
)
self._log_conversation()
self.chain_run_id = None
def _log_conversation(self) -> None:
"""Log the conversation to the context API."""
if len(self.messages) == 0:
return
self.client.log.conversation_upsert(
body={
"conversation": self.conversation_model(
messages=self.messages,
metadata=self.metadata,
)
}
)
self.messages = []
self.metadata = {}
| |
158723
|
def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
"""Run on agent action."""
self.step += 1
self.tool_starts += 1
self.starts += 1
resp: Dict[str, Any] = {}
resp.update(
{
"action": "on_agent_action",
"tool": action.tool,
"tool_input": action.tool_input,
"log": action.log,
}
)
resp.update(self.get_custom_callback_meta())
self.deck.append(self.markdown_renderer().to_html("### Agent Action"))
self.deck.append(
self.table_renderer().to_html(self.pandas.DataFrame([resp])) + "\n"
)
| |
158787
|
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
default_chunk_class = AIMessageChunk
self.client.arun(
[convert_message_to_dict(m) for m in messages],
self.spark_user_id,
self.model_kwargs,
streaming=True,
)
for content in self.client.subscribe(timeout=self.request_timeout):
if "data" not in content:
continue
delta = content["data"]
chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
cg_chunk = ChatGenerationChunk(message=chunk)
if run_manager:
run_manager.on_llm_new_token(str(chunk.content), chunk=cg_chunk)
yield cg_chunk
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stream: Optional[bool] = None,
**kwargs: Any,
) -> ChatResult:
if stream or self.streaming:
stream_iter = self._stream(
messages=messages, stop=stop, run_manager=run_manager, **kwargs
)
return generate_from_stream(stream_iter)
self.client.arun(
[convert_message_to_dict(m) for m in messages],
self.spark_user_id,
self.model_kwargs,
False,
)
completion = {}
llm_output = {}
for content in self.client.subscribe(timeout=self.request_timeout):
if "usage" in content:
llm_output["token_usage"] = content["usage"]
if "data" not in content:
continue
completion = content["data"]
message = convert_dict_to_message(completion)
generations = [ChatGeneration(message=message)]
return ChatResult(generations=generations, llm_output=llm_output)
@property
def _llm_type(self) -> str:
return "spark-llm-chat"
class _SparkLLMClient:
"""
Use websocket-client to call the SparkLLM interface provided by Xfyun,
which is the iFlyTek's open platform for AI capabilities
"""
def __init__(
self,
app_id: str,
api_key: str,
api_secret: str,
api_url: Optional[str] = None,
spark_domain: Optional[str] = None,
model_kwargs: Optional[dict] = None,
):
try:
import websocket
self.websocket_client = websocket
except ImportError:
raise ImportError(
"Could not import websocket client python package. "
"Please install it with `pip install websocket-client`."
)
self.api_url = SPARK_API_URL if not api_url else api_url
self.app_id = app_id
self.model_kwargs = model_kwargs
self.spark_domain = spark_domain or SPARK_LLM_DOMAIN
self.queue: Queue[Dict] = Queue()
self.blocking_message = {"content": "", "role": "assistant"}
self.api_key = api_key
self.api_secret = api_secret
@staticmethod
def _create_url(api_url: str, api_key: str, api_secret: str) -> str:
"""
Generate a request url with an api key and an api secret.
"""
# generate timestamp by RFC1123
date = format_date_time(mktime(datetime.now().timetuple()))
# urlparse
parsed_url = urlparse(api_url)
host = parsed_url.netloc
path = parsed_url.path
signature_origin = f"host: {host}\ndate: {date}\nGET {path} HTTP/1.1"
# encrypt using hmac-sha256
signature_sha = hmac.new(
api_secret.encode("utf-8"),
signature_origin.encode("utf-8"),
digestmod=hashlib.sha256,
).digest()
signature_sha_base64 = base64.b64encode(signature_sha).decode(encoding="utf-8")
authorization_origin = f'api_key="{api_key}", algorithm="hmac-sha256", \
headers="host date request-line", signature="{signature_sha_base64}"'
authorization = base64.b64encode(authorization_origin.encode("utf-8")).decode(
encoding="utf-8"
)
# generate url
params_dict = {"authorization": authorization, "date": date, "host": host}
encoded_params = urlencode(params_dict)
url = urlunparse(
(
parsed_url.scheme,
parsed_url.netloc,
parsed_url.path,
parsed_url.params,
encoded_params,
parsed_url.fragment,
)
)
return url
def run(
self,
messages: List[Dict],
user_id: str,
model_kwargs: Optional[dict] = None,
streaming: bool = False,
) -> None:
self.websocket_client.enableTrace(False)
ws = self.websocket_client.WebSocketApp(
_SparkLLMClient._create_url(
self.api_url,
self.api_key,
self.api_secret,
),
on_message=self.on_message,
on_error=self.on_error,
on_close=self.on_close,
on_open=self.on_open,
)
ws.messages = messages # type: ignore[attr-defined]
ws.user_id = user_id # type: ignore[attr-defined]
ws.model_kwargs = self.model_kwargs if model_kwargs is None else model_kwargs # type: ignore[attr-defined]
ws.streaming = streaming # type: ignore[attr-defined]
ws.run_forever()
def arun(
self,
messages: List[Dict],
user_id: str,
model_kwargs: Optional[dict] = None,
streaming: bool = False,
) -> threading.Thread:
ws_thread = threading.Thread(
target=self.run,
args=(
messages,
user_id,
model_kwargs,
streaming,
),
)
ws_thread.start()
return ws_thread
def on_error(self, ws: Any, error: Optional[Any]) -> None:
self.queue.put({"error": error})
ws.close()
def on_close(self, ws: Any, close_status_code: int, close_reason: str) -> None:
logger.debug(
{
"log": {
"close_status_code": close_status_code,
"close_reason": close_reason,
}
}
)
self.queue.put({"done": True})
def on_open(self, ws: Any) -> None:
self.blocking_message = {"content": "", "role": "assistant"}
data = json.dumps(
self.gen_params(
messages=ws.messages, user_id=ws.user_id, model_kwargs=ws.model_kwargs
)
)
ws.send(data)
def on_message(self, ws: Any, message: str) -> None:
data = json.loads(message)
code = data["header"]["code"]
if code != 0:
self.queue.put(
{"error": f"Code: {code}, Error: {data['header']['message']}"}
)
ws.close()
else:
choices = data["payload"]["choices"]
status = choices["status"]
content = choices["text"][0]["content"]
if ws.streaming:
self.queue.put({"data": choices["text"][0]})
else:
self.blocking_message["content"] += content
if status == 2:
if not ws.streaming:
self.queue.put({"data": self.blocking_message})
usage_data = (
data.get("payload", {}).get("usage", {}).get("text", {})
if data
else {}
)
self.queue.put({"usage": usage_data})
ws.close()
def gen_params(
self, messages: list, user_id: str, model_kwargs: Optional[dict] = None
) -> dict:
data: Dict = {
"header": {"app_id": self.app_id, "uid": user_id},
"parameter": {"chat": {"domain": self.spark_domain}},
"payload": {"message": {"text": messages}},
}
if model_kwargs:
data["parameter"]["chat"].update(model_kwargs)
logger.debug(f"Spark Request Parameters: {data}")
return data
def subscribe(self, timeout: Optional[int] = 30) -> Generator[Dict, None, None]:
while True:
try:
content = self.queue.get(timeout=timeout)
except queue.Empty as _:
raise TimeoutError(
f"SparkLLMClient wait LLM api response timeout {timeout} seconds"
)
if "error" in content:
raise ConnectionError(content["error"])
if "usage" in content:
yield content
continue
if "done" in content:
break
if "data" not in content:
break
yield content
| |
158829
|
"""**Chat Models** are a variation on language models.
While Chat Models use language models under the hood, the interface they expose
is a bit different. Rather than expose a "text in, text out" API, they expose
an interface where "chat messages" are the inputs and outputs.
**Class hierarchy:**
.. code-block::
BaseLanguageModel --> BaseChatModel --> <name> # Examples: ChatOpenAI, ChatGooglePalm
**Main helpers:**
.. code-block::
AIMessage, BaseMessage, HumanMessage
""" # noqa: E501
import importlib
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from langchain_community.chat_models.anthropic import (
ChatAnthropic,
)
from langchain_community.chat_models.anyscale import (
ChatAnyscale,
)
from langchain_community.chat_models.azure_openai import (
AzureChatOpenAI,
)
from langchain_community.chat_models.baichuan import (
ChatBaichuan,
)
from langchain_community.chat_models.baidu_qianfan_endpoint import (
QianfanChatEndpoint,
)
from langchain_community.chat_models.bedrock import (
BedrockChat,
)
from langchain_community.chat_models.cohere import (
ChatCohere,
)
from langchain_community.chat_models.coze import (
ChatCoze,
)
from langchain_community.chat_models.databricks import (
ChatDatabricks,
)
from langchain_community.chat_models.deepinfra import (
ChatDeepInfra,
)
from langchain_community.chat_models.edenai import ChatEdenAI
from langchain_community.chat_models.ernie import (
ErnieBotChat,
)
from langchain_community.chat_models.everlyai import (
ChatEverlyAI,
)
from langchain_community.chat_models.fake import (
FakeListChatModel,
)
from langchain_community.chat_models.fireworks import (
ChatFireworks,
)
from langchain_community.chat_models.friendli import (
ChatFriendli,
)
from langchain_community.chat_models.gigachat import (
GigaChat,
)
from langchain_community.chat_models.google_palm import (
ChatGooglePalm,
)
from langchain_community.chat_models.gpt_router import (
GPTRouter,
)
from langchain_community.chat_models.huggingface import (
ChatHuggingFace,
)
from langchain_community.chat_models.human import (
HumanInputChatModel,
)
from langchain_community.chat_models.hunyuan import (
ChatHunyuan,
)
from langchain_community.chat_models.javelin_ai_gateway import (
ChatJavelinAIGateway,
)
from langchain_community.chat_models.jinachat import (
JinaChat,
)
from langchain_community.chat_models.kinetica import (
ChatKinetica,
)
from langchain_community.chat_models.konko import (
ChatKonko,
)
from langchain_community.chat_models.litellm import (
ChatLiteLLM,
)
from langchain_community.chat_models.litellm_router import (
ChatLiteLLMRouter,
)
from langchain_community.chat_models.llama_edge import (
LlamaEdgeChatService,
)
from langchain_community.chat_models.llamacpp import ChatLlamaCpp
from langchain_community.chat_models.maritalk import (
ChatMaritalk,
)
from langchain_community.chat_models.minimax import (
MiniMaxChat,
)
from langchain_community.chat_models.mlflow import (
ChatMlflow,
)
from langchain_community.chat_models.mlflow_ai_gateway import (
ChatMLflowAIGateway,
)
from langchain_community.chat_models.mlx import (
ChatMLX,
)
from langchain_community.chat_models.moonshot import (
MoonshotChat,
)
from langchain_community.chat_models.oci_data_science import (
ChatOCIModelDeployment,
ChatOCIModelDeploymentTGI,
ChatOCIModelDeploymentVLLM,
)
from langchain_community.chat_models.oci_generative_ai import (
ChatOCIGenAI, # noqa: F401
)
from langchain_community.chat_models.octoai import ChatOctoAI
from langchain_community.chat_models.ollama import (
ChatOllama,
)
from langchain_community.chat_models.openai import (
ChatOpenAI,
)
from langchain_community.chat_models.pai_eas_endpoint import (
PaiEasChatEndpoint,
)
from langchain_community.chat_models.perplexity import (
ChatPerplexity,
)
from langchain_community.chat_models.premai import (
ChatPremAI,
)
from langchain_community.chat_models.promptlayer_openai import (
PromptLayerChatOpenAI,
)
from langchain_community.chat_models.sambanova import (
ChatSambaNovaCloud,
ChatSambaStudio,
)
from langchain_community.chat_models.snowflake import (
ChatSnowflakeCortex,
)
from langchain_community.chat_models.solar import (
SolarChat,
)
from langchain_community.chat_models.sparkllm import (
ChatSparkLLM,
)
from langchain_community.chat_models.symblai_nebula import ChatNebula
from langchain_community.chat_models.tongyi import (
ChatTongyi,
)
from langchain_community.chat_models.vertexai import (
ChatVertexAI,
)
from langchain_community.chat_models.volcengine_maas import (
VolcEngineMaasChat,
)
from langchain_community.chat_models.yandex import (
ChatYandexGPT,
)
from langchain_community.chat_models.yi import (
ChatYi,
)
from langchain_community.chat_models.yuan2 import (
ChatYuan2,
)
from langchain_community.chat_models.zhipuai import (
ChatZhipuAI,
)
__all__ = [
"AzureChatOpenAI",
"BedrockChat",
"ChatAnthropic",
"ChatAnyscale",
"ChatBaichuan",
"ChatCohere",
"ChatCoze",
"ChatOctoAI",
"ChatDatabricks",
"ChatDeepInfra",
"ChatEdenAI",
"ChatEverlyAI",
"ChatFireworks",
"ChatFriendli",
"ChatGooglePalm",
"ChatHuggingFace",
"ChatHunyuan",
"ChatJavelinAIGateway",
"ChatKinetica",
"ChatKonko",
"ChatLiteLLM",
"ChatLiteLLMRouter",
"ChatMLX",
"ChatMLflowAIGateway",
"ChatMaritalk",
"ChatMlflow",
"ChatNebula",
"ChatOCIGenAI",
"ChatOCIModelDeployment",
"ChatOCIModelDeploymentVLLM",
"ChatOCIModelDeploymentTGI",
"ChatOllama",
"ChatOpenAI",
"ChatPerplexity",
"ChatPremAI",
"ChatSambaNovaCloud",
"ChatSambaStudio",
"ChatSparkLLM",
"ChatSnowflakeCortex",
"ChatTongyi",
"ChatVertexAI",
"ChatYandexGPT",
"ChatYuan2",
"ChatZhipuAI",
"ChatLlamaCpp",
"ErnieBotChat",
"FakeListChatModel",
"GPTRouter",
"GigaChat",
"HumanInputChatModel",
"JinaChat",
"LlamaEdgeChatService",
"MiniMaxChat",
"MoonshotChat",
"PaiEasChatEndpoint",
"PromptLayerChatOpenAI",
"QianfanChatEndpoint",
"SolarChat",
"VolcEngineMaasChat",
"ChatYi",
]
| |
158830
|
_module_lookup = {
"AzureChatOpenAI": "langchain_community.chat_models.azure_openai",
"BedrockChat": "langchain_community.chat_models.bedrock",
"ChatAnthropic": "langchain_community.chat_models.anthropic",
"ChatAnyscale": "langchain_community.chat_models.anyscale",
"ChatBaichuan": "langchain_community.chat_models.baichuan",
"ChatCohere": "langchain_community.chat_models.cohere",
"ChatCoze": "langchain_community.chat_models.coze",
"ChatDatabricks": "langchain_community.chat_models.databricks",
"ChatDeepInfra": "langchain_community.chat_models.deepinfra",
"ChatEverlyAI": "langchain_community.chat_models.everlyai",
"ChatEdenAI": "langchain_community.chat_models.edenai",
"ChatFireworks": "langchain_community.chat_models.fireworks",
"ChatFriendli": "langchain_community.chat_models.friendli",
"ChatGooglePalm": "langchain_community.chat_models.google_palm",
"ChatHuggingFace": "langchain_community.chat_models.huggingface",
"ChatHunyuan": "langchain_community.chat_models.hunyuan",
"ChatJavelinAIGateway": "langchain_community.chat_models.javelin_ai_gateway",
"ChatKinetica": "langchain_community.chat_models.kinetica",
"ChatKonko": "langchain_community.chat_models.konko",
"ChatLiteLLM": "langchain_community.chat_models.litellm",
"ChatLiteLLMRouter": "langchain_community.chat_models.litellm_router",
"ChatMLflowAIGateway": "langchain_community.chat_models.mlflow_ai_gateway",
"ChatMLX": "langchain_community.chat_models.mlx",
"ChatMaritalk": "langchain_community.chat_models.maritalk",
"ChatMlflow": "langchain_community.chat_models.mlflow",
"ChatNebula": "langchain_community.chat_models.symblai_nebula",
"ChatOctoAI": "langchain_community.chat_models.octoai",
"ChatOCIGenAI": "langchain_community.chat_models.oci_generative_ai",
"ChatOCIModelDeployment": "langchain_community.chat_models.oci_data_science",
"ChatOCIModelDeploymentVLLM": "langchain_community.chat_models.oci_data_science",
"ChatOCIModelDeploymentTGI": "langchain_community.chat_models.oci_data_science",
"ChatOllama": "langchain_community.chat_models.ollama",
"ChatOpenAI": "langchain_community.chat_models.openai",
"ChatPerplexity": "langchain_community.chat_models.perplexity",
"ChatSambaNovaCloud": "langchain_community.chat_models.sambanova",
"ChatSambaStudio": "langchain_community.chat_models.sambanova",
"ChatSnowflakeCortex": "langchain_community.chat_models.snowflake",
"ChatSparkLLM": "langchain_community.chat_models.sparkllm",
"ChatTongyi": "langchain_community.chat_models.tongyi",
"ChatVertexAI": "langchain_community.chat_models.vertexai",
"ChatYandexGPT": "langchain_community.chat_models.yandex",
"ChatYuan2": "langchain_community.chat_models.yuan2",
"ChatZhipuAI": "langchain_community.chat_models.zhipuai",
"ErnieBotChat": "langchain_community.chat_models.ernie",
"FakeListChatModel": "langchain_community.chat_models.fake",
"GPTRouter": "langchain_community.chat_models.gpt_router",
"GigaChat": "langchain_community.chat_models.gigachat",
"HumanInputChatModel": "langchain_community.chat_models.human",
"JinaChat": "langchain_community.chat_models.jinachat",
"LlamaEdgeChatService": "langchain_community.chat_models.llama_edge",
"MiniMaxChat": "langchain_community.chat_models.minimax",
"MoonshotChat": "langchain_community.chat_models.moonshot",
"PaiEasChatEndpoint": "langchain_community.chat_models.pai_eas_endpoint",
"PromptLayerChatOpenAI": "langchain_community.chat_models.promptlayer_openai",
"SolarChat": "langchain_community.chat_models.solar",
"QianfanChatEndpoint": "langchain_community.chat_models.baidu_qianfan_endpoint",
"VolcEngineMaasChat": "langchain_community.chat_models.volcengine_maas",
"ChatPremAI": "langchain_community.chat_models.premai",
"ChatLlamaCpp": "langchain_community.chat_models.llamacpp",
"ChatYi": "langchain_community.chat_models.yi",
}
def __getattr__(name: str) -> Any:
if name in _module_lookup:
module = importlib.import_module(_module_lookup[name])
return getattr(module, name)
raise AttributeError(f"module {__name__} has no attribute {name}")
| |
158844
|
class ChatOpenAI(BaseChatModel):
"""`OpenAI` Chat large language models API.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example:
.. code-block:: python
from langchain_community.chat_models import ChatOpenAI
openai = ChatOpenAI(model="gpt-3.5-turbo")
"""
@property
def lc_secrets(self) -> Dict[str, str]:
return {"openai_api_key": "OPENAI_API_KEY"}
@classmethod
def get_lc_namespace(cls) -> List[str]:
"""Get the namespace of the langchain object."""
return ["langchain", "chat_models", "openai"]
@property
def lc_attributes(self) -> Dict[str, Any]:
attributes: Dict[str, Any] = {}
if self.openai_organization:
attributes["openai_organization"] = self.openai_organization
if self.openai_api_base:
attributes["openai_api_base"] = self.openai_api_base
if self.openai_proxy:
attributes["openai_proxy"] = self.openai_proxy
return attributes
@classmethod
def is_lc_serializable(cls) -> bool:
"""Return whether this model can be serialized by Langchain."""
return True
client: Any = Field(default=None, exclude=True) #: :meta private:
async_client: Any = Field(default=None, exclude=True) #: :meta private:
model_name: str = Field(default="gpt-3.5-turbo", alias="model")
"""Model name to use."""
temperature: float = 0.7
"""What sampling temperature to use."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
# When updating this to use a SecretStr
# Check for classes that derive from this class (as some of them
# may assume openai_api_key is a str)
openai_api_key: Optional[str] = Field(default=None, alias="api_key")
"""Automatically inferred from env var `OPENAI_API_KEY` if not provided."""
openai_api_base: Optional[str] = Field(default=None, alias="base_url")
"""Base URL path for API requests, leave blank if not using a proxy or service
emulator."""
openai_organization: Optional[str] = Field(default=None, alias="organization")
"""Automatically inferred from env var `OPENAI_ORG_ID` if not provided."""
# to support explicit proxy for OpenAI
openai_proxy: Optional[str] = None
request_timeout: Union[float, Tuple[float, float], Any, None] = Field(
default=None, alias="timeout"
)
"""Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or
None."""
max_retries: int = Field(default=2)
"""Maximum number of retries to make when generating."""
streaming: bool = False
"""Whether to stream the results or not."""
n: int = 1
"""Number of chat completions to generate for each prompt."""
max_tokens: Optional[int] = None
"""Maximum number of tokens to generate."""
tiktoken_model_name: Optional[str] = None
"""The model name to pass to tiktoken when using this class.
Tiktoken is used to count the number of tokens in documents to constrain
them to be under a certain limit. By default, when set to None, this will
be the same as the embedding model name. However, there are some cases
where you may want to use this Embedding class with a model name not
supported by tiktoken. This can include when using Azure embeddings or
when using one of the many model providers that expose an OpenAI-like
API but with different models. In those cases, in order to avoid erroring
when tiktoken is called, you can specify a model name to use here."""
default_headers: Union[Mapping[str, str], None] = None
default_query: Union[Mapping[str, object], None] = None
# Configure a custom httpx client. See the
# [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
http_client: Union[Any, None] = None
"""Optional httpx.Client."""
model_config = ConfigDict(
populate_by_name=True,
)
@model_validator(mode="before")
@classmethod
def build_extra(cls, values: Dict[str, Any]) -> Any:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
if field_name not in all_required_field_names:
logger.warning(
f"""WARNING! {field_name} is not default parameter.
{field_name} was transferred to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
if invalid_model_kwargs:
raise ValueError(
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
f"Instead they were passed in as part of `model_kwargs` parameter."
)
values["model_kwargs"] = extra
return values
@pre_init
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
if values["n"] < 1:
raise ValueError("n must be at least 1.")
if values["n"] > 1 and values["streaming"]:
raise ValueError("n must be 1 when streaming.")
values["openai_api_key"] = get_from_dict_or_env(
values, "openai_api_key", "OPENAI_API_KEY"
)
# Check OPENAI_ORGANIZATION for backwards compatibility.
values["openai_organization"] = (
values["openai_organization"]
or os.getenv("OPENAI_ORG_ID")
or os.getenv("OPENAI_ORGANIZATION")
)
values["openai_api_base"] = values["openai_api_base"] or os.getenv(
"OPENAI_API_BASE"
)
values["openai_proxy"] = get_from_dict_or_env(
values,
"openai_proxy",
"OPENAI_PROXY",
default="",
)
try:
import openai
except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
if is_openai_v1():
client_params = {
"api_key": values["openai_api_key"],
"organization": values["openai_organization"],
"base_url": values["openai_api_base"],
"timeout": values["request_timeout"],
"max_retries": values["max_retries"],
"default_headers": values["default_headers"],
"default_query": values["default_query"],
"http_client": values["http_client"],
}
if not values.get("client"):
values["client"] = openai.OpenAI(**client_params).chat.completions
if not values.get("async_client"):
values["async_client"] = openai.AsyncOpenAI(
**client_params
).chat.completions
elif not values.get("client"):
values["client"] = openai.ChatCompletion
else:
pass
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling OpenAI API."""
params = {
"model": self.model_name,
"stream": self.streaming,
"n": self.n,
"temperature": self.temperature,
**self.model_kwargs,
}
if self.max_tokens is not None:
params["max_tokens"] = self.max_tokens
if self.request_timeout is not None and not is_openai_v1():
params["request_timeout"] = self.request_timeout
return params
def completion_with_retry(
self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any
) -> Any:
"""Use tenacity to retry the completion call."""
if is_openai_v1():
return self.client.create(**kwargs)
retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
@retry_decorator
def _completion_with_retry(**kwargs: Any) -> Any:
return self.client.create(**kwargs)
return _completion_with_retry(**kwargs)
| |
158850
|
class AzureChatOpenAI(ChatOpenAI):
"""`Azure OpenAI` Chat Completion API.
To use this class you
must have a deployed model on Azure OpenAI. Use `deployment_name` in the
constructor to refer to the "Model deployment name" in the Azure portal.
In addition, you should have the ``openai`` python package installed, and the
following environment variables set or passed in constructor in lower case:
- ``AZURE_OPENAI_API_KEY``
- ``AZURE_OPENAI_ENDPOINT``
- ``AZURE_OPENAI_AD_TOKEN``
- ``OPENAI_API_VERSION``
- ``OPENAI_PROXY``
For example, if you have `gpt-35-turbo` deployed, with the deployment name
`35-turbo-dev`, the constructor should look like:
.. code-block:: python
AzureChatOpenAI(
azure_deployment="35-turbo-dev",
openai_api_version="2023-05-15",
)
Be aware the API version may change.
You can also specify the version of the model using ``model_version`` constructor
parameter, as Azure OpenAI doesn't return model version with the response.
Default is empty. When you specify the version, it will be appended to the
model name in the response. Setting correct version will help you to calculate the
cost properly. Model version is not validated, so make sure you set it correctly
to get the correct cost.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
"""
azure_endpoint: Union[str, None] = None
"""Your Azure endpoint, including the resource.
Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided.
Example: `https://example-resource.azure.openai.com/`
"""
deployment_name: Union[str, None] = Field(default=None, alias="azure_deployment")
"""A model deployment.
If given sets the base client URL to include `/deployments/{azure_deployment}`.
Note: this means you won't be able to use non-deployment endpoints.
"""
openai_api_version: str = Field(default="", alias="api_version")
"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
openai_api_key: Union[str, None] = Field(default=None, alias="api_key")
"""Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided."""
azure_ad_token: Union[str, None] = None
"""Your Azure Active Directory token.
Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided.
For more:
https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
"""
azure_ad_token_provider: Union[Callable[[], str], None] = None
"""A function that returns an Azure Active Directory token.
Will be invoked on every request.
"""
model_version: str = ""
"""Legacy, for openai<1.0.0 support."""
openai_api_type: str = ""
"""Legacy, for openai<1.0.0 support."""
validate_base_url: bool = True
"""For backwards compatibility. If legacy val openai_api_base is passed in, try to
infer if it is a base_url or azure_endpoint and update accordingly.
"""
@classmethod
def get_lc_namespace(cls) -> List[str]:
"""Get the namespace of the langchain object."""
return ["langchain", "chat_models", "azure_openai"]
@pre_init
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
if values["n"] < 1:
raise ValueError("n must be at least 1.")
if values["n"] > 1 and values["streaming"]:
raise ValueError("n must be 1 when streaming.")
# Check OPENAI_KEY for backwards compatibility.
# TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using
# other forms of azure credentials.
values["openai_api_key"] = (
values["openai_api_key"]
or os.getenv("AZURE_OPENAI_API_KEY")
or os.getenv("OPENAI_API_KEY")
)
values["openai_api_base"] = values["openai_api_base"] or os.getenv(
"OPENAI_API_BASE"
)
values["openai_api_version"] = values["openai_api_version"] or os.getenv(
"OPENAI_API_VERSION"
)
# Check OPENAI_ORGANIZATION for backwards compatibility.
values["openai_organization"] = (
values["openai_organization"]
or os.getenv("OPENAI_ORG_ID")
or os.getenv("OPENAI_ORGANIZATION")
)
values["azure_endpoint"] = values["azure_endpoint"] or os.getenv(
"AZURE_OPENAI_ENDPOINT"
)
values["azure_ad_token"] = values["azure_ad_token"] or os.getenv(
"AZURE_OPENAI_AD_TOKEN"
)
values["openai_api_type"] = get_from_dict_or_env(
values, "openai_api_type", "OPENAI_API_TYPE", default="azure"
)
values["openai_proxy"] = get_from_dict_or_env(
values, "openai_proxy", "OPENAI_PROXY", default=""
)
try:
import openai
except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
if is_openai_v1():
# For backwards compatibility. Before openai v1, no distinction was made
# between azure_endpoint and base_url (openai_api_base).
openai_api_base = values["openai_api_base"]
if openai_api_base and values["validate_base_url"]:
if "/openai" not in openai_api_base:
values["openai_api_base"] = (
values["openai_api_base"].rstrip("/") + "/openai"
)
warnings.warn(
"As of openai>=1.0.0, Azure endpoints should be specified via "
f"the `azure_endpoint` param not `openai_api_base` "
f"(or alias `base_url`). Updating `openai_api_base` from "
f"{openai_api_base} to {values['openai_api_base']}."
)
if values["deployment_name"]:
warnings.warn(
"As of openai>=1.0.0, if `deployment_name` (or alias "
"`azure_deployment`) is specified then "
"`openai_api_base` (or alias `base_url`) should not be. "
"Instead use `deployment_name` (or alias `azure_deployment`) "
"and `azure_endpoint`."
)
if values["deployment_name"] not in values["openai_api_base"]:
warnings.warn(
"As of openai>=1.0.0, if `openai_api_base` "
"(or alias `base_url`) is specified it is expected to be "
"of the form "
"https://example-resource.azure.openai.com/openai/deployments/example-deployment. " # noqa: E501
f"Updating {openai_api_base} to "
f"{values['openai_api_base']}."
)
values["openai_api_base"] += (
"/deployments/" + values["deployment_name"]
)
values["deployment_name"] = None
client_params = {
"api_version": values["openai_api_version"],
"azure_endpoint": values["azure_endpoint"],
"azure_deployment": values["deployment_name"],
"api_key": values["openai_api_key"],
"azure_ad_token": values["azure_ad_token"],
"azure_ad_token_provider": values["azure_ad_token_provider"],
"organization": values["openai_organization"],
"base_url": values["openai_api_base"],
"timeout": values["request_timeout"],
"max_retries": values["max_retries"],
"default_headers": values["default_headers"],
"default_query": values["default_query"],
"http_client": values["http_client"],
}
values["client"] = openai.AzureOpenAI(**client_params).chat.completions
values["async_client"] = openai.AsyncAzureOpenAI(
**client_params
).chat.completions
else:
values["client"] = openai.ChatCompletion
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling OpenAI API."""
if is_openai_v1():
return super()._default_params
else:
return {
**super()._default_params,
"engine": self.deployment_name,
}
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {**self._default_params}
| |
158894
|
@deprecated(
since="0.3.1",
removal="1.0.0",
alternative_import="langchain_ollama.ChatOllama",
)
class ChatOllama(BaseChatModel, _OllamaCommon):
"""Ollama locally runs large language models.
To use, follow the instructions at https://ollama.ai/.
Example:
.. code-block:: python
from langchain_community.chat_models import ChatOllama
ollama = ChatOllama(model="llama2")
"""
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "ollama-chat"
@classmethod
def is_lc_serializable(cls) -> bool:
"""Return whether this model can be serialized by Langchain."""
return False
def _get_ls_params(
self, stop: Optional[List[str]] = None, **kwargs: Any
) -> LangSmithParams:
"""Get standard params for tracing."""
params = self._get_invocation_params(stop=stop, **kwargs)
ls_params = LangSmithParams(
ls_provider="ollama",
ls_model_name=self.model,
ls_model_type="chat",
ls_temperature=params.get("temperature", self.temperature),
)
if ls_max_tokens := params.get("num_predict", self.num_predict):
ls_params["ls_max_tokens"] = ls_max_tokens
if ls_stop := stop or params.get("stop", None) or self.stop:
ls_params["ls_stop"] = ls_stop
return ls_params
@deprecated("0.0.3", alternative="_convert_messages_to_ollama_messages")
def _format_message_as_text(self, message: BaseMessage) -> str:
if isinstance(message, ChatMessage):
message_text = f"\n\n{message.role.capitalize()}: {message.content}"
elif isinstance(message, HumanMessage):
if isinstance(message.content, List):
first_content = cast(List[Dict], message.content)[0]
content_type = first_content.get("type")
if content_type == "text":
message_text = f"[INST] {first_content['text']} [/INST]"
elif content_type == "image_url":
message_text = first_content["image_url"]["url"]
else:
message_text = f"[INST] {message.content} [/INST]"
elif isinstance(message, AIMessage):
message_text = f"{message.content}"
elif isinstance(message, SystemMessage):
message_text = f"<<SYS>> {message.content} <</SYS>>"
else:
raise ValueError(f"Got unknown type {message}")
return message_text
def _format_messages_as_text(self, messages: List[BaseMessage]) -> str:
return "\n".join(
[self._format_message_as_text(message) for message in messages]
)
def _convert_messages_to_ollama_messages(
self, messages: List[BaseMessage]
) -> List[Dict[str, Union[str, List[str]]]]:
ollama_messages: List = []
for message in messages:
role = ""
if isinstance(message, HumanMessage):
role = "user"
elif isinstance(message, AIMessage):
role = "assistant"
elif isinstance(message, SystemMessage):
role = "system"
else:
raise ValueError("Received unsupported message type for Ollama.")
content = ""
images = []
if isinstance(message.content, str):
content = message.content
else:
for content_part in cast(List[Dict], message.content):
if content_part.get("type") == "text":
content += f"\n{content_part['text']}"
elif content_part.get("type") == "image_url":
image_url = None
temp_image_url = content_part.get("image_url")
if isinstance(temp_image_url, str):
image_url = content_part["image_url"]
elif (
isinstance(temp_image_url, dict) and "url" in temp_image_url
):
image_url = temp_image_url["url"]
else:
raise ValueError(
"Only string image_url or dict with string 'url' "
"inside content parts are supported."
)
image_url_components = image_url.split(",")
# Support data:image/jpeg;base64,<image> format
# and base64 strings
if len(image_url_components) > 1:
images.append(image_url_components[1])
else:
images.append(image_url_components[0])
else:
raise ValueError(
"Unsupported message content type. "
"Must either have type 'text' or type 'image_url' "
"with a string 'image_url' field."
)
ollama_messages.append(
{
"role": role,
"content": content,
"images": images,
}
)
return ollama_messages
def _create_chat_stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
**kwargs: Any,
) -> Iterator[str]:
payload = {
"model": self.model,
"messages": self._convert_messages_to_ollama_messages(messages),
}
yield from self._create_stream(
payload=payload, stop=stop, api_url=f"{self.base_url}/api/chat", **kwargs
)
async def _acreate_chat_stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
**kwargs: Any,
) -> AsyncIterator[str]:
payload = {
"model": self.model,
"messages": self._convert_messages_to_ollama_messages(messages),
}
async for stream_resp in self._acreate_stream(
payload=payload, stop=stop, api_url=f"{self.base_url}/api/chat", **kwargs
):
yield stream_resp
def _chat_stream_with_aggregation(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
verbose: bool = False,
**kwargs: Any,
) -> ChatGenerationChunk:
final_chunk: Optional[ChatGenerationChunk] = None
for stream_resp in self._create_chat_stream(messages, stop, **kwargs):
if stream_resp:
chunk = _chat_stream_response_to_chat_generation_chunk(stream_resp)
if final_chunk is None:
final_chunk = chunk
else:
final_chunk += chunk
if run_manager:
run_manager.on_llm_new_token(
chunk.text,
chunk=chunk,
verbose=verbose,
)
if final_chunk is None:
raise ValueError("No data received from Ollama stream.")
return final_chunk
async def _achat_stream_with_aggregation(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
verbose: bool = False,
**kwargs: Any,
) -> ChatGenerationChunk:
final_chunk: Optional[ChatGenerationChunk] = None
async for stream_resp in self._acreate_chat_stream(messages, stop, **kwargs):
if stream_resp:
chunk = _chat_stream_response_to_chat_generation_chunk(stream_resp)
if final_chunk is None:
final_chunk = chunk
else:
final_chunk += chunk
if run_manager:
await run_manager.on_llm_new_token(
chunk.text,
chunk=chunk,
verbose=verbose,
)
if final_chunk is None:
raise ValueError("No data received from Ollama stream.")
return final_chunk
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
"""Call out to Ollama's generate endpoint.
Args:
messages: The list of base messages to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
Chat generations from the model
Example:
.. code-block:: python
response = ollama([
HumanMessage(content="Tell me about the history of AI")
])
"""
final_chunk = self._chat_stream_with_aggregation(
messages,
stop=stop,
run_manager=run_manager,
verbose=self.verbose,
**kwargs,
)
chat_generation = ChatGeneration(
message=AIMessage(content=final_chunk.text),
generation_info=final_chunk.generation_info,
)
return ChatResult(generations=[chat_generation])
| |
158999
|
"""SQLAlchemy wrapper around a database."""
from __future__ import annotations
from typing import Any, Dict, Iterable, List, Literal, Optional, Sequence, Union
import sqlalchemy
from langchain_core._api import deprecated
from langchain_core.utils import get_from_env
from sqlalchemy import (
MetaData,
Table,
create_engine,
inspect,
select,
text,
)
from sqlalchemy.engine import URL, Engine, Result
from sqlalchemy.exc import ProgrammingError, SQLAlchemyError
from sqlalchemy.schema import CreateTable
from sqlalchemy.sql.expression import Executable
from sqlalchemy.types import NullType
def _format_index(index: sqlalchemy.engine.interfaces.ReflectedIndex) -> str:
return (
f'Name: {index["name"]}, Unique: {index["unique"]},'
f' Columns: {str(index["column_names"])}'
)
def truncate_word(content: Any, *, length: int, suffix: str = "...") -> str:
"""
Truncate a string to a certain number of words, based on the max string
length.
"""
if not isinstance(content, str) or length <= 0:
return content
if len(content) <= length:
return content
return content[: length - len(suffix)].rsplit(" ", 1)[0] + suffix
class SQLDatabase:
"""SQLAlchemy wrapper around a database."""
def __init__(
self,
engine: Engine,
schema: Optional[str] = None,
metadata: Optional[MetaData] = None,
ignore_tables: Optional[List[str]] = None,
include_tables: Optional[List[str]] = None,
sample_rows_in_table_info: int = 3,
indexes_in_table_info: bool = False,
custom_table_info: Optional[dict] = None,
view_support: bool = False,
max_string_length: int = 300,
lazy_table_reflection: bool = False,
):
"""Create engine from database URI."""
self._engine = engine
self._schema = schema
if include_tables and ignore_tables:
raise ValueError("Cannot specify both include_tables and ignore_tables")
self._inspector = inspect(self._engine)
# including view support by adding the views as well as tables to the all
# tables list if view_support is True
self._all_tables = set(
self._inspector.get_table_names(schema=schema)
+ (self._inspector.get_view_names(schema=schema) if view_support else [])
)
self._include_tables = set(include_tables) if include_tables else set()
if self._include_tables:
missing_tables = self._include_tables - self._all_tables
if missing_tables:
raise ValueError(
f"include_tables {missing_tables} not found in database"
)
self._ignore_tables = set(ignore_tables) if ignore_tables else set()
if self._ignore_tables:
missing_tables = self._ignore_tables - self._all_tables
if missing_tables:
raise ValueError(
f"ignore_tables {missing_tables} not found in database"
)
usable_tables = self.get_usable_table_names()
self._usable_tables = set(usable_tables) if usable_tables else self._all_tables
if not isinstance(sample_rows_in_table_info, int):
raise TypeError("sample_rows_in_table_info must be an integer")
self._sample_rows_in_table_info = sample_rows_in_table_info
self._indexes_in_table_info = indexes_in_table_info
self._custom_table_info = custom_table_info
if self._custom_table_info:
if not isinstance(self._custom_table_info, dict):
raise TypeError(
"table_info must be a dictionary with table names as keys and the "
"desired table info as values"
)
# only keep the tables that are also present in the database
intersection = set(self._custom_table_info).intersection(self._all_tables)
self._custom_table_info = dict(
(table, self._custom_table_info[table])
for table in self._custom_table_info
if table in intersection
)
self._max_string_length = max_string_length
self._view_support = view_support
self._metadata = metadata or MetaData()
if not lazy_table_reflection:
# including view support if view_support = true
self._metadata.reflect(
views=view_support,
bind=self._engine,
only=list(self._usable_tables),
schema=self._schema,
)
@classmethod
def from_uri(
cls,
database_uri: Union[str, URL],
engine_args: Optional[dict] = None,
**kwargs: Any,
) -> SQLDatabase:
"""Construct a SQLAlchemy engine from URI."""
_engine_args = engine_args or {}
return cls(create_engine(database_uri, **_engine_args), **kwargs)
@classmethod
def from_databricks(
cls,
catalog: str,
schema: str,
host: Optional[str] = None,
api_token: Optional[str] = None,
warehouse_id: Optional[str] = None,
cluster_id: Optional[str] = None,
engine_args: Optional[dict] = None,
**kwargs: Any,
) -> SQLDatabase:
"""
Class method to create an SQLDatabase instance from a Databricks connection.
This method requires the 'databricks-sql-connector' package. If not installed,
it can be added using `pip install databricks-sql-connector`.
Args:
catalog (str): The catalog name in the Databricks database.
schema (str): The schema name in the catalog.
host (Optional[str]): The Databricks workspace hostname, excluding
'https://' part. If not provided, it attempts to fetch from the
environment variable 'DATABRICKS_HOST'. If still unavailable and if
running in a Databricks notebook, it defaults to the current workspace
hostname. Defaults to None.
api_token (Optional[str]): The Databricks personal access token for
accessing the Databricks SQL warehouse or the cluster. If not provided,
it attempts to fetch from 'DATABRICKS_TOKEN'. If still unavailable
and running in a Databricks notebook, a temporary token for the current
user is generated. Defaults to None.
warehouse_id (Optional[str]): The warehouse ID in the Databricks SQL. If
provided, the method configures the connection to use this warehouse.
Cannot be used with 'cluster_id'. Defaults to None.
cluster_id (Optional[str]): The cluster ID in the Databricks Runtime. If
provided, the method configures the connection to use this cluster.
Cannot be used with 'warehouse_id'. If running in a Databricks notebook
and both 'warehouse_id' and 'cluster_id' are None, it uses the ID of the
cluster the notebook is attached to. Defaults to None.
engine_args (Optional[dict]): The arguments to be used when connecting
Databricks. Defaults to None.
**kwargs (Any): Additional keyword arguments for the `from_uri` method.
Returns:
SQLDatabase: An instance of SQLDatabase configured with the provided
Databricks connection details.
Raises:
ValueError: If 'databricks-sql-connector' is not found, or if both
'warehouse_id' and 'cluster_id' are provided, or if neither
'warehouse_id' nor 'cluster_id' are provided and it's not executing
inside a Databricks notebook.
"""
try:
from databricks import sql # noqa: F401
except ImportError:
raise ImportError(
"databricks-sql-connector package not found, please install with"
" `pip install databricks-sql-connector`"
)
context = None
try:
from dbruntime.databricks_repl_context import get_context
context = get_context()
default_host = context.browserHostName
except (ImportError, AttributeError):
default_host = None
if host is None:
host = get_from_env("host", "DATABRICKS_HOST", default_host)
default_api_token = context.apiToken if context else None
if api_token is None:
api_token = get_from_env("api_token", "DATABRICKS_TOKEN", default_api_token)
if warehouse_id is None and cluster_id is None:
if context:
cluster_id = context.clusterId
else:
raise ValueError(
"Need to provide either 'warehouse_id' or 'cluster_id'."
)
if warehouse_id and cluster_id:
raise ValueError("Can't have both 'warehouse_id' or 'cluster_id'.")
if warehouse_id:
http_path = f"/sql/1.0/warehouses/{warehouse_id}"
else:
http_path = f"/sql/protocolv1/o/0/{cluster_id}"
uri = (
f"databricks://token:{api_token}@{host}?"
f"http_path={http_path}&catalog={catalog}&schema={schema}"
)
return cls.from_uri(database_uri=uri, engine_args=engine_args, **kwargs)
| |
159019
|
import logging
from typing import Any
logger = logging.getLogger(__name__)
def __getattr__(name: str) -> Any:
if name in "PythonREPL":
raise AssertionError(
"PythonREPL has been deprecated from langchain_community due to being "
"flagged by security scanners. See: "
"https://github.com/langchain-ai/langchain/issues/14345 "
"If you need to use it, please use the version "
"from langchain_experimental. "
"from langchain_experimental.utilities.python import PythonREPL."
)
raise AttributeError(f"module {__name__} has no attribute {name}")
| |
159055
|
async def adelete_by_metadata_filter(
self,
filter: dict[str, Any],
*,
batch_size: int = 50,
) -> int:
"""Delete all documents matching a certain metadata filtering condition.
This operation does not use the vector embeddings in any way, it simply
removes all documents whose metadata match the provided condition.
Args:
filter: Filter on the metadata to apply. The filter cannot be empty.
batch_size: amount of deletions per each batch (until exhaustion of
the matching documents).
Returns:
A number expressing the amount of deleted documents.
"""
if not filter:
msg = (
"Method `delete_by_metadata_filter` does not accept an empty "
"filter. Use the `clear()` method if you really want to empty "
"the vector store."
)
raise ValueError(msg)
return await self.table.afind_and_delete_entries(
metadata=filter,
batch_size=batch_size,
)
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
batch_size: int = 16,
ttl_seconds: Optional[int] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Texts to add to the vectorstore.
metadatas: Optional list of metadatas.
ids: Optional list of IDs.
batch_size: Number of concurrent requests to send to the server.
ttl_seconds: Optional time-to-live for the added texts.
Returns:
List[str]: List of IDs of the added texts.
"""
_texts = list(texts)
ids = ids or [uuid.uuid4().hex for _ in _texts]
metadatas = metadatas or [{}] * len(_texts)
ttl_seconds = ttl_seconds or self.ttl_seconds
embedding_vectors = self.embedding.embed_documents(_texts)
for i in range(0, len(_texts), batch_size):
batch_texts = _texts[i : i + batch_size]
batch_embedding_vectors = embedding_vectors[i : i + batch_size]
batch_ids = ids[i : i + batch_size]
batch_metadatas = metadatas[i : i + batch_size]
futures = [
self.table.put_async(
row_id=text_id,
body_blob=text,
vector=embedding_vector,
metadata=metadata or {},
ttl_seconds=ttl_seconds,
)
for text, embedding_vector, text_id, metadata in zip(
batch_texts, batch_embedding_vectors, batch_ids, batch_metadatas
)
]
for future in futures:
future.result()
return ids
async def aadd_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
concurrency: int = 16,
ttl_seconds: Optional[int] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Texts to add to the vectorstore.
metadatas: Optional list of metadatas.
ids: Optional list of IDs.
concurrency: Number of concurrent queries to the database.
Defaults to 16.
ttl_seconds: Optional time-to-live for the added texts.
Returns:
List[str]: List of IDs of the added texts.
"""
_texts = list(texts)
ids = ids or [uuid.uuid4().hex for _ in _texts]
_metadatas: List[dict] = metadatas or [{}] * len(_texts)
ttl_seconds = ttl_seconds or self.ttl_seconds
embedding_vectors = await self.embedding.aembed_documents(_texts)
sem = asyncio.Semaphore(concurrency)
async def send_concurrently(
row_id: str, text: str, embedding_vector: List[float], metadata: dict
) -> None:
async with sem:
await self.table.aput(
row_id=row_id,
body_blob=text,
vector=embedding_vector,
metadata=metadata or {},
ttl_seconds=ttl_seconds,
)
for i in range(0, len(_texts)):
tasks = [
asyncio.create_task(
send_concurrently(
ids[i], _texts[i], embedding_vectors[i], _metadatas[i]
)
)
]
await asyncio.gather(*tasks)
return ids
def replace_metadata(
self,
id_to_metadata: dict[str, dict],
*,
batch_size: int = 50,
) -> None:
"""Replace the metadata of documents.
For each document to update, identified by its ID, the new metadata
dictionary completely replaces what is on the store. This includes
passing empty metadata `{}` to erase the currently-stored information.
Args:
id_to_metadata: map from the Document IDs to modify to the
new metadata for updating.
Keys in this dictionary that do not correspond to an existing
document will not cause an error, rather will result in new
rows being written into the Cassandra table but without an
associated vector: hence unreachable through vector search.
batch_size: Number of concurrent requests to send to the server.
Returns:
None if the writes succeed (otherwise an error is raised).
"""
ids_and_metadatas = list(id_to_metadata.items())
for i in range(0, len(ids_and_metadatas), batch_size):
batch_i_m = ids_and_metadatas[i : i + batch_size]
futures = [
self.table.put_async(
row_id=doc_id,
metadata=doc_md,
)
for doc_id, doc_md in batch_i_m
]
for future in futures:
future.result()
return
async def areplace_metadata(
self,
id_to_metadata: dict[str, dict],
*,
concurrency: int = 50,
) -> None:
"""Replace the metadata of documents.
For each document to update, identified by its ID, the new metadata
dictionary completely replaces what is on the store. This includes
passing empty metadata `{}` to erase the currently-stored information.
Args:
id_to_metadata: map from the Document IDs to modify to the
new metadata for updating.
Keys in this dictionary that do not correspond to an existing
document will not cause an error, rather will result in new
rows being written into the Cassandra table but without an
associated vector: hence unreachable through vector search.
concurrency: Number of concurrent queries to the database.
Defaults to 50.
Returns:
None if the writes succeed (otherwise an error is raised).
"""
ids_and_metadatas = list(id_to_metadata.items())
sem = asyncio.Semaphore(concurrency)
async def send_concurrently(doc_id: str, doc_md: dict) -> None:
async with sem:
await self.table.aput(
row_id=doc_id,
metadata=doc_md,
)
for doc_id, doc_md in ids_and_metadatas:
tasks = [asyncio.create_task(send_concurrently(doc_id, doc_md))]
await asyncio.gather(*tasks)
return
@staticmethod
def _row_to_document(row: Dict[str, Any]) -> Document:
return Document(
id=row["row_id"],
page_content=row["body_blob"],
metadata=row["metadata"],
)
def get_by_document_id(self, document_id: str) -> Document | None:
"""Get by document ID.
Args:
document_id: the document ID to get.
"""
row = self.table.get(row_id=document_id)
if row is None:
return None
return self._row_to_document(row=row)
async def aget_by_document_id(self, document_id: str) -> Document | None:
"""Get by document ID.
Args:
document_id: the document ID to get.
"""
row = await self.table.aget(row_id=document_id)
if row is None:
return None
return self._row_to_document(row=row)
def metadata_search(
self,
metadata: dict[str, Any] = {}, # noqa: B006
n: int = 5,
) -> Iterable[Document]:
"""Get documents via a metadata search.
Args:
metadata: the metadata to query for.
"""
rows = self.table.find_entries(metadata=metadata, n=n)
return [self._row_to_document(row=row) for row in rows if row]
async def ametadata_search(
self,
metadata: dict[str, Any] = {}, # noqa: B006
n: int = 5,
) -> Iterable[Document]:
"""Get documents via a metadata search.
Args:
metadata: the metadata to query for.
"""
rows = await self.table.afind_entries(metadata=metadata, n=n)
return [self._row_to_document(row=row) for row in rows]
| |
159065
|
from __future__ import annotations
import asyncio
import base64
import itertools
import json
import logging
import time
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
ClassVar,
Collection,
Dict,
Iterable,
List,
Literal,
Optional,
Tuple,
Type,
Union,
cast,
)
import numpy as np
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.exceptions import LangChainException
from langchain_core.retrievers import BaseRetriever
from langchain_core.utils import get_from_env
from langchain_core.vectorstores import VectorStore
from pydantic import ConfigDict, model_validator
from langchain_community.vectorstores.utils import maximal_marginal_relevance
logger = logging.getLogger()
if TYPE_CHECKING:
from azure.search.documents import SearchClient, SearchItemPaged
from azure.search.documents.aio import (
AsyncSearchItemPaged,
)
from azure.search.documents.aio import (
SearchClient as AsyncSearchClient,
)
from azure.search.documents.indexes.models import (
CorsOptions,
ScoringProfile,
SearchField,
SemanticConfiguration,
VectorSearch,
)
# Allow overriding field names for Azure Search
FIELDS_ID = get_from_env(
key="AZURESEARCH_FIELDS_ID", env_key="AZURESEARCH_FIELDS_ID", default="id"
)
FIELDS_CONTENT = get_from_env(
key="AZURESEARCH_FIELDS_CONTENT",
env_key="AZURESEARCH_FIELDS_CONTENT",
default="content",
)
FIELDS_CONTENT_VECTOR = get_from_env(
key="AZURESEARCH_FIELDS_CONTENT_VECTOR",
env_key="AZURESEARCH_FIELDS_CONTENT_VECTOR",
default="content_vector",
)
FIELDS_METADATA = get_from_env(
key="AZURESEARCH_FIELDS_TAG", env_key="AZURESEARCH_FIELDS_TAG", default="metadata"
)
MAX_UPLOAD_BATCH_SIZE = 1000
def _get_search_client(
endpoint: str,
index_name: str,
key: Optional[str] = None,
azure_ad_access_token: Optional[str] = None,
semantic_configuration_name: Optional[str] = None,
fields: Optional[List[SearchField]] = None,
vector_search: Optional[VectorSearch] = None,
semantic_configurations: Optional[
Union[SemanticConfiguration, List[SemanticConfiguration]]
] = None,
scoring_profiles: Optional[List[ScoringProfile]] = None,
default_scoring_profile: Optional[str] = None,
default_fields: Optional[List[SearchField]] = None,
user_agent: Optional[str] = "langchain",
cors_options: Optional[CorsOptions] = None,
async_: bool = False,
additional_search_client_options: Optional[Dict[str, Any]] = None,
) -> Union[SearchClient, AsyncSearchClient]:
from azure.core.credentials import AccessToken, AzureKeyCredential, TokenCredential
from azure.core.exceptions import ResourceNotFoundError
from azure.identity import DefaultAzureCredential, InteractiveBrowserCredential
from azure.search.documents import SearchClient
from azure.search.documents.aio import SearchClient as AsyncSearchClient
from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents.indexes.models import (
ExhaustiveKnnAlgorithmConfiguration,
ExhaustiveKnnParameters,
HnswAlgorithmConfiguration,
HnswParameters,
SearchIndex,
SemanticConfiguration,
SemanticField,
SemanticPrioritizedFields,
SemanticSearch,
VectorSearch,
VectorSearchAlgorithmKind,
VectorSearchAlgorithmMetric,
VectorSearchProfile,
)
additional_search_client_options = additional_search_client_options or {}
default_fields = default_fields or []
credential: Union[AzureKeyCredential, TokenCredential, InteractiveBrowserCredential]
# Determine the appropriate credential to use
if key is not None:
if key.upper() == "INTERACTIVE":
credential = InteractiveBrowserCredential()
credential.get_token("https://search.azure.com/.default")
else:
credential = AzureKeyCredential(key)
elif azure_ad_access_token is not None:
credential = TokenCredential(
lambda *scopes, **kwargs: AccessToken(
azure_ad_access_token, int(time.time()) + 3600
)
)
else:
credential = DefaultAzureCredential()
index_client: SearchIndexClient = SearchIndexClient(
endpoint=endpoint, credential=credential, user_agent=user_agent
)
try:
index_client.get_index(name=index_name)
except ResourceNotFoundError:
# Fields configuration
if fields is not None:
# Check mandatory fields
fields_types = {f.name: f.type for f in fields}
mandatory_fields = {df.name: df.type for df in default_fields}
# Check for missing keys
missing_fields = {
key: mandatory_fields[key]
for key, value in set(mandatory_fields.items())
- set(fields_types.items())
}
if len(missing_fields) > 0:
# Helper for formatting field information for each missing field.
def fmt_err(x: str) -> str:
return (
f"{x} current type: '{fields_types.get(x, 'MISSING')}'. "
f"It has to be '{mandatory_fields.get(x)}' or you can point "
f"to a different '{mandatory_fields.get(x)}' field name by "
f"using the env variable 'AZURESEARCH_FIELDS_{x.upper()}'"
)
error = "\n".join([fmt_err(x) for x in missing_fields])
raise ValueError(
f"You need to specify at least the following fields "
f"{missing_fields} or provide alternative field names in the env "
f"variables.\n\n{error}"
)
else:
fields = default_fields
# Vector search configuration
if vector_search is None:
vector_search = VectorSearch(
algorithms=[
HnswAlgorithmConfiguration(
name="default",
kind=VectorSearchAlgorithmKind.HNSW,
parameters=HnswParameters(
m=4,
ef_construction=400,
ef_search=500,
metric=VectorSearchAlgorithmMetric.COSINE,
),
),
ExhaustiveKnnAlgorithmConfiguration(
name="default_exhaustive_knn",
kind=VectorSearchAlgorithmKind.EXHAUSTIVE_KNN,
parameters=ExhaustiveKnnParameters(
metric=VectorSearchAlgorithmMetric.COSINE
),
),
],
profiles=[
VectorSearchProfile(
name="myHnswProfile",
algorithm_configuration_name="default",
),
VectorSearchProfile(
name="myExhaustiveKnnProfile",
algorithm_configuration_name="default_exhaustive_knn",
),
],
)
# Create the semantic settings with the configuration
if semantic_configurations:
if not isinstance(semantic_configurations, list):
semantic_configurations = [semantic_configurations]
semantic_search = SemanticSearch(
configurations=semantic_configurations,
default_configuration_name=semantic_configuration_name,
)
elif semantic_configuration_name:
# use default semantic configuration
semantic_configuration = SemanticConfiguration(
name=semantic_configuration_name,
prioritized_fields=SemanticPrioritizedFields(
content_fields=[SemanticField(field_name=FIELDS_CONTENT)],
),
)
semantic_search = SemanticSearch(configurations=[semantic_configuration])
else:
# don't use semantic search
semantic_search = None
# Create the search index with the semantic settings and vector search
index = SearchIndex(
name=index_name,
fields=fields,
vector_search=vector_search,
semantic_search=semantic_search,
scoring_profiles=scoring_profiles,
default_scoring_profile=default_scoring_profile,
cors_options=cors_options,
)
index_client.create_index(index)
# Create the search client
if not async_:
return SearchClient(
endpoint=endpoint,
index_name=index_name,
credential=credential,
user_agent=user_agent,
**additional_search_client_options,
)
else:
return AsyncSearchClient(
endpoint=endpoint,
index_name=index_name,
credential=credential,
user_agent=user_agent,
**additional_search_client_options,
)
| |
159066
|
class AzureSearch(VectorStore):
"""`Azure Cognitive Search` vector store."""
def __init__(
self,
azure_search_endpoint: str,
azure_search_key: str,
index_name: str,
embedding_function: Union[Callable, Embeddings],
search_type: str = "hybrid",
semantic_configuration_name: Optional[str] = None,
fields: Optional[List[SearchField]] = None,
vector_search: Optional[VectorSearch] = None,
semantic_configurations: Optional[
Union[SemanticConfiguration, List[SemanticConfiguration]]
] = None,
scoring_profiles: Optional[List[ScoringProfile]] = None,
default_scoring_profile: Optional[str] = None,
cors_options: Optional[CorsOptions] = None,
*,
vector_search_dimensions: Optional[int] = None,
additional_search_client_options: Optional[Dict[str, Any]] = None,
azure_ad_access_token: Optional[str] = None,
**kwargs: Any,
):
try:
from azure.search.documents.indexes.models import (
SearchableField,
SearchField,
SearchFieldDataType,
SimpleField,
)
except ImportError as e:
raise ImportError(
"Unable to import azure.search.documents. Please install with "
"`pip install -U azure-search-documents`."
) from e
"""Initialize with necessary components."""
# Initialize base class
self.embedding_function = embedding_function
if isinstance(self.embedding_function, Embeddings):
self.embed_query = self.embedding_function.embed_query
else:
self.embed_query = self.embedding_function
default_fields = [
SimpleField(
name=FIELDS_ID,
type=SearchFieldDataType.String,
key=True,
filterable=True,
),
SearchableField(
name=FIELDS_CONTENT,
type=SearchFieldDataType.String,
),
SearchField(
name=FIELDS_CONTENT_VECTOR,
type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
searchable=True,
vector_search_dimensions=vector_search_dimensions
or len(self.embed_query("Text")),
vector_search_profile_name="myHnswProfile",
),
SearchableField(
name=FIELDS_METADATA,
type=SearchFieldDataType.String,
),
]
user_agent = "langchain"
if "user_agent" in kwargs and kwargs["user_agent"]:
user_agent += " " + kwargs["user_agent"]
self.client = _get_search_client(
azure_search_endpoint,
index_name,
azure_search_key,
azure_ad_access_token,
semantic_configuration_name=semantic_configuration_name,
fields=fields,
vector_search=vector_search,
semantic_configurations=semantic_configurations,
scoring_profiles=scoring_profiles,
default_scoring_profile=default_scoring_profile,
default_fields=default_fields,
user_agent=user_agent,
cors_options=cors_options,
additional_search_client_options=additional_search_client_options,
)
self.async_client = _get_search_client(
azure_search_endpoint,
index_name,
azure_search_key,
azure_ad_access_token,
semantic_configuration_name=semantic_configuration_name,
fields=fields,
vector_search=vector_search,
semantic_configurations=semantic_configurations,
scoring_profiles=scoring_profiles,
default_scoring_profile=default_scoring_profile,
default_fields=default_fields,
user_agent=user_agent,
cors_options=cors_options,
async_=True,
)
self.search_type = search_type
self.semantic_configuration_name = semantic_configuration_name
self.fields = fields if fields else default_fields
self._azure_search_endpoint = azure_search_endpoint
self._azure_search_key = azure_search_key
self._index_name = index_name
self._semantic_configuration_name = semantic_configuration_name
self._fields = fields
self._vector_search = vector_search
self._semantic_configurations = semantic_configurations
self._scoring_profiles = scoring_profiles
self._default_scoring_profile = default_scoring_profile
self._default_fields = default_fields
self._user_agent = user_agent
self._cors_options = cors_options
def __del__(self) -> None:
# Close the sync client
if hasattr(self, "client") and self.client:
self.client.close()
# Close the async client
if hasattr(self, "async_client") and self.async_client:
# Check if we're in an existing event loop
try:
loop = asyncio.get_event_loop()
if loop.is_running():
# Schedule the coroutine to close the async client
loop.create_task(self.async_client.close())
else:
# If no event loop is running, run the coroutine directly
loop.run_until_complete(self.async_client.close())
except RuntimeError:
# Handle the case where there's no event loop
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
try:
loop.run_until_complete(self.async_client.close())
finally:
loop.close()
@property
def embeddings(self) -> Optional[Embeddings]:
# TODO: Support embedding object directly
return (
self.embedding_function
if isinstance(self.embedding_function, Embeddings)
else None
)
async def _aembed_query(self, text: str) -> List[float]:
if self.embeddings:
return await self.embeddings.aembed_query(text)
else:
return cast(Callable, self.embedding_function)(text)
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
*,
keys: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Add texts data to an existing index."""
# batching support if embedding function is an Embeddings object
if isinstance(self.embedding_function, Embeddings):
try:
embeddings = self.embedding_function.embed_documents(list(texts))
except NotImplementedError:
embeddings = [self.embedding_function.embed_query(x) for x in texts]
else:
embeddings = [self.embedding_function(x) for x in texts]
if len(embeddings) == 0:
logger.debug("Nothing to insert, skipping.")
return []
# when `keys` are not passed in and there is `ids` in kwargs, use those instead
# base class expects `ids` passed in rather than `keys`
# https://github.com/langchain-ai/langchain/blob/4cdaca67dc51dba887289f56c6fead3c1a52f97d/libs/core/langchain_core/vectorstores/base.py#L65
if (not keys) and ("ids" in kwargs) and (len(kwargs["ids"]) == len(embeddings)):
keys = kwargs["ids"]
return self.add_embeddings(zip(texts, embeddings), metadatas, keys=keys)
async def aadd_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
*,
keys: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
if isinstance(self.embedding_function, Embeddings):
try:
embeddings = await self.embedding_function.aembed_documents(list(texts))
except NotImplementedError:
embeddings = [
await self.embedding_function.aembed_query(x) for x in texts
]
else:
embeddings = [self.embedding_function(x) for x in texts]
if len(embeddings) == 0:
logger.debug("Nothing to insert, skipping.")
return []
# when `keys` are not passed in and there is `ids` in kwargs, use those instead
# base class expects `ids` passed in rather than `keys`
# https://github.com/langchain-ai/langchain/blob/4cdaca67dc51dba887289f56c6fead3c1a52f97d/libs/core/langchain_core/vectorstores/base.py#L65
if (not keys) and ("ids" in kwargs) and (len(kwargs["ids"]) == len(embeddings)):
keys = kwargs["ids"]
return await self.aadd_embeddings(zip(texts, embeddings), metadatas, keys=keys)
| |
159070
|
async def asemantic_hybrid_search_with_score(
self,
query: str,
k: int = 4,
score_type: Literal["score", "reranker_score"] = "score",
*,
score_threshold: Optional[float] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""
Returns the most similar indexed documents to the query text.
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
score_type: Must either be "score" or "reranker_score".
Defaulted to "score".
filters: Filtering expression.
Returns:
List[Tuple[Document, float]]: A list of documents and their
corresponding scores.
"""
docs_and_scores = await self.asemantic_hybrid_search_with_score_and_rerank(
query, k=k, **kwargs
)
if score_type == "score":
return [
(doc, score)
for doc, score, _ in docs_and_scores
if score_threshold is None or score >= score_threshold
]
elif score_type == "reranker_score":
return [
(doc, reranker_score)
for doc, _, reranker_score in docs_and_scores
if score_threshold is None or reranker_score >= score_threshold
]
def semantic_hybrid_search_with_score_and_rerank(
self, query: str, k: int = 4, *, filters: Optional[str] = None, **kwargs: Any
) -> List[Tuple[Document, float, float]]:
"""Return docs most similar to query with a hybrid query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filters: Filtering expression.
Returns:
List of Documents most similar to the query and score for each
"""
from azure.search.documents.models import VectorizedQuery
results = self.client.search(
search_text=query,
vector_queries=[
VectorizedQuery(
vector=np.array(self.embed_query(query), dtype=np.float32).tolist(),
k_nearest_neighbors=k,
fields=FIELDS_CONTENT_VECTOR,
)
],
filter=filters,
query_type="semantic",
semantic_configuration_name=self.semantic_configuration_name,
query_caption="extractive",
query_answer="extractive",
top=k,
**kwargs,
)
# Get Semantic Answers
semantic_answers = results.get_answers() or []
semantic_answers_dict: Dict = {}
for semantic_answer in semantic_answers:
semantic_answers_dict[semantic_answer.key] = {
"text": semantic_answer.text,
"highlights": semantic_answer.highlights,
}
# Convert results to Document objects
docs = [
(
Document(
page_content=result.pop(FIELDS_CONTENT),
metadata={
**(
{FIELDS_ID: result.pop(FIELDS_ID)}
if FIELDS_ID in result
else {}
),
**(
json.loads(result[FIELDS_METADATA])
if FIELDS_METADATA in result
else {
k: v
for k, v in result.items()
if k != FIELDS_CONTENT_VECTOR
}
),
**{
"captions": (
{
"text": result.get("@search.captions", [{}])[
0
].text,
"highlights": result.get("@search.captions", [{}])[
0
].highlights,
}
if result.get("@search.captions")
else {}
),
"answers": semantic_answers_dict.get(
result.get(FIELDS_ID, ""),
"",
),
},
},
),
float(result["@search.score"]),
float(result["@search.reranker_score"]),
)
for result in results
]
return docs
async def asemantic_hybrid_search_with_score_and_rerank(
self, query: str, k: int = 4, *, filters: Optional[str] = None, **kwargs: Any
) -> List[Tuple[Document, float, float]]:
"""Return docs most similar to query with a hybrid query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filters: Filtering expression.
Returns:
List of Documents most similar to the query and score for each
"""
from azure.search.documents.models import VectorizedQuery
vector = await self._aembed_query(query)
results = await self.async_client.search(
search_text=query,
vector_queries=[
VectorizedQuery(
vector=np.array(vector, dtype=np.float32).tolist(),
k_nearest_neighbors=k,
fields=FIELDS_CONTENT_VECTOR,
)
],
filter=filters,
query_type="semantic",
semantic_configuration_name=self.semantic_configuration_name,
query_caption="extractive",
query_answer="extractive",
top=k,
**kwargs,
)
# Get Semantic Answers
semantic_answers = (await results.get_answers()) or []
semantic_answers_dict: Dict = {}
for semantic_answer in semantic_answers:
semantic_answers_dict[semantic_answer.key] = {
"text": semantic_answer.text,
"highlights": semantic_answer.highlights,
}
# Convert results to Document objects
docs = [
(
Document(
page_content=result.pop(FIELDS_CONTENT),
metadata={
**(
{FIELDS_ID: result.pop(FIELDS_ID)}
if FIELDS_ID in result
else {}
),
**(
json.loads(result[FIELDS_METADATA])
if FIELDS_METADATA in result
else {
k: v
for k, v in result.items()
if k != FIELDS_CONTENT_VECTOR
}
),
**{
"captions": (
{
"text": result.get("@search.captions", [{}])[
0
].text,
"highlights": result.get("@search.captions", [{}])[
0
].highlights,
}
if result.get("@search.captions")
else {}
),
"answers": semantic_answers_dict.get(
result.get(FIELDS_ID, ""),
"",
),
},
},
),
float(result["@search.score"]),
float(result["@search.reranker_score"]),
)
async for result in results
]
return docs
@classmethod
def from_texts(
cls: Type[AzureSearch],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
azure_search_endpoint: str = "",
azure_search_key: str = "",
azure_ad_access_token: Optional[str] = None,
index_name: str = "langchain-index",
fields: Optional[List[SearchField]] = None,
**kwargs: Any,
) -> AzureSearch:
# Creating a new Azure Search instance
azure_search = cls(
azure_search_endpoint,
azure_search_key,
index_name,
embedding,
fields=fields,
azure_ad_access_token=azure_ad_access_token,
**kwargs,
)
azure_search.add_texts(texts, metadatas, **kwargs)
return azure_search
@classmethod
async def afrom_texts(
cls: Type[AzureSearch],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
azure_search_endpoint: str = "",
azure_search_key: str = "",
azure_ad_access_token: Optional[str] = None,
index_name: str = "langchain-index",
fields: Optional[List[SearchField]] = None,
**kwargs: Any,
) -> AzureSearch:
# Creating a new Azure Search instance
azure_search = cls(
azure_search_endpoint,
azure_search_key,
index_name,
embedding,
fields=fields,
azure_ad_access_token=azure_ad_access_token,
**kwargs,
)
await azure_search.aadd_texts(texts, metadatas, **kwargs)
return azure_search
| |
159084
|
class Aerospike(VectorStore):
"""`Aerospike` vector store.
To use, you should have the ``aerospike_vector_search`` python package installed.
"""
def __init__(
self,
client: Client,
embedding: Union[Embeddings, Callable],
namespace: str,
index_name: Optional[str] = None,
vector_key: str = "_vector",
text_key: str = "_text",
id_key: str = "_id",
set_name: Optional[str] = None,
distance_strategy: Optional[
Union[DistanceStrategy, VectorDistanceMetric]
] = DistanceStrategy.EUCLIDEAN_DISTANCE,
):
"""Initialize with Aerospike client.
Args:
client: Aerospike client.
embedding: Embeddings object or Callable (deprecated) to embed text.
namespace: Namespace to use for storing vectors. This should match
index_name: Name of the index previously created in Aerospike. This
vector_key: Key to use for vector in metadata. This should match the
key used during index creation.
text_key: Key to use for text in metadata.
id_key: Key to use for id in metadata.
set_name: Default set name to use for storing vectors.
distance_strategy: Distance strategy to use for similarity search
This should match the distance strategy used during index creation.
"""
aerospike = _import_aerospike()
if not isinstance(embedding, Embeddings):
warnings.warn(
"Passing in `embedding` as a Callable is deprecated. Please pass in an"
" Embeddings object instead."
)
if not isinstance(client, aerospike):
raise ValueError(
f"client should be an instance of aerospike_vector_search.Client, "
f"got {type(client)}"
)
self._client = client
self._embedding = embedding
self._text_key = text_key
self._vector_key = vector_key
self._id_key = id_key
self._index_name = index_name
self._namespace = namespace
self._set_name = set_name
self._distance_strategy = self.convert_distance_strategy(distance_strategy)
@property
def embeddings(self) -> Optional[Embeddings]:
"""Access the query embedding object if available."""
if isinstance(self._embedding, Embeddings):
return self._embedding
return None
def _embed_documents(self, texts: Iterable[str]) -> List[List[float]]:
"""Embed search docs."""
if isinstance(self._embedding, Embeddings):
return self._embedding.embed_documents(list(texts))
return [self._embedding(t) for t in texts]
def _embed_query(self, text: str) -> List[float]:
"""Embed query text."""
if isinstance(self._embedding, Embeddings):
return self._embedding.embed_query(text)
return self._embedding(text)
@staticmethod
def convert_distance_strategy(
distance_strategy: Union[VectorDistanceMetric, DistanceStrategy],
) -> DistanceStrategy:
"""
Convert Aerospikes distance strategy to langchains DistanceStrategy
enum. This is a convenience method to allow users to pass in the same
distance metric used to create the index.
"""
from aerospike_vector_search.types import VectorDistanceMetric
if isinstance(distance_strategy, DistanceStrategy):
return distance_strategy
if distance_strategy == VectorDistanceMetric.COSINE:
return DistanceStrategy.COSINE
if distance_strategy == VectorDistanceMetric.DOT_PRODUCT:
return DistanceStrategy.DOT_PRODUCT
if distance_strategy == VectorDistanceMetric.SQUARED_EUCLIDEAN:
return DistanceStrategy.EUCLIDEAN_DISTANCE
raise ValueError(
"Unknown distance strategy, must be cosine, dot_product" ", or euclidean"
)
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
set_name: Optional[str] = None,
embedding_chunk_size: int = 1000,
index_name: Optional[str] = None,
wait_for_index: bool = True,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of ids to associate with the texts.
set_name: Optional aerospike set name to add the texts to.
batch_size: Batch size to use when adding the texts to the vectorstore.
embedding_chunk_size: Chunk size to use when embedding the texts.
index_name: Optional aerospike index name used for waiting for index
completion. If not provided, the default index_name will be used.
wait_for_index: If True, wait for the all the texts to be indexed
before returning. Requires index_name to be provided. Defaults
to True.
kwargs: Additional keyword arguments to pass to the client upsert call.
Returns:
List of ids from adding the texts into the vectorstore.
"""
if set_name is None:
set_name = self._set_name
if index_name is None:
index_name = self._index_name
if wait_for_index and index_name is None:
raise ValueError("if wait_for_index is True, index_name must be provided")
texts = list(texts)
ids = ids or [str(uuid.uuid4()) for _ in texts]
# We need to shallow copy so that we can add the vector and text keys
if metadatas:
metadatas = [m.copy() for m in metadatas]
else:
metadatas = metadatas or [{} for _ in texts]
for i in range(0, len(texts), embedding_chunk_size):
chunk_texts = texts[i : i + embedding_chunk_size]
chunk_ids = ids[i : i + embedding_chunk_size]
chunk_metadatas = metadatas[i : i + embedding_chunk_size]
embeddings = self._embed_documents(chunk_texts)
for metadata, embedding, text in zip(
chunk_metadatas, embeddings, chunk_texts
):
metadata[self._vector_key] = embedding
metadata[self._text_key] = text
for id, metadata in zip(chunk_ids, chunk_metadatas):
metadata[self._id_key] = id
self._client.upsert(
namespace=self._namespace,
key=id,
set_name=set_name,
record_data=metadata,
**kwargs,
)
if wait_for_index:
self._client.wait_for_index_completion(
namespace=self._namespace,
name=index_name,
)
return ids
def delete(
self,
ids: Optional[List[str]] = None,
set_name: Optional[str] = None,
**kwargs: Any,
) -> Optional[bool]:
"""Delete by vector ID or other criteria.
Args:
ids: List of ids to delete.
**kwargs: Other keyword arguments to pass to client delete call.
Returns:
Optional[bool]: True if deletion is successful,
False otherwise, None if not implemented.
"""
from aerospike_vector_search import AVSServerError
if ids:
for id in ids:
try:
self._client.delete(
namespace=self._namespace,
key=id,
set_name=set_name,
**kwargs,
)
except AVSServerError:
return False
return True
def similarity_search_with_score(
self,
query: str,
k: int = 4,
metadata_keys: Optional[List[str]] = None,
index_name: Optional[str] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return aerospike documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
metadata_keys: List of metadata keys to return with the documents.
If None, all metadata keys will be returned. Defaults to None.
index_name: Name of the index to search. Overrides the default
index_name.
kwargs: Additional keyword arguments to pass to the search method.
Returns:
List of Documents most similar to the query and associated scores.
"""
return self.similarity_search_by_vector_with_score(
self._embed_query(query),
k=k,
metadata_keys=metadata_keys,
index_name=index_name,
**kwargs,
)
| |
159088
|
from __future__ import annotations
import logging
from typing import Any, Iterable, List, Optional, Tuple, Union
from uuid import uuid4
import numpy as np
from langchain_core._api.deprecation import deprecated
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore
from langchain_community.vectorstores.utils import maximal_marginal_relevance
logger = logging.getLogger(__name__)
DEFAULT_MILVUS_CONNECTION = {
"host": "localhost",
"port": "19530",
"user": "",
"password": "",
"secure": False,
}
@deprecated(
since="0.2.0",
removal="1.0",
alternative_import="langchain_milvus.MilvusVectorStore",
)
class Milvus(VectorStore):
"""`Milvus` vector store.
You need to install `pymilvus` and run Milvus.
See the following documentation for how to run a Milvus instance:
https://milvus.io/docs/install_standalone-docker.md
If looking for a hosted Milvus, take a look at this documentation:
https://zilliz.com/cloud and make use of the Zilliz vectorstore found in
this project.
IF USING L2/IP metric, IT IS HIGHLY SUGGESTED TO NORMALIZE YOUR DATA.
Args:
embedding_function (Embeddings): Function used to embed the text.
collection_name (str): Which Milvus collection to use. Defaults to
"LangChainCollection".
collection_description (str): The description of the collection. Defaults to
"".
collection_properties (Optional[dict[str, any]]): The collection properties.
Defaults to None.
If set, will override collection existing properties.
For example: {"collection.ttl.seconds": 60}.
connection_args (Optional[dict[str, any]]): The connection args used for
this class comes in the form of a dict.
consistency_level (str): The consistency level to use for a collection.
Defaults to "Session".
index_params (Optional[dict]): Which index params to use. Defaults to
HNSW/AUTOINDEX depending on service.
search_params (Optional[dict]): Which search params to use. Defaults to
default of index.
drop_old (Optional[bool]): Whether to drop the current collection. Defaults
to False.
auto_id (bool): Whether to enable auto id for primary key. Defaults to False.
If False, you needs to provide text ids (string less than 65535 bytes).
If True, Milvus will generate unique integers as primary keys.
primary_field (str): Name of the primary key field. Defaults to "pk".
text_field (str): Name of the text field. Defaults to "text".
vector_field (str): Name of the vector field. Defaults to "vector".
metadata_field (str): Name of the metadata field. Defaults to None.
When metadata_field is specified,
the document's metadata will store as json.
The connection args used for this class comes in the form of a dict,
here are a few of the options:
address (str): The actual address of Milvus
instance. Example address: "localhost:19530"
uri (str): The uri of Milvus instance. Example uri:
"http://randomwebsite:19530",
"tcp:foobarsite:19530",
"https://ok.s3.south.com:19530".
host (str): The host of Milvus instance. Default at "localhost",
PyMilvus will fill in the default host if only port is provided.
port (str/int): The port of Milvus instance. Default at 19530, PyMilvus
will fill in the default port if only host is provided.
user (str): Use which user to connect to Milvus instance. If user and
password are provided, we will add related header in every RPC call.
password (str): Required when user is provided. The password
corresponding to the user.
secure (bool): Default is false. If set to true, tls will be enabled.
client_key_path (str): If use tls two-way authentication, need to
write the client.key path.
client_pem_path (str): If use tls two-way authentication, need to
write the client.pem path.
ca_pem_path (str): If use tls two-way authentication, need to write
the ca.pem path.
server_pem_path (str): If use tls one-way authentication, need to
write the server.pem path.
server_name (str): If use tls, need to write the common name.
Example:
.. code-block:: python
from langchain_community.vectorstores import Milvus
from langchain_community.embeddings import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
# Connect to a milvus instance on localhost
milvus_store = Milvus(
embedding_function = Embeddings,
collection_name = "LangChainCollection",
drop_old = True,
auto_id = True
)
Raises:
ValueError: If the pymilvus python package is not installed.
"""
| |
159089
|
def __init__(
self,
embedding_function: Embeddings,
collection_name: str = "LangChainCollection",
collection_description: str = "",
collection_properties: Optional[dict[str, Any]] = None,
connection_args: Optional[dict[str, Any]] = None,
consistency_level: str = "Session",
index_params: Optional[dict] = None,
search_params: Optional[dict] = None,
drop_old: Optional[bool] = False,
auto_id: bool = False,
*,
primary_field: str = "pk",
text_field: str = "text",
vector_field: str = "vector",
metadata_field: Optional[str] = None,
partition_key_field: Optional[str] = None,
partition_names: Optional[list] = None,
replica_number: int = 1,
timeout: Optional[float] = None,
num_shards: Optional[int] = None,
):
"""Initialize the Milvus vector store."""
try:
from pymilvus import Collection, utility
except ImportError:
raise ImportError(
"Could not import pymilvus python package. "
"Please install it with `pip install pymilvus`."
)
# Default search params when one is not provided.
self.default_search_params = {
"IVF_FLAT": {"metric_type": "L2", "params": {"nprobe": 10}},
"IVF_SQ8": {"metric_type": "L2", "params": {"nprobe": 10}},
"IVF_PQ": {"metric_type": "L2", "params": {"nprobe": 10}},
"HNSW": {"metric_type": "L2", "params": {"ef": 10}},
"RHNSW_FLAT": {"metric_type": "L2", "params": {"ef": 10}},
"RHNSW_SQ": {"metric_type": "L2", "params": {"ef": 10}},
"RHNSW_PQ": {"metric_type": "L2", "params": {"ef": 10}},
"IVF_HNSW": {"metric_type": "L2", "params": {"nprobe": 10, "ef": 10}},
"ANNOY": {"metric_type": "L2", "params": {"search_k": 10}},
"SCANN": {"metric_type": "L2", "params": {"search_k": 10}},
"AUTOINDEX": {"metric_type": "L2", "params": {}},
"GPU_CAGRA": {
"metric_type": "L2",
"params": {
"itopk_size": 128,
"search_width": 4,
"min_iterations": 0,
"max_iterations": 0,
"team_size": 0,
},
},
"GPU_IVF_FLAT": {"metric_type": "L2", "params": {"nprobe": 10}},
"GPU_IVF_PQ": {"metric_type": "L2", "params": {"nprobe": 10}},
}
self.embedding_func = embedding_function
self.collection_name = collection_name
self.collection_description = collection_description
self.collection_properties = collection_properties
self.index_params = index_params
self.search_params = search_params
self.consistency_level = consistency_level
self.auto_id = auto_id
# In order for a collection to be compatible, pk needs to be varchar
self._primary_field = primary_field
# In order for compatibility, the text field will need to be called "text"
self._text_field = text_field
# In order for compatibility, the vector field needs to be called "vector"
self._vector_field = vector_field
self._metadata_field = metadata_field
self._partition_key_field = partition_key_field
self.fields: list[str] = []
self.partition_names = partition_names
self.replica_number = replica_number
self.timeout = timeout
self.num_shards = num_shards
# Create the connection to the server
if connection_args is None:
connection_args = DEFAULT_MILVUS_CONNECTION
self.alias = self._create_connection_alias(connection_args)
self.col: Optional[Collection] = None
# Grab the existing collection if it exists
if utility.has_collection(self.collection_name, using=self.alias):
self.col = Collection(
self.collection_name,
using=self.alias,
)
if self.collection_properties is not None:
self.col.set_properties(self.collection_properties)
# If need to drop old, drop it
if drop_old and isinstance(self.col, Collection):
self.col.drop()
self.col = None
# Initialize the vector store
self._init(
partition_names=partition_names,
replica_number=replica_number,
timeout=timeout,
)
@property
def embeddings(self) -> Embeddings:
return self.embedding_func
def _create_connection_alias(self, connection_args: dict) -> str:
"""Create the connection to the Milvus server."""
from pymilvus import MilvusException, connections
# Grab the connection arguments that are used for checking existing connection
host: str = connection_args.get("host", None)
port: Union[str, int] = connection_args.get("port", None)
address: str = connection_args.get("address", None)
uri: str = connection_args.get("uri", None)
user = connection_args.get("user", None)
# Order of use is host/port, uri, address
if host is not None and port is not None:
given_address = str(host) + ":" + str(port)
elif uri is not None:
if uri.startswith("https://"):
given_address = uri.split("https://")[1]
elif uri.startswith("http://"):
given_address = uri.split("http://")[1]
else:
logger.error("Invalid Milvus URI: %s", uri)
raise ValueError("Invalid Milvus URI: %s", uri)
elif address is not None:
given_address = address
else:
given_address = None
logger.debug("Missing standard address type for reuse attempt")
# User defaults to empty string when getting connection info
if user is not None:
tmp_user = user
else:
tmp_user = ""
# If a valid address was given, then check if a connection exists
if given_address is not None:
for con in connections.list_connections():
addr = connections.get_connection_addr(con[0])
if (
con[1]
and ("address" in addr)
and (addr["address"] == given_address)
and ("user" in addr)
and (addr["user"] == tmp_user)
):
logger.debug("Using previous connection: %s", con[0])
return con[0]
# Generate a new connection if one doesn't exist
alias = uuid4().hex
try:
connections.connect(alias=alias, **connection_args)
logger.debug("Created new connection using: %s", alias)
return alias
except MilvusException as e:
logger.error("Failed to create new connection using: %s", alias)
raise e
def _init(
self,
embeddings: Optional[list] = None,
metadatas: Optional[list[dict]] = None,
partition_names: Optional[list] = None,
replica_number: int = 1,
timeout: Optional[float] = None,
) -> None:
if embeddings is not None:
self._create_collection(embeddings, metadatas)
self._extract_fields()
self._create_index()
self._create_search_params()
self._load(
partition_names=partition_names,
replica_number=replica_number,
timeout=timeout,
)
| |
159090
|
def _create_collection(
self, embeddings: list, metadatas: Optional[list[dict]] = None
) -> None:
from pymilvus import (
Collection,
CollectionSchema,
DataType,
FieldSchema,
MilvusException,
)
from pymilvus.orm.types import infer_dtype_bydata
# Determine embedding dim
dim = len(embeddings[0])
fields = []
if self._metadata_field is not None:
fields.append(FieldSchema(self._metadata_field, DataType.JSON))
else:
# Determine metadata schema
if metadatas:
# Create FieldSchema for each entry in metadata.
for key, value in metadatas[0].items():
# Infer the corresponding datatype of the metadata
dtype = infer_dtype_bydata(value)
# Datatype isn't compatible
if dtype == DataType.UNKNOWN or dtype == DataType.NONE:
logger.error(
(
"Failure to create collection, "
"unrecognized dtype for key: %s"
),
key,
)
raise ValueError(f"Unrecognized datatype for {key}.")
# Dataype is a string/varchar equivalent
elif dtype == DataType.VARCHAR:
fields.append(
FieldSchema(key, DataType.VARCHAR, max_length=65_535)
)
else:
fields.append(FieldSchema(key, dtype))
# Create the text field
fields.append(
FieldSchema(self._text_field, DataType.VARCHAR, max_length=65_535)
)
# Create the primary key field
if self.auto_id:
fields.append(
FieldSchema(
self._primary_field, DataType.INT64, is_primary=True, auto_id=True
)
)
else:
fields.append(
FieldSchema(
self._primary_field,
DataType.VARCHAR,
is_primary=True,
auto_id=False,
max_length=65_535,
)
)
# Create the vector field, supports binary or float vectors
fields.append(
FieldSchema(self._vector_field, infer_dtype_bydata(embeddings[0]), dim=dim)
)
# Create the schema for the collection
schema = CollectionSchema(
fields,
description=self.collection_description,
partition_key_field=self._partition_key_field,
)
# Create the collection
try:
if self.num_shards is not None:
# Issue with defaults:
# https://github.com/milvus-io/pymilvus/blob/59bf5e811ad56e20946559317fed855330758d9c/pymilvus/client/prepare.py#L82-L85
self.col = Collection(
name=self.collection_name,
schema=schema,
consistency_level=self.consistency_level,
using=self.alias,
num_shards=self.num_shards,
)
else:
self.col = Collection(
name=self.collection_name,
schema=schema,
consistency_level=self.consistency_level,
using=self.alias,
)
# Set the collection properties if they exist
if self.collection_properties is not None:
self.col.set_properties(self.collection_properties)
except MilvusException as e:
logger.error(
"Failed to create collection: %s error: %s", self.collection_name, e
)
raise e
def _extract_fields(self) -> None:
"""Grab the existing fields from the Collection"""
from pymilvus import Collection
if isinstance(self.col, Collection):
schema = self.col.schema
for x in schema.fields:
self.fields.append(x.name)
def _get_index(self) -> Optional[dict[str, Any]]:
"""Return the vector index information if it exists"""
from pymilvus import Collection
if isinstance(self.col, Collection):
for x in self.col.indexes:
if x.field_name == self._vector_field:
return x.to_dict()
return None
def _create_index(self) -> None:
"""Create a index on the collection"""
from pymilvus import Collection, MilvusException
if isinstance(self.col, Collection) and self._get_index() is None:
try:
# If no index params, use a default HNSW based one
if self.index_params is None:
self.index_params = {
"metric_type": "L2",
"index_type": "HNSW",
"params": {"M": 8, "efConstruction": 64},
}
try:
self.col.create_index(
self._vector_field,
index_params=self.index_params,
using=self.alias,
)
# If default did not work, most likely on Zilliz Cloud
except MilvusException:
# Use AUTOINDEX based index
self.index_params = {
"metric_type": "L2",
"index_type": "AUTOINDEX",
"params": {},
}
self.col.create_index(
self._vector_field,
index_params=self.index_params,
using=self.alias,
)
logger.debug(
"Successfully created an index on collection: %s",
self.collection_name,
)
except MilvusException as e:
logger.error(
"Failed to create an index on collection: %s", self.collection_name
)
raise e
def _create_search_params(self) -> None:
"""Generate search params based on the current index type"""
from pymilvus import Collection
if isinstance(self.col, Collection) and self.search_params is None:
index = self._get_index()
if index is not None:
index_type: str = index["index_param"]["index_type"]
metric_type: str = index["index_param"]["metric_type"]
self.search_params = self.default_search_params[index_type]
self.search_params["metric_type"] = metric_type
def _load(
self,
partition_names: Optional[list] = None,
replica_number: int = 1,
timeout: Optional[float] = None,
) -> None:
"""Load the collection if available."""
from pymilvus import Collection, utility
from pymilvus.client.types import LoadState
timeout = self.timeout or timeout
if (
isinstance(self.col, Collection)
and self._get_index() is not None
and utility.load_state(self.collection_name, using=self.alias)
== LoadState.NotLoad
):
self.col.load(
partition_names=partition_names,
replica_number=replica_number,
timeout=timeout,
)
| |
159116
|
@deprecated(
since="0.0.18", removal="1.0", alternative_import="langchain_pinecone.Pinecone"
)
class Pinecone(VectorStore):
"""`Pinecone` vector store.
To use, you should have the ``pinecone-client`` python package installed.
This version of Pinecone is deprecated. Please use `langchain_pinecone.Pinecone`
instead.
"""
def __init__(
self,
index: Any,
embedding: Union[Embeddings, Callable],
text_key: str,
namespace: Optional[str] = None,
distance_strategy: Optional[DistanceStrategy] = DistanceStrategy.COSINE,
):
"""Initialize with Pinecone client."""
pinecone = _import_pinecone()
if not isinstance(embedding, Embeddings):
warnings.warn(
"Passing in `embedding` as a Callable is deprecated. Please pass in an"
" Embeddings object instead."
)
if not isinstance(index, pinecone.Index):
raise ValueError(
f"client should be an instance of pinecone.Index, " f"got {type(index)}"
)
self._index = index
self._embedding = embedding
self._text_key = text_key
self._namespace = namespace
self.distance_strategy = distance_strategy
@property
def embeddings(self) -> Optional[Embeddings]:
"""Access the query embedding object if available."""
if isinstance(self._embedding, Embeddings):
return self._embedding
return None
def _embed_documents(self, texts: Iterable[str]) -> List[List[float]]:
"""Embed search docs."""
if isinstance(self._embedding, Embeddings):
return self._embedding.embed_documents(list(texts))
return [self._embedding(t) for t in texts]
def _embed_query(self, text: str) -> List[float]:
"""Embed query text."""
if isinstance(self._embedding, Embeddings):
return self._embedding.embed_query(text)
return self._embedding(text)
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
namespace: Optional[str] = None,
batch_size: int = 32,
embedding_chunk_size: int = 1000,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Upsert optimization is done by chunking the embeddings and upserting them.
This is done to avoid memory issues and optimize using HTTP based embeddings.
For OpenAI embeddings, use pool_threads>4 when constructing the pinecone.Index,
embedding_chunk_size>1000 and batch_size~64 for best performance.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of ids to associate with the texts.
namespace: Optional pinecone namespace to add the texts to.
batch_size: Batch size to use when adding the texts to the vectorstore.
embedding_chunk_size: Chunk size to use when embedding the texts.
Returns:
List of ids from adding the texts into the vectorstore.
"""
if namespace is None:
namespace = self._namespace
texts = list(texts)
ids = ids or [str(uuid.uuid4()) for _ in texts]
metadatas = metadatas or [{} for _ in texts]
for metadata, text in zip(metadatas, texts):
metadata[self._text_key] = text
# For loops to avoid memory issues and optimize when using HTTP based embeddings
# The first loop runs the embeddings, it benefits when using OpenAI embeddings
# The second loops runs the pinecone upsert asynchronously.
for i in range(0, len(texts), embedding_chunk_size):
chunk_texts = texts[i : i + embedding_chunk_size]
chunk_ids = ids[i : i + embedding_chunk_size]
chunk_metadatas = metadatas[i : i + embedding_chunk_size]
embeddings = self._embed_documents(chunk_texts)
async_res = [
self._index.upsert(
vectors=batch,
namespace=namespace,
async_req=True,
**kwargs,
)
for batch in batch_iterate(
batch_size, zip(chunk_ids, embeddings, chunk_metadatas)
)
]
[res.get() for res in async_res]
return ids
def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
) -> List[Tuple[Document, float]]:
"""Return pinecone documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Dictionary of argument(s) to filter on metadata
namespace: Namespace to search in. Default will search in '' namespace.
Returns:
List of Documents most similar to the query and score for each
"""
return self.similarity_search_by_vector_with_score(
self._embed_query(query), k=k, filter=filter, namespace=namespace
)
def similarity_search_by_vector_with_score(
self,
embedding: List[float],
*,
k: int = 4,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
) -> List[Tuple[Document, float]]:
"""Return pinecone documents most similar to embedding, along with scores."""
if namespace is None:
namespace = self._namespace
docs = []
results = self._index.query(
vector=[embedding],
top_k=k,
include_metadata=True,
namespace=namespace,
filter=filter,
)
for res in results["matches"]:
metadata = res["metadata"]
if self._text_key in metadata:
text = metadata.pop(self._text_key)
score = res["score"]
docs.append((Document(page_content=text, metadata=metadata), score))
else:
logger.warning(
f"Found document with no `{self._text_key}` key. Skipping."
)
return docs
def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return pinecone documents most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Dictionary of argument(s) to filter on metadata
namespace: Namespace to search in. Default will search in '' namespace.
Returns:
List of Documents most similar to the query and score for each
"""
docs_and_scores = self.similarity_search_with_score(
query, k=k, filter=filter, namespace=namespace, **kwargs
)
return [doc for doc, _ in docs_and_scores]
def _select_relevance_score_fn(self) -> Callable[[float], float]:
"""
The 'correct' relevance function
may differ depending on a few things, including:
- the distance / similarity metric used by the VectorStore
- the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
- embedding dimensionality
- etc.
"""
if self.distance_strategy == DistanceStrategy.COSINE:
return self._cosine_relevance_score_fn
elif self.distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT:
return self._max_inner_product_relevance_score_fn
elif self.distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE:
return self._euclidean_relevance_score_fn
else:
raise ValueError(
"Unknown distance strategy, must be cosine, max_inner_product "
"(dot product), or euclidean"
)
@staticmethod
def _cosine_relevance_score_fn(score: float) -> float:
"""Pinecone returns cosine similarity scores between [-1,1]"""
return (score + 1) / 2
| |
159118
|
import asyncio
from typing import Any, Dict, Iterable, List, Optional, Tuple
import numpy as np
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore
from langchain_community.vectorstores.utils import maximal_marginal_relevance
DEFAULT_K = 4 # Number of Documents to return.
| |
159142
|
def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
**kwargs: Any,
) -> List[Document]:
"""Return documents most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Array of Elasticsearch filter clauses to apply to the query.
Returns:
List of Documents most similar to the query,
in descending order of similarity.
"""
results = self.similarity_search_with_score(
query=query, k=k, filter=filter, **kwargs
)
return [doc for doc, _ in results]
def similarity_search_with_score(
self, query: str, k: int, filter: Optional[dict] = None, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Return documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
size: Number of Documents to return. Defaults to 4.
filter: Array of Elasticsearch filter clauses to apply to the query.
Returns:
List of Documents most similar to the query and score for each
"""
search_params = kwargs.get("search_params") or {}
if len(search_params) == 0 or search_params.get("size") is None:
search_params["size"] = k
return self._search(query=query, filter=filter, **kwargs)
@classmethod
def from_documents(
cls,
documents: List[Document],
embedding: Optional[Embeddings] = None,
**kwargs: Any,
) -> "BESVectorStore":
"""Construct BESVectorStore wrapper from documents.
Args:
documents: List of documents to add to the Elasticsearch index.
embedding: Embedding function to use to embed the texts.
Do not provide if using a strategy
that doesn't require inference.
kwargs: create index key words arguments
"""
vectorStore = BESVectorStore._bes_vector_store(embedding=embedding, **kwargs)
# Encode the provided texts and add them to the newly created index.
vectorStore.add_documents(documents)
return vectorStore
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[Dict[str, Any]]] = None,
**kwargs: Any,
) -> "BESVectorStore":
"""Construct BESVectorStore wrapper from raw documents.
Args:
texts: List of texts to add to the Elasticsearch index.
embedding: Embedding function to use to embed the texts.
metadatas: Optional list of metadatas associated with the texts.
index_name: Name of the Elasticsearch index to create.
kwargs: create index key words arguments
"""
vectorStore = BESVectorStore._bes_vector_store(embedding=embedding, **kwargs)
# Encode the provided texts and add them to the newly created index.
vectorStore.add_texts(texts, metadatas=metadatas, **kwargs)
return vectorStore
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[Dict[Any, Any]]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
Returns:
List of ids from adding the texts into the vectorstore.
"""
try:
from elasticsearch.helpers import BulkIndexError, bulk
except ImportError:
raise ImportError(
"Could not import elasticsearch python package. "
"Please install it with `pip install elasticsearch`."
)
embeddings = []
create_index_if_not_exists = kwargs.get("create_index_if_not_exists", True)
ids = kwargs.get("ids", [str(uuid.uuid4()) for _ in texts])
refresh_indices = kwargs.get("refresh_indices", True)
requests = []
if self.embedding is not None:
embeddings = self.embedding.embed_documents(list(texts))
dims_length = len(embeddings[0])
if create_index_if_not_exists:
self._create_index_if_not_exists(dims_length=dims_length)
for i, (text, vector) in enumerate(zip(texts, embeddings)):
metadata = metadatas[i] if metadatas else {}
requests.append(
{
"_op_type": "index",
"_index": self.index_name,
self.query_field: text,
self.vector_query_field: vector,
"metadata": metadata,
"_id": ids[i],
}
)
else:
if create_index_if_not_exists:
self._create_index_if_not_exists()
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
requests.append(
{
"_op_type": "index",
"_index": self.index_name,
self.query_field: text,
"metadata": metadata,
"_id": ids[i],
}
)
if len(requests) > 0:
try:
success, failed = bulk(
self.client, requests, stats_only=True, refresh=refresh_indices
)
logger.debug(
f"Added {success} and failed to add {failed} texts to index"
)
logger.debug(f"added texts {ids} to index")
return ids
except BulkIndexError as e:
logger.error(f"Error adding texts: {e}")
firstError = e.errors[0].get("index", {}).get("error", {})
logger.error(f"First error reason: {firstError.get('reason')}")
raise e
else:
logger.debug("No texts to add to index")
return []
@staticmethod
def _bes_vector_store(
embedding: Optional[Embeddings] = None, **kwargs: Any
) -> "BESVectorStore":
index_name = kwargs.get("index_name")
if index_name is None:
raise ValueError("Please provide an index_name.")
bes_url = kwargs.get("bes_url")
if bes_url is None:
raise ValueError("Please provided a valid bes connection url")
return BESVectorStore(embedding=embedding, **kwargs)
| |
159150
|
def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to QdrantClient.search()
Returns:
List of documents most similar to the query text and distance for each.
"""
return self.similarity_search_with_score_by_vector(
self._embed_query(query),
k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
@sync_call_fallback
async def asimilarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to
AsyncQdrantClient.Search().
Returns:
List of documents most similar to the query text and distance for each.
"""
query_embedding = await self._aembed_query(query)
return await self.asimilarity_search_with_score_by_vector(
query_embedding,
k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to QdrantClient.search()
Returns:
List of Documents most similar to the query.
"""
results = self.similarity_search_with_score_by_vector(
embedding,
k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return list(map(itemgetter(0), results))
@sync_call_fallback
async def asimilarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to
AsyncQdrantClient.Search().
Returns:
List of Documents most similar to the query.
"""
results = await self.asimilarity_search_with_score_by_vector(
embedding,
k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return list(map(itemgetter(0), results))
| |
159151
|
def similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to QdrantClient.search()
Returns:
List of documents most similar to the query text and distance for each.
"""
if filter is not None and isinstance(filter, dict):
warnings.warn(
"Using dict as a `filter` is deprecated. Please use qdrant-client "
"filters directly: "
"https://qdrant.tech/documentation/concepts/filtering/",
DeprecationWarning,
)
qdrant_filter = self._qdrant_filter_from_dict(filter)
else:
qdrant_filter = filter
query_vector = embedding
if self.vector_name is not None:
query_vector = (self.vector_name, embedding) # type: ignore[assignment]
results = self.client.search(
collection_name=self.collection_name,
query_vector=query_vector,
query_filter=qdrant_filter,
search_params=search_params,
limit=k,
offset=offset,
with_payload=True,
with_vectors=False, # Langchain does not expect vectors to be returned
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return [
(
self._document_from_scored_point(
result,
self.collection_name,
self.content_payload_key,
self.metadata_payload_key,
),
result.score,
)
for result in results
]
@sync_call_fallback
async def asimilarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to
AsyncQdrantClient.Search().
Returns:
List of documents most similar to the query text and distance for each.
"""
from qdrant_client.local.async_qdrant_local import AsyncQdrantLocal
if self.async_client is None or isinstance(
self.async_client._client, AsyncQdrantLocal
):
raise NotImplementedError(
"QdrantLocal cannot interoperate with sync and async clients"
)
if filter is not None and isinstance(filter, dict):
warnings.warn(
"Using dict as a `filter` is deprecated. Please use qdrant-client "
"filters directly: "
"https://qdrant.tech/documentation/concepts/filtering/",
DeprecationWarning,
)
qdrant_filter = self._qdrant_filter_from_dict(filter)
else:
qdrant_filter = filter
query_vector = embedding
if self.vector_name is not None:
query_vector = (self.vector_name, embedding) # type: ignore[assignment]
results = await self.async_client.search(
collection_name=self.collection_name,
query_vector=query_vector,
query_filter=qdrant_filter,
search_params=search_params,
limit=k,
offset=offset,
with_payload=True,
with_vectors=False, # Langchain does not expect vectors to be returned
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return [
(
self._document_from_scored_point(
result,
self.collection_name,
self.content_payload_key,
self.metadata_payload_key,
),
result.score,
)
for result in results
]
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to QdrantClient.search()
Returns:
List of Documents selected by maximal marginal relevance.
"""
query_embedding = self._embed_query(query)
return self.max_marginal_relevance_search_by_vector(
query_embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
filter=filter,
search_params=search_params,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
| |
159156
|
@classmethod
def construct_instance(
cls: Type[Qdrant],
texts: List[str],
embedding: Embeddings,
location: Optional[str] = None,
url: Optional[str] = None,
port: Optional[int] = 6333,
grpc_port: int = 6334,
prefer_grpc: bool = False,
https: Optional[bool] = None,
api_key: Optional[str] = None,
prefix: Optional[str] = None,
timeout: Optional[float] = None,
host: Optional[str] = None,
path: Optional[str] = None,
collection_name: Optional[str] = None,
distance_func: str = "Cosine",
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADATA_KEY,
vector_name: Optional[str] = VECTOR_NAME,
shard_number: Optional[int] = None,
replication_factor: Optional[int] = None,
write_consistency_factor: Optional[int] = None,
on_disk_payload: Optional[bool] = None,
hnsw_config: Optional[common_types.HnswConfigDiff] = None,
optimizers_config: Optional[common_types.OptimizersConfigDiff] = None,
wal_config: Optional[common_types.WalConfigDiff] = None,
quantization_config: Optional[common_types.QuantizationConfig] = None,
init_from: Optional[common_types.InitFrom] = None,
on_disk: Optional[bool] = None,
force_recreate: bool = False,
**kwargs: Any,
) -> Qdrant:
try:
import qdrant_client # noqa
except ImportError:
raise ImportError(
"Could not import qdrant-client python package. "
"Please install it with `pip install qdrant-client`."
)
from grpc import RpcError
from qdrant_client.http import models as rest
from qdrant_client.http.exceptions import UnexpectedResponse
# Just do a single quick embedding to get vector size
partial_embeddings = embedding.embed_documents(texts[:1])
vector_size = len(partial_embeddings[0])
collection_name = collection_name or uuid.uuid4().hex
distance_func = distance_func.upper()
client, async_client = cls._generate_clients(
location=location,
url=url,
port=port,
grpc_port=grpc_port,
prefer_grpc=prefer_grpc,
https=https,
api_key=api_key,
prefix=prefix,
timeout=timeout,
host=host,
path=path,
**kwargs,
)
try:
# Skip any validation in case of forced collection recreate.
if force_recreate:
raise ValueError
# Get the vector configuration of the existing collection and vector, if it
# was specified. If the old configuration does not match the current one,
# an exception is being thrown.
collection_info = client.get_collection(collection_name=collection_name)
current_vector_config = collection_info.config.params.vectors
if isinstance(current_vector_config, dict) and vector_name is not None:
if vector_name not in current_vector_config:
raise QdrantException(
f"Existing Qdrant collection {collection_name} does not "
f"contain vector named {vector_name}. Did you mean one of the "
f"existing vectors: {', '.join(current_vector_config.keys())}? "
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
current_vector_config = current_vector_config.get(vector_name) # type: ignore[assignment]
elif isinstance(current_vector_config, dict) and vector_name is None:
raise QdrantException(
f"Existing Qdrant collection {collection_name} uses named vectors. "
f"If you want to reuse it, please set `vector_name` to any of the "
f"existing named vectors: "
f"{', '.join(current_vector_config.keys())}."
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
elif (
not isinstance(current_vector_config, dict) and vector_name is not None
):
raise QdrantException(
f"Existing Qdrant collection {collection_name} doesn't use named "
f"vectors. If you want to reuse it, please set `vector_name` to "
f"`None`. If you want to recreate the collection, set "
f"`force_recreate` parameter to `True`."
)
# Check if the vector configuration has the same dimensionality.
if current_vector_config.size != vector_size: # type: ignore[union-attr]
raise QdrantException(
f"Existing Qdrant collection is configured for vectors with "
f"{current_vector_config.size} " # type: ignore[union-attr]
f"dimensions. Selected embeddings are {vector_size}-dimensional. "
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
current_distance_func = (
current_vector_config.distance.name.upper() # type: ignore[union-attr]
)
if current_distance_func != distance_func:
raise QdrantException(
f"Existing Qdrant collection is configured for "
f"{current_distance_func} similarity, but requested "
f"{distance_func}. Please set `distance_func` parameter to "
f"`{current_distance_func}` if you want to reuse it. "
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
except (UnexpectedResponse, RpcError, ValueError):
vectors_config = rest.VectorParams(
size=vector_size,
distance=rest.Distance[distance_func],
on_disk=on_disk,
)
# If vector name was provided, we're going to use the named vectors feature
# with just a single vector.
if vector_name is not None:
vectors_config = { # type: ignore[assignment]
vector_name: vectors_config,
}
client.recreate_collection(
collection_name=collection_name,
vectors_config=vectors_config,
shard_number=shard_number,
replication_factor=replication_factor,
write_consistency_factor=write_consistency_factor,
on_disk_payload=on_disk_payload,
hnsw_config=hnsw_config,
optimizers_config=optimizers_config,
wal_config=wal_config,
quantization_config=quantization_config,
init_from=init_from,
timeout=timeout, # type: ignore[arg-type]
)
qdrant = cls(
client=client,
collection_name=collection_name,
embeddings=embedding,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
distance_strategy=distance_func,
vector_name=vector_name,
async_client=async_client,
)
return qdrant
| |
159157
|
@classmethod
async def aconstruct_instance(
cls: Type[Qdrant],
texts: List[str],
embedding: Embeddings,
location: Optional[str] = None,
url: Optional[str] = None,
port: Optional[int] = 6333,
grpc_port: int = 6334,
prefer_grpc: bool = False,
https: Optional[bool] = None,
api_key: Optional[str] = None,
prefix: Optional[str] = None,
timeout: Optional[float] = None,
host: Optional[str] = None,
path: Optional[str] = None,
collection_name: Optional[str] = None,
distance_func: str = "Cosine",
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADATA_KEY,
vector_name: Optional[str] = VECTOR_NAME,
shard_number: Optional[int] = None,
replication_factor: Optional[int] = None,
write_consistency_factor: Optional[int] = None,
on_disk_payload: Optional[bool] = None,
hnsw_config: Optional[common_types.HnswConfigDiff] = None,
optimizers_config: Optional[common_types.OptimizersConfigDiff] = None,
wal_config: Optional[common_types.WalConfigDiff] = None,
quantization_config: Optional[common_types.QuantizationConfig] = None,
init_from: Optional[common_types.InitFrom] = None,
on_disk: Optional[bool] = None,
force_recreate: bool = False,
**kwargs: Any,
) -> Qdrant:
try:
import qdrant_client # noqa
except ImportError:
raise ImportError(
"Could not import qdrant-client python package. "
"Please install it with `pip install qdrant-client`."
)
from grpc import RpcError
from qdrant_client.http import models as rest
from qdrant_client.http.exceptions import UnexpectedResponse
# Just do a single quick embedding to get vector size
partial_embeddings = await embedding.aembed_documents(texts[:1])
vector_size = len(partial_embeddings[0])
collection_name = collection_name or uuid.uuid4().hex
distance_func = distance_func.upper()
client, async_client = cls._generate_clients(
location=location,
url=url,
port=port,
grpc_port=grpc_port,
prefer_grpc=prefer_grpc,
https=https,
api_key=api_key,
prefix=prefix,
timeout=timeout,
host=host,
path=path,
**kwargs,
)
try:
# Skip any validation in case of forced collection recreate.
if force_recreate:
raise ValueError
# Get the vector configuration of the existing collection and vector, if it
# was specified. If the old configuration does not match the current one,
# an exception is being thrown.
collection_info = client.get_collection(collection_name=collection_name)
current_vector_config = collection_info.config.params.vectors
if isinstance(current_vector_config, dict) and vector_name is not None:
if vector_name not in current_vector_config:
raise QdrantException(
f"Existing Qdrant collection {collection_name} does not "
f"contain vector named {vector_name}. Did you mean one of the "
f"existing vectors: {', '.join(current_vector_config.keys())}? "
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
current_vector_config = current_vector_config.get(vector_name) # type: ignore[assignment]
elif isinstance(current_vector_config, dict) and vector_name is None:
raise QdrantException(
f"Existing Qdrant collection {collection_name} uses named vectors. "
f"If you want to reuse it, please set `vector_name` to any of the "
f"existing named vectors: "
f"{', '.join(current_vector_config.keys())}."
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
elif (
not isinstance(current_vector_config, dict) and vector_name is not None
):
raise QdrantException(
f"Existing Qdrant collection {collection_name} doesn't use named "
f"vectors. If you want to reuse it, please set `vector_name` to "
f"`None`. If you want to recreate the collection, set "
f"`force_recreate` parameter to `True`."
)
# Check if the vector configuration has the same dimensionality.
if current_vector_config.size != vector_size: # type: ignore[union-attr]
raise QdrantException(
f"Existing Qdrant collection is configured for vectors with "
f"{current_vector_config.size} " # type: ignore[union-attr]
f"dimensions. Selected embeddings are {vector_size}-dimensional. "
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
current_distance_func = (
current_vector_config.distance.name.upper() # type: ignore[union-attr]
)
if current_distance_func != distance_func:
raise QdrantException(
f"Existing Qdrant collection is configured for "
f"{current_vector_config.distance} " # type: ignore[union-attr]
f"similarity. Please set `distance_func` parameter to "
f"`{distance_func}` if you want to reuse it. If you want to "
f"recreate the collection, set `force_recreate` parameter to "
f"`True`."
)
except (UnexpectedResponse, RpcError, ValueError):
vectors_config = rest.VectorParams(
size=vector_size,
distance=rest.Distance[distance_func],
on_disk=on_disk,
)
# If vector name was provided, we're going to use the named vectors feature
# with just a single vector.
if vector_name is not None:
vectors_config = { # type: ignore[assignment]
vector_name: vectors_config,
}
client.recreate_collection(
collection_name=collection_name,
vectors_config=vectors_config,
shard_number=shard_number,
replication_factor=replication_factor,
write_consistency_factor=write_consistency_factor,
on_disk_payload=on_disk_payload,
hnsw_config=hnsw_config,
optimizers_config=optimizers_config,
wal_config=wal_config,
quantization_config=quantization_config,
init_from=init_from,
timeout=timeout, # type: ignore[arg-type]
)
qdrant = cls(
client=client,
collection_name=collection_name,
embeddings=embedding,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
distance_strategy=distance_func,
vector_name=vector_name,
async_client=async_client,
)
return qdrant
@staticmethod
def _cosine_relevance_score_fn(distance: float) -> float:
"""Normalize the distance to a score on a scale [0, 1]."""
return (distance + 1.0) / 2.0
def _select_relevance_score_fn(self) -> Callable[[float], float]:
"""
The 'correct' relevance function
may differ depending on a few things, including:
- the distance / similarity metric used by the VectorStore
- the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
- embedding dimensionality
- etc.
"""
if self.distance_strategy == "COSINE":
return self._cosine_relevance_score_fn
elif self.distance_strategy == "DOT":
return self._max_inner_product_relevance_score_fn
elif self.distance_strategy == "EUCLID":
return self._euclidean_relevance_score_fn
else:
raise ValueError(
"Unknown distance strategy, must be cosine, "
"max_inner_product, or euclidean"
)
def _similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Args:
query: input text
k: Number of Documents to return. Defaults to 4.
**kwargs: kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns:
List of Tuples of (doc, similarity_score)
"""
return self.similarity_search_with_score(query, k, **kwargs)
| |
159181
|
@deprecated(
since="0.0.25",
removal="1.0",
alternative_import="langchain_mongodb.MongoDBAtlasVectorSearch",
)
class MongoDBAtlasVectorSearch(VectorStore):
"""`MongoDB Atlas Vector Search` vector store.
To use, you should have both:
- the ``pymongo`` python package installed
- a connection string associated with a MongoDB Atlas Cluster having deployed an
Atlas Search index
Example:
.. code-block:: python
from langchain_community.vectorstores import MongoDBAtlasVectorSearch
from langchain_community.embeddings.openai import OpenAIEmbeddings
from pymongo import MongoClient
mongo_client = MongoClient("<YOUR-CONNECTION-STRING>")
collection = mongo_client["<db_name>"]["<collection_name>"]
embeddings = OpenAIEmbeddings()
vectorstore = MongoDBAtlasVectorSearch(collection, embeddings)
"""
def __init__(
self,
collection: Collection[MongoDBDocumentType],
embedding: Embeddings,
*,
index_name: str = "default",
text_key: str = "text",
embedding_key: str = "embedding",
relevance_score_fn: str = "cosine",
):
"""
Args:
collection: MongoDB collection to add the texts to.
embedding: Text embedding model to use.
text_key: MongoDB field that will contain the text for each
document.
embedding_key: MongoDB field that will contain the embedding for
each document.
index_name: Name of the Atlas Search index.
relevance_score_fn: The similarity score used for the index.
Currently supported: Euclidean, cosine, and dot product.
"""
self._collection = collection
self._embedding = embedding
self._index_name = index_name
self._text_key = text_key
self._embedding_key = embedding_key
self._relevance_score_fn = relevance_score_fn
@property
def embeddings(self) -> Embeddings:
return self._embedding
def _select_relevance_score_fn(self) -> Callable[[float], float]:
if self._relevance_score_fn == "euclidean":
return self._euclidean_relevance_score_fn
elif self._relevance_score_fn == "dotProduct":
return self._max_inner_product_relevance_score_fn
elif self._relevance_score_fn == "cosine":
return self._cosine_relevance_score_fn
else:
raise NotImplementedError(
f"No relevance score function for ${self._relevance_score_fn}"
)
@classmethod
def from_connection_string(
cls,
connection_string: str,
namespace: str,
embedding: Embeddings,
**kwargs: Any,
) -> MongoDBAtlasVectorSearch:
"""Construct a `MongoDB Atlas Vector Search` vector store
from a MongoDB connection URI.
Args:
connection_string: A valid MongoDB connection URI.
namespace: A valid MongoDB namespace (database and collection).
embedding: The text embedding model to use for the vector store.
Returns:
A new MongoDBAtlasVectorSearch instance.
"""
try:
from importlib.metadata import version
from pymongo import MongoClient
from pymongo.driver_info import DriverInfo
except ImportError:
raise ImportError(
"Could not import pymongo, please install it with "
"`pip install pymongo`."
)
client: MongoClient = MongoClient(
connection_string,
driver=DriverInfo(name="Langchain", version=version("langchain")),
)
db_name, collection_name = namespace.split(".")
collection = client[db_name][collection_name]
return cls(collection, embedding, **kwargs)
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[Dict[str, Any]]] = None,
**kwargs: Any,
) -> List:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
Returns:
List of ids from adding the texts into the vectorstore.
"""
batch_size = kwargs.get("batch_size", DEFAULT_INSERT_BATCH_SIZE)
_metadatas: Union[List, Generator] = metadatas or ({} for _ in texts)
texts_batch = []
metadatas_batch = []
result_ids = []
for i, (text, metadata) in enumerate(zip(texts, _metadatas)):
texts_batch.append(text)
metadatas_batch.append(metadata)
if (i + 1) % batch_size == 0:
result_ids.extend(self._insert_texts(texts_batch, metadatas_batch))
texts_batch = []
metadatas_batch = []
if texts_batch:
result_ids.extend(self._insert_texts(texts_batch, metadatas_batch))
return result_ids
def _insert_texts(self, texts: List[str], metadatas: List[Dict[str, Any]]) -> List:
if not texts:
return []
# Embed and create the documents
embeddings = self._embedding.embed_documents(texts)
to_insert = [
{self._text_key: t, self._embedding_key: embedding, **m}
for t, m, embedding in zip(texts, metadatas, embeddings)
]
# insert the documents in MongoDB Atlas
insert_result = self._collection.insert_many(to_insert) # type: ignore
return insert_result.inserted_ids
def _similarity_search_with_score(
self,
embedding: List[float],
k: int = 4,
pre_filter: Optional[Dict] = None,
post_filter_pipeline: Optional[List[Dict]] = None,
) -> List[Tuple[Document, float]]:
params = {
"queryVector": embedding,
"path": self._embedding_key,
"numCandidates": k * 10,
"limit": k,
"index": self._index_name,
}
if pre_filter:
params["filter"] = pre_filter
query = {"$vectorSearch": params}
pipeline = [
query,
{"$set": {"score": {"$meta": "vectorSearchScore"}}},
]
if post_filter_pipeline is not None:
pipeline.extend(post_filter_pipeline)
cursor = self._collection.aggregate(pipeline) # type: ignore[arg-type]
docs = []
for res in cursor:
text = res.pop(self._text_key)
score = res.pop("score")
docs.append((Document(page_content=text, metadata=res), score))
return docs
def similarity_search_with_score(
self,
query: str,
k: int = 4,
pre_filter: Optional[Dict] = None,
post_filter_pipeline: Optional[List[Dict]] = None,
) -> List[Tuple[Document, float]]:
"""Return MongoDB documents most similar to the given query and their scores.
Uses the vectorSearch operator available in MongoDB Atlas Search.
For more: https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/
Args:
query: Text to look up documents similar to.
k: (Optional) number of documents to return. Defaults to 4.
pre_filter: (Optional) dictionary of argument(s) to prefilter document
fields on.
post_filter_pipeline: (Optional) Pipeline of MongoDB aggregation stages
following the vectorSearch stage.
Returns:
List of documents most similar to the query and their scores.
"""
embedding = self._embedding.embed_query(query)
docs = self._similarity_search_with_score(
embedding,
k=k,
pre_filter=pre_filter,
post_filter_pipeline=post_filter_pipeline,
)
return docs
def similarity_search(
self,
query: str,
k: int = 4,
pre_filter: Optional[Dict] = None,
post_filter_pipeline: Optional[List[Dict]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return MongoDB documents most similar to the given query.
Uses the vectorSearch operator available in MongoDB Atlas Search.
For more: https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/
Args:
query: Text to look up documents similar to.
k: (Optional) number of documents to return. Defaults to 4.
pre_filter: (Optional) dictionary of argument(s) to prefilter document
fields on.
post_filter_pipeline: (Optional) Pipeline of MongoDB aggregation stages
following the vectorSearch stage.
Returns:
List of documents most similar to the query and their scores.
"""
additional = kwargs.get("additional")
docs_and_scores = self.similarity_search_with_score(
query,
k=k,
pre_filter=pre_filter,
post_filter_pipeline=post_filter_pipeline,
)
if additional and "similarity_score" in additional:
for doc, score in docs_and_scores:
doc.metadata["score"] = score
return [doc for doc, _ in docs_and_scores]
| |
159204
|
def _search_with_score_and_embeddings_by_vector(
self,
embedding: List[float],
k: int = DEFAULT_TOP_K,
filter: Optional[Dict[str, Any]] = None,
brute_force: bool = False,
fraction_lists_to_search: Optional[float] = None,
) -> List[Tuple[Document, List[float], float]]:
from google.cloud import bigquery
# Create an index if no index exists.
if not self._have_index and not self._creating_index:
self._initialize_vector_index()
# Prepare filter
filter_expr = "TRUE"
if filter:
filter_expressions = []
for i in filter.items():
if isinstance(i[1], float):
expr = (
"ABS(CAST(JSON_VALUE("
f"base.`{self.metadata_field}`,'$.{i[0]}') "
f"AS FLOAT64) - {i[1]}) "
f"<= {sys.float_info.epsilon}"
)
else:
val = str(i[1]).replace('"', '\\"')
expr = (
f"JSON_VALUE(base.`{self.metadata_field}`,'$.{i[0]}')"
f' = "{val}"'
)
filter_expressions.append(expr)
filter_expression_str = " AND ".join(filter_expressions)
filter_expr += f" AND ({filter_expression_str})"
# Configure and run a query job.
job_config = bigquery.QueryJobConfig(
query_parameters=[
bigquery.ArrayQueryParameter("v", "FLOAT64", embedding),
],
use_query_cache=False,
priority=bigquery.QueryPriority.BATCH,
)
if self.distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE:
distance_type = "EUCLIDEAN"
elif self.distance_strategy == DistanceStrategy.COSINE:
distance_type = "COSINE"
# Default to EUCLIDEAN_DISTANCE
else:
distance_type = "EUCLIDEAN"
if brute_force:
options_string = ",options => '{\"use_brute_force\":true}'"
elif fraction_lists_to_search:
if fraction_lists_to_search == 0 or fraction_lists_to_search >= 1.0:
raise ValueError(
"`fraction_lists_to_search` must be between " "0.0 and 1.0"
)
options_string = (
',options => \'{"fraction_lists_to_search":'
f"{fraction_lists_to_search}}}'"
)
else:
options_string = ""
query = f"""
SELECT
base.*,
distance AS _vector_search_distance
FROM VECTOR_SEARCH(
TABLE `{self.full_table_id}`,
"{self.text_embedding_field}",
(SELECT @v AS {self.text_embedding_field}),
distance_type => "{distance_type}",
top_k => {k}
{options_string}
)
WHERE {filter_expr}
LIMIT {k}
"""
document_tuples: List[Tuple[Document, List[float], float]] = []
# TODO(vladkol): Use jobCreationMode=JOB_CREATION_OPTIONAL when available.
job = self.bq_client.query(
query, job_config=job_config, api_method=bigquery.enums.QueryApiMethod.QUERY
)
# Process job results.
for row in job:
metadata = row[self.metadata_field]
if metadata:
if not isinstance(metadata, dict):
metadata = json.loads(metadata)
else:
metadata = {}
metadata["__id"] = row[self.doc_id_field]
metadata["__job_id"] = job.job_id
doc = Document(page_content=row[self.content_field], metadata=metadata)
document_tuples.append(
(doc, row[self.text_embedding_field], row["_vector_search_distance"])
)
return document_tuples
def similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = DEFAULT_TOP_K,
filter: Optional[Dict[str, Any]] = None,
brute_force: bool = False,
fraction_lists_to_search: Optional[float] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter on metadata properties, e.g.
{
"str_property": "foo",
"int_property": 123
}
brute_force: Whether to use brute force search. Defaults to False.
fraction_lists_to_search: Optional percentage of lists to search,
must be in range 0.0 and 1.0, exclusive.
If Node, uses service's default which is 0.05.
Returns:
List of Documents most similar to the query vector with distance.
"""
del kwargs
document_tuples = self._search_with_score_and_embeddings_by_vector(
embedding, k, filter, brute_force, fraction_lists_to_search
)
return [(doc, distance) for doc, _, distance in document_tuples]
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = DEFAULT_TOP_K,
filter: Optional[Dict[str, Any]] = None,
brute_force: bool = False,
fraction_lists_to_search: Optional[float] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter on metadata properties, e.g.
{
"str_property": "foo",
"int_property": 123
}
brute_force: Whether to use brute force search. Defaults to False.
fraction_lists_to_search: Optional percentage of lists to search,
must be in range 0.0 and 1.0, exclusive.
If Node, uses service's default which is 0.05.
Returns:
List of Documents most similar to the query vector.
"""
tuples = self.similarity_search_with_score_by_vector(
embedding, k, filter, brute_force, fraction_lists_to_search, **kwargs
)
return [i[0] for i in tuples]
def similarity_search_with_score(
self,
query: str,
k: int = DEFAULT_TOP_K,
filter: Optional[Dict[str, Any]] = None,
brute_force: bool = False,
fraction_lists_to_search: Optional[float] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Run similarity search with score.
Args:
query: search query text.
k: Number of Documents to return. Defaults to 4.
filter: Filter on metadata properties, e.g.
{
"str_property": "foo",
"int_property": 123
}
brute_force: Whether to use brute force search. Defaults to False.
fraction_lists_to_search: Optional percentage of lists to search,
must be in range 0.0 and 1.0, exclusive.
If Node, uses service's default which is 0.05.
Returns:
List of Documents most similar to the query vector, with similarity scores.
"""
emb = self.embedding_model.embed_query(query) # type: ignore
return self.similarity_search_with_score_by_vector(
emb, k, filter, brute_force, fraction_lists_to_search, **kwargs
)
def similarity_search(
self,
query: str,
k: int = DEFAULT_TOP_K,
filter: Optional[Dict[str, Any]] = None,
brute_force: bool = False,
fraction_lists_to_search: Optional[float] = None,
**kwargs: Any,
) -> List[Document]:
"""Run similarity search.
Args:
query: search query text.
k: Number of Documents to return. Defaults to 4.
filter: Filter on metadata properties, e.g.
{
"str_property": "foo",
"int_property": 123
}
brute_force: Whether to use brute force search. Defaults to False.
fraction_lists_to_search: Optional percentage of lists to search,
must be in range 0.0 and 1.0, exclusive.
If Node, uses service's default which is 0.05.
Returns:
List of Documents most similar to the query vector.
"""
tuples = self.similarity_search_with_score(
query, k, filter, brute_force, fraction_lists_to_search, **kwargs
)
return [i[0] for i in tuples]
def _select_relevance_score_fn(self) -> Callable[[float], float]:
if self.distance_strategy == DistanceStrategy.COSINE:
return BigQueryVectorSearch._cosine_relevance_score_fn
else:
raise ValueError(
"Relevance score is not supported "
f"for `{self.distance_strategy}` distance."
)
| |
159208
|
def _construct_documents_from_results_without_score(
self, results: Dict[str, List[Dict[str, str]]]
) -> List[Document]:
"""Helper to convert Marqo results into documents.
Args:
results (List[dict]): A marqo results object with the 'hits'.
include_scores (bool, optional): Include scores alongside documents.
Defaults to False.
Returns:
Union[List[Document], List[Tuple[Document, float]]]: The documents or
document score pairs if `include_scores` is true.
"""
documents: List[Document] = []
for res in results["hits"]:
if self.page_content_builder is None:
text = res["text"]
else:
text = self.page_content_builder(res)
metadata = json.loads(res.get("metadata", "{}"))
documents.append(Document(page_content=text, metadata=metadata))
return documents
def marqo_similarity_search(
self,
query: Union[str, Dict[str, float]],
k: int = 4,
) -> Dict[str, List[Dict[str, str]]]:
"""Return documents from Marqo exposing Marqo's output directly
Args:
query (str): The query to search with.
k (int, optional): The number of documents to return. Defaults to 4.
Returns:
List[Dict[str, Any]]: This hits from marqo.
"""
results = self._client.index(self._index_name).search(
q=query, searchable_attributes=self._searchable_attributes, limit=k
)
return results
def marqo_bulk_similarity_search(
self, queries: Iterable[Union[str, Dict[str, float]]], k: int = 4
) -> Dict[str, List[Dict[str, List[Dict[str, str]]]]]:
"""Return documents from Marqo using a bulk search, exposes Marqo's
output directly
Args:
queries (Iterable[Union[str, Dict[str, float]]]): A list of queries.
k (int, optional): The number of documents to return for each query.
Defaults to 4.
Returns:
Dict[str, Dict[List[Dict[str, Dict[str, Any]]]]]: A bulk search results
object
"""
bulk_results = {
"result": [
self._client.index(self._index_name).search(
q=query, searchable_attributes=self._searchable_attributes, limit=k
)
for query in queries
]
}
return bulk_results
@classmethod
def from_documents(
cls: Type[Marqo],
documents: List[Document],
embedding: Union[Embeddings, None] = None,
**kwargs: Any,
) -> Marqo:
"""Return VectorStore initialized from documents. Note that Marqo does not
need embeddings, we retain the parameter to adhere to the Liskov substitution
principle.
Args:
documents (List[Document]): Input documents
embedding (Any, optional): Embeddings (not required). Defaults to None.
Returns:
VectorStore: A Marqo vectorstore
"""
texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
return cls.from_texts(texts, metadatas=metadatas, **kwargs)
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Any = None,
metadatas: Optional[List[dict]] = None,
index_name: str = "",
url: str = "http://localhost:8882",
api_key: str = "",
add_documents_settings: Optional[Dict[str, Any]] = None,
searchable_attributes: Optional[List[str]] = None,
page_content_builder: Optional[Callable[[Dict[str, str]], str]] = None,
index_settings: Optional[Dict[str, Any]] = None,
verbose: bool = True,
**kwargs: Any,
) -> Marqo:
"""Return Marqo initialized from texts. Note that Marqo does not need
embeddings, we retain the parameter to adhere to the Liskov
substitution principle.
This is a quick way to get started with marqo - simply provide your texts and
metadatas and this will create an instance of the data store and index the
provided data.
To know the ids of your documents with this approach you will need to include
them in under the key "_id" in your metadatas for each text
Example:
.. code-block:: python
from langchain_community.vectorstores import Marqo
datastore = Marqo(texts=['text'], index_name='my-first-index',
url='http://localhost:8882')
Args:
texts (List[str]): A list of texts to index into marqo upon creation.
embedding (Any, optional): Embeddings (not required). Defaults to None.
index_name (str, optional): The name of the index to use, if none is
provided then one will be created with a UUID. Defaults to None.
url (str, optional): The URL for Marqo. Defaults to "http://localhost:8882".
api_key (str, optional): The API key for Marqo. Defaults to "".
metadatas (Optional[List[dict]], optional): A list of metadatas, to
accompany the texts. Defaults to None.
this is only used when a new index is being created. Defaults to "cpu". Can
be "cpu" or "cuda".
add_documents_settings (Optional[Dict[str, Any]], optional): Settings
for adding documents, see
https://docs.marqo.ai/0.0.16/API-Reference/documents/#query-parameters.
Defaults to {}.
index_settings (Optional[Dict[str, Any]], optional): Index settings if
the index doesn't exist, see
https://docs.marqo.ai/0.0.16/API-Reference/indexes/#index-defaults-object.
Defaults to {}.
Returns:
Marqo: An instance of the Marqo vector store
"""
try:
import marqo
except ImportError:
raise ImportError(
"Could not import marqo python package. "
"Please install it with `pip install marqo`."
)
if not index_name:
index_name = str(uuid.uuid4())
client = marqo.Client(url=url, api_key=api_key)
try:
client.create_index(index_name, settings_dict=index_settings or {})
if verbose:
print(f"Created {index_name} successfully.") # noqa: T201
except Exception:
if verbose:
print(f"Index {index_name} exists.") # noqa: T201
instance: Marqo = cls(
client,
index_name,
searchable_attributes=searchable_attributes,
add_documents_settings=add_documents_settings or {},
page_content_builder=page_content_builder,
)
instance.add_texts(texts, metadatas)
return instance
def get_indexes(self) -> List[Dict[str, str]]:
"""Helper to see your available indexes in marqo, useful if the
from_texts method was used without an index name specified
Returns:
List[Dict[str, str]]: The list of indexes
"""
return self._client.get_indexes()["results"]
def get_number_of_documents(self) -> int:
"""Helper to see the number of documents in the index
Returns:
int: The number of documents
"""
return self._client.index(self._index_name).get_stats()["numberOfDocuments"]
| |
159212
|
class DocumentDBVectorSearch(VectorStore):
"""`Amazon DocumentDB (with MongoDB compatibility)` vector store.
Please refer to the official Vector Search documentation for more details:
https://docs.aws.amazon.com/documentdb/latest/developerguide/vector-search.html
To use, you should have both:
- the ``pymongo`` python package installed
- a connection string and credentials associated with a DocumentDB cluster
Example:
. code-block:: python
from langchain_community.vectorstores import DocumentDBVectorSearch
from langchain_community.embeddings.openai import OpenAIEmbeddings
from pymongo import MongoClient
mongo_client = MongoClient("<YOUR-CONNECTION-STRING>")
collection = mongo_client["<db_name>"]["<collection_name>"]
embeddings = OpenAIEmbeddings()
vectorstore = DocumentDBVectorSearch(collection, embeddings)
"""
def __init__(
self,
collection: Collection[DocumentDBDocumentType],
embedding: Embeddings,
*,
index_name: str = "vectorSearchIndex",
text_key: str = "textContent",
embedding_key: str = "vectorContent",
):
"""Constructor for DocumentDBVectorSearch
Args:
collection: MongoDB collection to add the texts to.
embedding: Text embedding model to use.
index_name: Name of the Vector Search index.
text_key: MongoDB field that will contain the text
for each document.
embedding_key: MongoDB field that will contain the embedding
for each document.
"""
self._collection = collection
self._embedding = embedding
self._index_name = index_name
self._text_key = text_key
self._embedding_key = embedding_key
self._similarity_type = DocumentDBSimilarityType.COS
@property
def embeddings(self) -> Embeddings:
return self._embedding
def get_index_name(self) -> str:
"""Returns the index name
Returns:
Returns the index name
"""
return self._index_name
@classmethod
def from_connection_string(
cls,
connection_string: str,
namespace: str,
embedding: Embeddings,
**kwargs: Any,
) -> DocumentDBVectorSearch:
"""Creates an Instance of DocumentDBVectorSearch from a Connection String
Args:
connection_string: The DocumentDB cluster endpoint connection string
namespace: The namespace (database.collection)
embedding: The embedding utility
**kwargs: Dynamic keyword arguments
Returns:
an instance of the vector store
"""
try:
from pymongo import MongoClient
except ImportError:
raise ImportError(
"Could not import pymongo, please install it with "
"`pip install pymongo`."
)
client: MongoClient = MongoClient(connection_string)
db_name, collection_name = namespace.split(".")
collection = client[db_name][collection_name]
return cls(collection, embedding, **kwargs)
def index_exists(self) -> bool:
"""Verifies if the specified index name during instance
construction exists on the collection
Returns:
Returns True on success and False if no such index exists
on the collection
"""
cursor = self._collection.list_indexes()
index_name = self._index_name
for res in cursor:
current_index_name = res.pop("name")
if current_index_name == index_name:
return True
return False
def delete_index(self) -> None:
"""Deletes the index specified during instance construction if it exists"""
if self.index_exists():
self._collection.drop_index(self._index_name)
# Raises OperationFailure on an error (e.g. trying to drop
# an index that does not exist)
def create_index(
self,
dimensions: int = 1536,
similarity: DocumentDBSimilarityType = DocumentDBSimilarityType.COS,
m: int = 16,
ef_construction: int = 64,
) -> dict[str, Any]:
"""Creates an index using the index name specified at
instance construction
Args:
dimensions: Number of dimensions for vector similarity.
The maximum number of supported dimensions is 2000
similarity: Similarity algorithm to use with the HNSW index.
Possible options are:
- DocumentDBSimilarityType.COS (cosine distance),
- DocumentDBSimilarityType.EUC (Euclidean distance), and
- DocumentDBSimilarityType.DOT (dot product).
m: Specifies the max number of connections for an HNSW index.
Large impact on memory consumption.
ef_construction: Specifies the size of the dynamic candidate list
for constructing the graph for HNSW index. Higher values lead
to more accurate results but slower indexing speed.
Returns:
An object describing the created index
"""
self._similarity_type = similarity
# prepare the command
create_index_commands = {
"createIndexes": self._collection.name,
"indexes": [
{
"name": self._index_name,
"key": {self._embedding_key: "vector"},
"vectorOptions": {
"type": "hnsw",
"similarity": similarity,
"dimensions": dimensions,
"m": m,
"efConstruction": ef_construction,
},
}
],
}
# retrieve the database object
current_database = self._collection.database
# invoke the command from the database object
create_index_responses: dict[str, Any] = current_database.command(
create_index_commands
)
return create_index_responses
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[Dict[str, Any]]] = None,
**kwargs: Any,
) -> List:
batch_size = kwargs.get("batch_size", DEFAULT_INSERT_BATCH_SIZE)
_metadatas: Union[List, Generator] = metadatas or ({} for _ in texts)
texts_batch = []
metadatas_batch = []
result_ids = []
for i, (text, metadata) in enumerate(zip(texts, _metadatas)):
texts_batch.append(text)
metadatas_batch.append(metadata)
if (i + 1) % batch_size == 0:
result_ids.extend(self._insert_texts(texts_batch, metadatas_batch))
texts_batch = []
metadatas_batch = []
if texts_batch:
result_ids.extend(self._insert_texts(texts_batch, metadatas_batch))
return result_ids
def _insert_texts(self, texts: List[str], metadatas: List[Dict[str, Any]]) -> List:
"""Used to Load Documents into the collection
Args:
texts: The list of documents strings to load
metadatas: The list of metadata objects associated with each document
Returns:
"""
# If the text is empty, then exit early
if not texts:
return []
# Embed and create the documents
embeddings = self._embedding.embed_documents(texts)
to_insert = [
{self._text_key: t, self._embedding_key: embedding, **m}
for t, m, embedding in zip(texts, metadatas, embeddings)
]
# insert the documents in DocumentDB
insert_result = self._collection.insert_many(to_insert) # type: ignore
return insert_result.inserted_ids
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
collection: Optional[Collection[DocumentDBDocumentType]] = None,
**kwargs: Any,
) -> DocumentDBVectorSearch:
if collection is None:
raise ValueError("Must provide 'collection' named parameter.")
vectorstore = cls(collection, embedding, **kwargs)
vectorstore.add_texts(texts, metadatas=metadatas)
return vectorstore
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
if ids is None:
raise ValueError("No document ids provided to delete.")
for document_id in ids:
self.delete_document_by_id(document_id)
return True
def delete_document_by_id(self, document_id: Optional[str] = None) -> None:
"""Removes a Specific Document by Id
Args:
document_id: The document identifier
"""
try:
from bson.objectid import ObjectId
except ImportError as e:
raise ImportError(
"Unable to import bson, please install with `pip install bson`."
) from e
if document_id is None:
raise ValueError("No document id provided to delete.")
self._collection.delete_one({"_id": ObjectId(document_id)})
| |
159213
|
def _similarity_search_without_score(
self,
embeddings: List[float],
k: int = 4,
ef_search: int = 40,
filter: Optional[Dict[str, Any]] = None,
) -> List[Document]:
"""Returns a list of documents.
Args:
embeddings: The query vector
k: the number of documents to return
ef_search: Specifies the size of the dynamic candidate list
that HNSW index uses during search. A higher value of
efSearch provides better recall at cost of speed.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
A list of documents closest to the query vector
"""
# $match can't be null, so intializes to {} when None to avoid
# "the match filter must be an expression in an object"
if not filter:
filter = {}
pipeline: List[dict[str, Any]] = [
{"$match": filter},
{
"$search": {
"vectorSearch": {
"vector": embeddings,
"path": self._embedding_key,
"similarity": self._similarity_type,
"k": k,
"efSearch": ef_search,
}
},
},
]
cursor = self._collection.aggregate(pipeline)
docs = []
for res in cursor:
text = res.pop(self._text_key)
docs.append(Document(page_content=text, metadata=res))
return docs
def similarity_search(
self,
query: str,
k: int = 4,
ef_search: int = 40,
*,
filter: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> List[Document]:
embeddings = self._embedding.embed_query(query)
docs = self._similarity_search_without_score(
embeddings=embeddings, k=k, ef_search=ef_search, filter=filter
)
return [doc for doc in docs]
| |
159216
|
def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query."""
documents = self.similarity_search_with_score(query=query, k=k)
return [doc for doc, _ in documents]
def similarity_search_with_score(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Perform a search on a query string and return results with score.
Args:
query (str): The text being searched.
k (int, optional): The amount of results to return. Defaults to 4.
Returns:
List[Tuple[Document, float]]
"""
embed = self._embedding.embed_query(query) # type: ignore
documents = self.similarity_search_with_score_by_vector(embedding=embed, k=k)
return documents
def similarity_search_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Document]:
res = self.similarity_search_with_score_by_vector(embedding, k)
return [doc for doc, _ in res]
def similarity_search_with_score_by_vector(
self, embedding: List[float], k: int = 4
) -> List[Tuple[Document, float]]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of pair (Documents, score) most similar to the query vector.
"""
if self._output_fields is None:
query_str = (
"select v, score(v) from "
+ self._entity_name
+ " v where v."
+ self._vectorfield
+ " <-> "
+ json.dumps(embedding)
+ "~"
+ str(k)
)
else:
query_proj = "select "
for field in self._output_fields[:-1]:
query_proj = query_proj + "v." + field + ","
query_proj = query_proj + "v." + self._output_fields[-1]
query_str = (
query_proj
+ ", score(v) from "
+ self._entity_name
+ " v where v."
+ self._vectorfield
+ " <-> "
+ json.dumps(embedding)
+ "~"
+ str(k)
)
query_res = self.ispn.req_query(query_str, self._cache_name)
result = json.loads(query_res.text)
return self._query_result_to_docs(result)
def _query_result_to_docs(
self, result: dict[str, Any]
) -> List[Tuple[Document, float]]:
documents = []
for row in result["hits"]:
hit = row["hit"] or {}
if self._output_fields is None:
entity = hit["*"]
else:
entity = {key: hit.get(key) for key in self._output_fields}
doc = Document(
page_content=self._to_content(entity),
metadata=self._to_metadata(entity),
)
documents.append((doc, hit["score()"]))
return documents
def configure(self, metadata: dict, dimension: int) -> None:
schema = self.schema_builder(metadata, dimension)
output = self.schema_create(schema)
assert (
output.status_code == self.ispn.Codes.OK
), "Unable to create schema. Already exists? "
"Consider using clear_old=True"
assert json.loads(output.text)["error"] is None
if not self.cache_exists():
output = self.cache_create()
assert (
output.status_code == self.ispn.Codes.OK
), "Unable to create cache. Already exists? "
"Consider using clear_old=True"
# Ensure index is clean
self.cache_index_clear()
def config_clear(self) -> None:
self.schema_delete()
self.cache_delete()
@classmethod
def from_texts(
cls: Type[InfinispanVS],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
clear_old: Optional[bool] = True,
auto_config: Optional[bool] = True,
**kwargs: Any,
) -> InfinispanVS:
"""Return VectorStore initialized from texts and embeddings.
In addition to parameters described by the super method, this
implementation provides other configuration params if different
configuration from default is needed.
Parameters
----------
ids : List[str]
Additional list of keys associated to the embedding. If not
provided UUIDs will be generated
clear_old : bool
Whether old data must be deleted. Default True
auto_config: bool
Whether to do a complete server setup (caches,
protobuf definition...). Default True
kwargs: Any
Rest of arguments passed to InfinispanVS. See docs"""
infinispanvs = cls(embedding=embedding, ids=ids, **kwargs)
if auto_config and len(metadatas or []) > 0:
if clear_old:
infinispanvs.config_clear()
vec = embedding.embed_query(texts[len(texts) - 1])
metadatas = cast(List[dict], metadatas)
infinispanvs.configure(metadatas[0], len(vec))
else:
if clear_old:
infinispanvs.cache_clear()
vec = embedding.embed_query(texts[len(texts) - 1])
if texts:
infinispanvs.add_texts(texts, metadatas, vector=vec)
return infinispanvs
REST_TIMEOUT = 10
| |
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|
add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of unique IDs.
Returns:
List of ids from adding the texts into the vectorstore.
"""
texts = list(texts)
embeddings = self._embed_documents(texts)
return self.__add(texts, embeddings, metadatas=metadatas, ids=ids)
async def aadd_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore
asynchronously.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of unique IDs.
Returns:
List of ids from adding the texts into the vectorstore.
"""
texts = list(texts)
embeddings = await self._aembed_documents(texts)
return self.__add(texts, embeddings, metadatas=metadatas, ids=ids)
def add_embeddings(
self,
text_embeddings: Iterable[Tuple[str, List[float]]],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Add the given texts and embeddings to the vectorstore.
Args:
text_embeddings: Iterable pairs of string and embedding to
add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of unique IDs.
Returns:
List of ids from adding the texts into the vectorstore.
"""
# Embed and create the documents.
texts, embeddings = zip(*text_embeddings)
return self.__add(texts, embeddings, metadatas=metadatas, ids=ids)
def similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[Union[Callable, Dict[str, Any]]] = None,
fetch_k: int = 20,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter (Optional[Union[Callable, Dict[str, Any]]]): Filter by metadata.
Defaults to None. If a callable, it must take as input the
metadata dict of Document and return a bool.
fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
**kwargs: kwargs to be passed to similarity search. Can include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns:
List of documents most similar to the query text and L2 distance
in float for each. Lower score represents more similarity.
"""
faiss = dependable_faiss_import()
vector = np.array([embedding], dtype=np.float32)
if self._normalize_L2:
faiss.normalize_L2(vector)
scores, indices = self.index.search(vector, k if filter is None else fetch_k)
docs = []
if filter is not None:
filter_func = self._create_filter_func(filter)
for j, i in enumerate(indices[0]):
if i == -1:
# This happens when not enough docs are returned.
continue
_id = self.index_to_docstore_id[i]
doc = self.docstore.search(_id)
if not isinstance(doc, Document):
raise ValueError(f"Could not find document for id {_id}, got {doc}")
if filter is not None:
if filter_func(doc.metadata):
docs.append((doc, scores[0][j]))
else:
docs.append((doc, scores[0][j]))
score_threshold = kwargs.get("score_threshold")
if score_threshold is not None:
cmp = (
operator.ge
if self.distance_strategy
in (DistanceStrategy.MAX_INNER_PRODUCT, DistanceStrategy.JACCARD)
else operator.le
)
docs = [
(doc, similarity)
for doc, similarity in docs
if cmp(similarity, score_threshold)
]
return docs[:k]
async def asimilarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[Union[Callable, Dict[str, Any]]] = None,
fetch_k: int = 20,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query asynchronously.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]): Filter by metadata.
Defaults to None. If a callable, it must take as input the
metadata dict of Document and return a bool.
fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
**kwargs: kwargs to be passed to similarity search. Can include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns:
List of documents most similar to the query text and L2 distance
in float for each. Lower score represents more similarity.
"""
# This is a temporary workaround to make the similarity search asynchronous.
return await run_in_executor(
None,
self.similarity_search_with_score_by_vector,
embedding,
k=k,
filter=filter,
fetch_k=fetch_k,
**kwargs,
)
def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[Union[Callable, Dict[str, Any]]] = None,
fetch_k: int = 20,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata.
Defaults to None. If a callable, it must take as input the
metadata dict of Document and return a bool.
fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
Returns:
List of documents most similar to the query text with
L2 distance in float. Lower score represents more similarity.
"""
embedding = self._embed_query(query)
docs = self.similarity_search_with_score_by_vector(
embedding,
k,
filter=filter,
fetch_k=fetch_k,
**kwargs,
)
return docs
async def asimilarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[Union[Callable, Dict[str, Any]]] = None,
fetch_k: int = 20,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query asynchronously.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata.
Defaults to None. If a callable, it must take as input the
metadata dict of Document and return a bool.
fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
Returns:
List of documents most similar to the query text with
L2 distance in float. Lower score represents more similarity.
"""
embedding = await self._aembed_query(query)
docs = await self.asimilarity_search_with_score_by_vector(
embedding,
k,
filter=filter,
fetch_k=fetch_k,
**kwargs,
)
return docs
| |
159231
|
_select_relevance_score_fn(self) -> Callable[[float], float]:
"""
The 'correct' relevance function
may differ depending on a few things, including:
- the distance / similarity metric used by the VectorStore
- the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
- embedding dimensionality
- etc.
"""
if self.override_relevance_score_fn is not None:
return self.override_relevance_score_fn
# Default strategy is to rely on distance strategy provided in
# vectorstore constructor
if self.distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT:
return self._max_inner_product_relevance_score_fn
elif self.distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE:
# Default behavior is to use euclidean distance relevancy
return self._euclidean_relevance_score_fn
elif self.distance_strategy == DistanceStrategy.COSINE:
return self._cosine_relevance_score_fn
else:
raise ValueError(
"Unknown distance strategy, must be cosine, max_inner_product,"
" or euclidean"
)
def _similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
filter: Optional[Union[Callable, Dict[str, Any]]] = None,
fetch_k: int = 20,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and their similarity scores on a scale from 0 to 1."""
# Pop score threshold so that only relevancy scores, not raw scores, are
# filtered.
relevance_score_fn = self._select_relevance_score_fn()
if relevance_score_fn is None:
raise ValueError(
"relevance_score_fn must be provided to"
" FAISS constructor to normalize scores"
)
docs_and_scores = self.similarity_search_with_score(
query,
k=k,
filter=filter,
fetch_k=fetch_k,
**kwargs,
)
docs_and_rel_scores = [
(doc, relevance_score_fn(score)) for doc, score in docs_and_scores
]
return docs_and_rel_scores
async def _asimilarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
filter: Optional[Union[Callable, Dict[str, Any]]] = None,
fetch_k: int = 20,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and their similarity scores on a scale from 0 to 1."""
# Pop score threshold so that only relevancy scores, not raw scores, are
# filtered.
relevance_score_fn = self._select_relevance_score_fn()
if relevance_score_fn is None:
raise ValueError(
"relevance_score_fn must be provided to"
" FAISS constructor to normalize scores"
)
docs_and_scores = await self.asimilarity_search_with_score(
query,
k=k,
filter=filter,
fetch_k=fetch_k,
**kwargs,
)
docs_and_rel_scores = [
(doc, relevance_score_fn(score)) for doc, score in docs_and_scores
]
return docs_and_rel_scores
@staticmethod
def _create_filter_func(
filter: Optional[Union[Callable, Dict[str, Any]]],
) -> Callable[[Dict[str, Any]], bool]:
"""
Create a filter function based on the provided filter.
Args:
filter: A callable or a dictionary representing the filter
conditions for documents.
Returns:
Callable[[Dict[str, Any]], bool]: A function that takes Document's metadata
and returns True if it satisfies the filter conditions, otherwise False.
"""
if callable(filter):
return filter
if not isinstance(filter, dict):
raise ValueError(
f"filter must be a dict of metadata or a callable, not {type(filter)}"
)
def filter_func(metadata: Dict[str, Any]) -> bool:
return all(
metadata.get(key) in value
if isinstance(value, list)
else metadata.get(key) == value
for key, value in filter.items() # type: ignore
)
return filter_func
| |
159246
|
def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[Dict[str, Any]] = None,
*,
query_type: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filters to apply to the query. Defaults to None.
query_type: The type of this query. Supported values are "ANN" and "HYBRID".
Returns:
List of Documents most similar to the embedding.
"""
docs_with_score = self.similarity_search_with_score(
query=query,
k=k,
filter=filter,
query_type=query_type,
**kwargs,
)
return [doc for doc, _ in docs_with_score]
def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[Dict[str, Any]] = None,
*,
query_type: Optional[str] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filters to apply to the query. Defaults to None.
query_type: The type of this query. Supported values are "ANN" and "HYBRID".
Returns:
List of Documents most similar to the embedding and score for each.
"""
if self._is_databricks_managed_embeddings():
query_text = query
query_vector = None
else:
assert self.embeddings is not None, "embedding model is required."
# The value for `query_text` needs to be specified only for hybrid search.
if query_type is not None and query_type.upper() == "HYBRID":
query_text = query
else:
query_text = None
query_vector = self.embeddings.embed_query(query)
search_resp = self.index.similarity_search(
columns=self.columns,
query_text=query_text,
query_vector=query_vector,
filters=filter or _alias_filters(kwargs),
num_results=k,
query_type=query_type,
)
return self._parse_search_response(search_resp)
@staticmethod
def _identity_fn(score: float) -> float:
return score
def _select_relevance_score_fn(self) -> Callable[[float], float]:
"""
Databricks Vector search uses a normalized score 1/(1+d) where d
is the L2 distance. Hence, we simply return the identity function.
"""
return self._identity_fn
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, Any]] = None,
*,
query_type: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Filters to apply to the query. Defaults to None.
query_type: The type of this query. Supported values are "ANN" and "HYBRID".
Returns:
List of Documents selected by maximal marginal relevance.
"""
if not self._is_databricks_managed_embeddings():
assert self.embeddings is not None, "embedding model is required."
query_vector = self.embeddings.embed_query(query)
else:
raise ValueError(
"`max_marginal_relevance_search` is not supported for index with "
"Databricks-managed embeddings."
)
docs = self.max_marginal_relevance_search_by_vector(
query_vector,
k,
fetch_k,
lambda_mult=lambda_mult,
filter=filter or _alias_filters(kwargs),
query_type=query_type,
)
return docs
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Any] = None,
*,
query_type: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Filters to apply to the query. Defaults to None.
query_type: The type of this query. Supported values are "ANN" and "HYBRID".
Returns:
List of Documents selected by maximal marginal relevance.
"""
if not self._is_databricks_managed_embeddings():
embedding_column = self._embedding_vector_column_name()
else:
raise ValueError(
"`max_marginal_relevance_search` is not supported for index with "
"Databricks-managed embeddings."
)
search_resp = self.index.similarity_search(
columns=list(set(self.columns + [embedding_column])),
query_text=None,
query_vector=embedding,
filters=filter or _alias_filters(kwargs),
num_results=fetch_k,
query_type=query_type,
)
embeddings_result_index = (
search_resp.get("manifest").get("columns").index({"name": embedding_column})
)
embeddings = [
doc[embeddings_result_index]
for doc in search_resp.get("result").get("data_array")
]
mmr_selected = maximal_marginal_relevance(
np.array(embedding, dtype=np.float32),
embeddings,
k=k,
lambda_mult=lambda_mult,
)
ignore_cols: List = (
[embedding_column] if embedding_column not in self.columns else []
)
candidates = self._parse_search_response(search_resp, ignore_cols=ignore_cols)
selected_results = [r[0] for i, r in enumerate(candidates) if i in mmr_selected]
return selected_results
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[Any] = None,
*,
query_type: Optional[str] = None,
query: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filters to apply to the query. Defaults to None.
query_type: The type of this query. Supported values are "ANN" and "HYBRID".
Returns:
List of Documents most similar to the embedding.
"""
docs_with_score = self.similarity_search_by_vector_with_score(
embedding=embedding,
k=k,
filter=filter,
query_type=query_type,
query=query,
**kwargs,
)
return [doc for doc, _ in docs_with_score]
| |
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|
class Weaviate(VectorStore):
"""`Weaviate` vector store.
To use, you should have the ``weaviate-client`` python package installed.
Example:
.. code-block:: python
import weaviate
from langchain_community.vectorstores import Weaviate
client = weaviate.Client(url=os.environ["WEAVIATE_URL"], ...)
weaviate = Weaviate(client, index_name, text_key)
"""
def __init__(
self,
client: Any,
index_name: str,
text_key: str,
embedding: Optional[Embeddings] = None,
attributes: Optional[List[str]] = None,
relevance_score_fn: Optional[
Callable[[float], float]
] = _default_score_normalizer,
by_text: bool = True,
):
"""Initialize with Weaviate client."""
try:
import weaviate
except ImportError:
raise ImportError(
"Could not import weaviate python package. "
"Please install it with `pip install weaviate-client`."
)
if not isinstance(client, weaviate.Client):
raise ValueError(
f"client should be an instance of weaviate.Client, got {type(client)}"
)
self._client = client
self._index_name = index_name
self._embedding = embedding
self._text_key = text_key
self._query_attrs = [self._text_key]
self.relevance_score_fn = relevance_score_fn
self._by_text = by_text
if attributes is not None:
self._query_attrs.extend(attributes)
@property
def embeddings(self) -> Optional[Embeddings]:
return self._embedding
def _select_relevance_score_fn(self) -> Callable[[float], float]:
return (
self.relevance_score_fn
if self.relevance_score_fn
else _default_score_normalizer
)
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Upload texts with metadata (properties) to Weaviate."""
from weaviate.util import get_valid_uuid
ids = []
embeddings: Optional[List[List[float]]] = None
if self._embedding:
if not isinstance(texts, list):
texts = list(texts)
embeddings = self._embedding.embed_documents(texts)
with self._client.batch as batch:
for i, text in enumerate(texts):
data_properties = {self._text_key: text}
if metadatas is not None:
for key, val in metadatas[i].items():
data_properties[key] = _json_serializable(val)
# Allow for ids (consistent w/ other methods)
# # Or uuids (backwards compatible w/ existing arg)
# If the UUID of one of the objects already exists
# then the existing object will be replaced by the new object.
_id = get_valid_uuid(uuid4())
if "uuids" in kwargs:
_id = kwargs["uuids"][i]
elif "ids" in kwargs:
_id = kwargs["ids"][i]
batch.add_data_object(
data_object=data_properties,
class_name=self._index_name,
uuid=_id,
vector=embeddings[i] if embeddings else None,
tenant=kwargs.get("tenant"),
)
ids.append(_id)
return ids
def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.
"""
if self._by_text:
return self.similarity_search_by_text(query, k, **kwargs)
else:
if self._embedding is None:
raise ValueError(
"_embedding cannot be None for similarity_search when "
"_by_text=False"
)
embedding = self._embedding.embed_query(query)
return self.similarity_search_by_vector(embedding, k, **kwargs)
def similarity_search_by_text(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.
"""
content: Dict[str, Any] = {"concepts": [query]}
if kwargs.get("search_distance"):
content["certainty"] = kwargs.get("search_distance")
query_obj = self._client.query.get(self._index_name, self._query_attrs)
if kwargs.get("where_filter"):
query_obj = query_obj.with_where(kwargs.get("where_filter"))
if kwargs.get("tenant"):
query_obj = query_obj.with_tenant(kwargs.get("tenant"))
if kwargs.get("additional"):
query_obj = query_obj.with_additional(kwargs.get("additional"))
result = query_obj.with_near_text(content).with_limit(k).do()
if "errors" in result:
raise ValueError(f"Error during query: {result['errors']}")
docs = []
for res in result["data"]["Get"][self._index_name]:
text = res.pop(self._text_key)
docs.append(Document(page_content=text, metadata=res))
return docs
def similarity_search_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Document]:
"""Look up similar documents by embedding vector in Weaviate."""
vector = {"vector": embedding}
query_obj = self._client.query.get(self._index_name, self._query_attrs)
if kwargs.get("where_filter"):
query_obj = query_obj.with_where(kwargs.get("where_filter"))
if kwargs.get("tenant"):
query_obj = query_obj.with_tenant(kwargs.get("tenant"))
if kwargs.get("additional"):
query_obj = query_obj.with_additional(kwargs.get("additional"))
result = query_obj.with_near_vector(vector).with_limit(k).do()
if "errors" in result:
raise ValueError(f"Error during query: {result['errors']}")
docs = []
for res in result["data"]["Get"][self._index_name]:
text = res.pop(self._text_key)
docs.append(Document(page_content=text, metadata=res))
return docs
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
if self._embedding is not None:
embedding = self._embedding.embed_query(query)
else:
raise ValueError(
"max_marginal_relevance_search requires a suitable Embeddings object"
)
return self.max_marginal_relevance_search_by_vector(
embedding, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, **kwargs
)
| |
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|
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
vector = {"vector": embedding}
query_obj = self._client.query.get(self._index_name, self._query_attrs)
if kwargs.get("where_filter"):
query_obj = query_obj.with_where(kwargs.get("where_filter"))
if kwargs.get("tenant"):
query_obj = query_obj.with_tenant(kwargs.get("tenant"))
results = (
query_obj.with_additional("vector")
.with_near_vector(vector)
.with_limit(fetch_k)
.do()
)
payload = results["data"]["Get"][self._index_name]
embeddings = [result["_additional"]["vector"] for result in payload]
mmr_selected = maximal_marginal_relevance(
np.array(embedding), embeddings, k=k, lambda_mult=lambda_mult
)
docs = []
for idx in mmr_selected:
text = payload[idx].pop(self._text_key)
payload[idx].pop("_additional")
meta = payload[idx]
docs.append(Document(page_content=text, metadata=meta))
return docs
def similarity_search_with_score(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""
Return list of documents most similar to the query
text and cosine distance in float for each.
Lower score represents more similarity.
"""
if self._embedding is None:
raise ValueError(
"_embedding cannot be None for similarity_search_with_score"
)
content: Dict[str, Any] = {"concepts": [query]}
if kwargs.get("search_distance"):
content["certainty"] = kwargs.get("search_distance")
query_obj = self._client.query.get(self._index_name, self._query_attrs)
if kwargs.get("where_filter"):
query_obj = query_obj.with_where(kwargs.get("where_filter"))
if kwargs.get("tenant"):
query_obj = query_obj.with_tenant(kwargs.get("tenant"))
embedded_query = self._embedding.embed_query(query)
if not self._by_text:
vector = {"vector": embedded_query}
result = (
query_obj.with_near_vector(vector)
.with_limit(k)
.with_additional("vector")
.do()
)
else:
result = (
query_obj.with_near_text(content)
.with_limit(k)
.with_additional("vector")
.do()
)
if "errors" in result:
raise ValueError(f"Error during query: {result['errors']}")
docs_and_scores = []
for res in result["data"]["Get"][self._index_name]:
text = res.pop(self._text_key)
score = np.dot(res["_additional"]["vector"], embedded_query)
docs_and_scores.append((Document(page_content=text, metadata=res), score))
return docs_and_scores
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
*,
client: Optional[weaviate.Client] = None,
weaviate_url: Optional[str] = None,
weaviate_api_key: Optional[str] = None,
batch_size: Optional[int] = None,
index_name: Optional[str] = None,
text_key: str = "text",
by_text: bool = False,
relevance_score_fn: Optional[
Callable[[float], float]
] = _default_score_normalizer,
**kwargs: Any,
) -> Weaviate:
"""Construct Weaviate wrapper from raw documents.
This is a user-friendly interface that:
1. Embeds documents.
2. Creates a new index for the embeddings in the Weaviate instance.
3. Adds the documents to the newly created Weaviate index.
This is intended to be a quick way to get started.
Args:
texts: Texts to add to vector store.
embedding: Text embedding model to use.
metadatas: Metadata associated with each text.
client: weaviate.Client to use.
weaviate_url: The Weaviate URL. If using Weaviate Cloud Services get it
from the ``Details`` tab. Can be passed in as a named param or by
setting the environment variable ``WEAVIATE_URL``. Should not be
specified if client is provided.
weaviate_api_key: The Weaviate API key. If enabled and using Weaviate Cloud
Services, get it from ``Details`` tab. Can be passed in as a named param
or by setting the environment variable ``WEAVIATE_API_KEY``. Should
not be specified if client is provided.
batch_size: Size of batch operations.
index_name: Index name.
text_key: Key to use for uploading/retrieving text to/from vectorstore.
by_text: Whether to search by text or by embedding.
relevance_score_fn: Function for converting whatever distance function the
vector store uses to a relevance score, which is a normalized similarity
score (0 means dissimilar, 1 means similar).
kwargs: Additional named parameters to pass to ``Weaviate.__init__()``.
Example:
.. code-block:: python
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import Weaviate
embeddings = OpenAIEmbeddings()
weaviate = Weaviate.from_texts(
texts,
embeddings,
weaviate_url="http://localhost:8080"
)
"""
try:
from weaviate.util import get_valid_uuid
except ImportError as e:
raise ImportError(
"Could not import weaviate python package. "
"Please install it with `pip install weaviate-client`"
) from e
client = client or _create_weaviate_client(
url=weaviate_url,
api_key=weaviate_api_key,
)
if batch_size:
client.batch.configure(batch_size=batch_size)
index_name = index_name or f"LangChain_{uuid4().hex}"
schema = _default_schema(index_name, text_key)
# check whether the index already exists
if not client.schema.exists(index_name):
client.schema.create_class(schema)
embeddings = embedding.embed_documents(texts) if embedding else None
attributes = list(metadatas[0].keys()) if metadatas else None
# If the UUID of one of the objects already exists
# then the existing object will be replaced by the new object.
if "uuids" in kwargs:
uuids = kwargs.pop("uuids")
else:
uuids = [get_valid_uuid(uuid4()) for _ in range(len(texts))]
with client.batch as batch:
for i, text in enumerate(texts):
data_properties = {
text_key: text,
}
if metadatas is not None:
for key in metadatas[i].keys():
data_properties[key] = metadatas[i][key]
_id = uuids[i]
# if an embedding strategy is not provided, we let
# weaviate create the embedding. Note that this will only
# work if weaviate has been installed with a vectorizer module
# like text2vec-contextionary for example
params = {
"uuid": _id,
"data_object": data_properties,
"class_name": index_name,
}
if embeddings is not None:
params["vector"] = embeddings[i]
batch.add_data_object(**params)
batch.flush()
return cls(
client,
index_name,
text_key,
embedding=embedding,
attributes=attributes,
relevance_score_fn=relevance_score_fn,
by_text=by_text,
**kwargs,
)
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> None:
"""Delete by vector IDs.
Args:
ids: List of ids to delete.
"""
if ids is None:
raise ValueError("No ids provided to delete.")
# TODO: Check if this can be done in bulk
for id in ids:
self._client.data_object.delete(uuid=id)
| |
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|
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
try:
import deeplake
from deeplake import VectorStore as DeepLakeVectorStore
from deeplake.core.fast_forwarding import version_compare
from deeplake.util.exceptions import SampleExtendError
_DEEPLAKE_INSTALLED = True
except ImportError:
_DEEPLAKE_INSTALLED = False
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore
from langchain_community.vectorstores.utils import maximal_marginal_relevance
logger = logging.getLogger(__name__)
class DeepLake(VectorStore):
"""`Activeloop Deep Lake` vector store.
We integrated deeplake's similarity search and filtering for fast prototyping.
Now, it supports Tensor Query Language (TQL) for production use cases
over billion rows.
Why Deep Lake?
- Not only stores embeddings, but also the original data with version control.
- Serverless, doesn't require another service and can be used with major
cloud providers (S3, GCS, etc.)
- More than just a multi-modal vector store. You can use the dataset
to fine-tune your own LLM models.
To use, you should have the ``deeplake`` python package installed.
Example:
.. code-block:: python
from langchain_community.vectorstores import DeepLake
from langchain_community.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = DeepLake("langchain_store", embeddings.embed_query)
"""
_LANGCHAIN_DEFAULT_DEEPLAKE_PATH: str = "./deeplake/"
_valid_search_kwargs = ["lambda_mult"]
def __init__(
self,
dataset_path: str = _LANGCHAIN_DEFAULT_DEEPLAKE_PATH,
token: Optional[str] = None,
embedding: Optional[Embeddings] = None,
embedding_function: Optional[Embeddings] = None,
read_only: bool = False,
ingestion_batch_size: int = 1024,
num_workers: int = 0,
verbose: bool = True,
exec_option: Optional[str] = None,
runtime: Optional[Dict] = None,
index_params: Optional[Dict[str, Union[int, str]]] = None,
**kwargs: Any,
) -> None:
"""Creates an empty DeepLakeVectorStore or loads an existing one.
The DeepLakeVectorStore is located at the specified ``path``.
Examples:
>>> # Create a vector store with default tensors
>>> deeplake_vectorstore = DeepLake(
... path = <path_for_storing_Data>,
... )
>>>
>>> # Create a vector store in the Deep Lake Managed Tensor Database
>>> data = DeepLake(
... path = "hub://org_id/dataset_name",
... runtime = {"tensor_db": True},
... )
Args:
dataset_path (str): The full path for storing to the Deep Lake
Vector Store. It can be:
- a Deep Lake cloud path of the form ``hub://org_id/dataset_name``.
Requires registration with Deep Lake.
- an s3 path of the form ``s3://bucketname/path/to/dataset``.
Credentials are required in either the environment or passed to
the creds argument.
- a local file system path of the form ``./path/to/dataset``
or ``~/path/to/dataset`` or ``path/to/dataset``.
- a memory path of the form ``mem://path/to/dataset`` which doesn't
save the dataset but keeps it in memory instead.
Should be used only for testing as it does not persist.
Defaults to _LANGCHAIN_DEFAULT_DEEPLAKE_PATH.
token (str, optional): Activeloop token, for fetching credentials
to the dataset at path if it is a Deep Lake dataset.
Tokens are normally autogenerated. Optional.
embedding (Embeddings, optional): Function to convert
either documents or query. Optional.
embedding_function (Embeddings, optional): Function to convert
either documents or query. Optional. Deprecated: keeping this
parameter for backwards compatibility.
read_only (bool): Open dataset in read-only mode. Default is False.
ingestion_batch_size (int): During data ingestion, data is divided
into batches. Batch size is the size of each batch.
Default is 1024.
num_workers (int): Number of workers to use during data ingestion.
Default is 0.
verbose (bool): Print dataset summary after each operation.
Default is True.
exec_option (str, optional): Default method for search execution.
It could be either ``"auto"``, ``"python"``, ``"compute_engine"``
or ``"tensor_db"``. Defaults to ``"auto"``.
If None, it's set to "auto".
- ``auto``- Selects the best execution method based on the storage
location of the Vector Store. It is the default option.
- ``python`` - Pure-python implementation that runs on the client and
can be used for data stored anywhere. WARNING: using this option
with big datasets is discouraged because it can lead to
memory issues.
- ``compute_engine`` - Performant C++ implementation of the Deep Lake
Compute Engine that runs on the client and can be used for any data
stored in or connected to Deep Lake. It cannot be used with
in-memory or local datasets.
- ``tensor_db`` - Performant and fully-hosted Managed Tensor Database
that is responsible for storage and query execution. Only available
for data stored in the Deep Lake Managed Database. Store datasets
in this database by specifying runtime = {"tensor_db": True}
during dataset creation.
runtime (Dict, optional): Parameters for creating the Vector Store in
Deep Lake's Managed Tensor Database. Not applicable when loading an
existing Vector Store. To create a Vector Store in the Managed Tensor
Database, set `runtime = {"tensor_db": True}`.
index_params (Optional[Dict[str, Union[int, str]]], optional): Dictionary
containing information about vector index that will be created. Defaults
to None, which will utilize ``DEFAULT_VECTORSTORE_INDEX_PARAMS`` from
``deeplake.constants``. The specified key-values override the default
ones.
- threshold: The threshold for the dataset size above which an index
will be created for the embedding tensor. When the threshold value
is set to -1, index creation is turned off. Defaults to -1, which
turns off the index.
- distance_metric: This key specifies the method of calculating the
distance between vectors when creating the vector database (VDB)
index. It can either be a string that corresponds to a member of
the DistanceType enumeration, or the string value itself.
- If no value is provided, it defaults to "L2".
- "L2" corresponds to DistanceType.L2_NORM.
- "COS" corresponds to DistanceType.COSINE_SIMILARITY.
- additional_params: Additional parameters for fine-tuning the index.
**kwargs: Other optional keyword arguments.
Raises:
ValueError: If some condition is not met.
"""
self.ingestion_batch_size = ingestion_batch_size
self.num_workers = num_workers
self.verbose = verbose
if _DEEPLAKE_INSTALLED is False:
raise ImportError(
"Could not import deeplake python package. "
"Please install it with `pip install deeplake[enterprise]`."
)
if (
runtime == {"tensor_db": True}
and version_compare(deeplake.__version__, "3.6.7") == -1
):
raise ImportError(
"To use tensor_db option you need to update deeplake to `3.6.7` or "
"higher. "
f"Currently installed deeplake version is {deeplake.__version__}. "
)
self.dataset_path = dataset_path
if embedding_function:
logger.warning(
"Using embedding function is deprecated and will be removed "
"in the future. Please use embedding instead."
)
self.vectorstore = DeepLakeVectorStore(
path=self.dataset_path,
embedding_function=embedding_function or embedding,
read_only=read_only,
token=token,
exec_option=exec_option,
verbose=verbose,
runtime=runtime,
index_params=index_params,
**kwargs,
)
self._embedding_function = embedding_function or embedding
self._id_tensor_name = "ids" if "ids" in self.vectorstore.tensors() else "id"
@property
def embeddings(self) -> Optional[Embeddings]:
return self._embedding_function
| |
159287
|
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Examples:
>>> ids = deeplake_vectorstore.add_texts(
... texts = <list_of_texts>,
... metadatas = <list_of_metadata_jsons>,
... ids = <list_of_ids>,
... )
Args:
texts (Iterable[str]): Texts to add to the vectorstore.
metadatas (Optional[List[dict]], optional): Optional list of metadatas.
ids (Optional[List[str]], optional): Optional list of IDs.
embedding_function (Optional[Embeddings], optional): Embedding function
to use to convert the text into embeddings.
**kwargs (Any): Any additional keyword arguments passed is not supported
by this method.
Returns:
List[str]: List of IDs of the added texts.
"""
self._validate_kwargs(kwargs, "add_texts")
kwargs = {}
if ids:
if self._id_tensor_name == "ids": # for backwards compatibility
kwargs["ids"] = ids
else:
kwargs["id"] = ids
if metadatas is None:
metadatas = [{}] * len(list(texts))
if not isinstance(texts, list):
texts = list(texts)
if texts is None:
raise ValueError("`texts` parameter shouldn't be None.")
elif len(texts) == 0:
raise ValueError("`texts` parameter shouldn't be empty.")
try:
return self.vectorstore.add(
text=texts,
metadata=metadatas,
embedding_data=texts,
embedding_tensor="embedding",
embedding_function=self._embedding_function.embed_documents, # type: ignore
return_ids=True,
**kwargs,
)
except SampleExtendError as e:
if "Failed to append a sample to the tensor 'metadata'" in str(e):
msg = (
"**Hint: You might be using invalid type of argument in "
"document loader (e.g. 'pathlib.PosixPath' instead of 'str')"
)
raise ValueError(e.args[0] + "\n\n" + msg)
else:
raise e
def _search_tql(
self,
tql: Optional[str],
exec_option: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Function for performing tql_search.
Args:
tql (str): TQL Query string for direct evaluation.
Available only for `compute_engine` and `tensor_db`.
exec_option (str, optional): Supports 3 ways to search.
Could be "python", "compute_engine" or "tensor_db". Default is "python".
- ``python`` - Pure-python implementation for the client.
WARNING: not recommended for big datasets due to potential memory
issues.
- ``compute_engine`` - C++ implementation of Deep Lake Compute
Engine for the client. Not for in-memory or local datasets.
- ``tensor_db`` - Hosted Managed Tensor Database for storage
and query execution. Only for data in Deep Lake Managed Database.
Use runtime = {"db_engine": True} during dataset creation.
return_score (bool): Return score with document. Default is False.
Returns:
Tuple[List[Document], List[Tuple[Document, float]]] - A tuple of two lists.
The first list contains Documents, and the second list contains
tuples of Document and float score.
Raises:
ValueError: If return_score is True but some condition is not met.
"""
result = self.vectorstore.search(
query=tql,
exec_option=exec_option,
)
metadatas = result["metadata"]
texts = result["text"]
docs = [
Document(
page_content=text,
metadata=metadata,
)
for text, metadata in zip(texts, metadatas)
]
if kwargs:
unsupported_argument = next(iter(kwargs))
if kwargs[unsupported_argument] is not False:
raise ValueError(
f"specifying {unsupported_argument} is "
"not supported with tql search."
)
return docs
| |
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def _search(
self,
query: Optional[str] = None,
embedding: Optional[Union[List[float], np.ndarray]] = None,
embedding_function: Optional[Callable] = None,
k: int = 4,
distance_metric: Optional[str] = None,
use_maximal_marginal_relevance: bool = False,
fetch_k: Optional[int] = 20,
filter: Optional[Union[Dict, Callable]] = None,
return_score: bool = False,
exec_option: Optional[str] = None,
deep_memory: bool = False,
**kwargs: Any,
) -> Any[List[Document], List[Tuple[Document, float]]]:
"""
Return docs similar to query.
Args:
query (str, optional): Text to look up similar docs.
embedding (Union[List[float], np.ndarray], optional): Query's embedding.
embedding_function (Callable, optional): Function to convert `query`
into embedding.
k (int): Number of Documents to return.
distance_metric (Optional[str], optional): `L2` for Euclidean, `L1` for
Nuclear, `max` for L-infinity distance, `cos` for cosine similarity,
'dot' for dot product.
filter (Union[Dict, Callable], optional): Additional filter prior
to the embedding search.
- ``Dict`` - Key-value search on tensors of htype json, on an
AND basis (a sample must satisfy all key-value filters to be True)
Dict = {"tensor_name_1": {"key": value},
"tensor_name_2": {"key": value}}
- ``Function`` - Any function compatible with `deeplake.filter`.
use_maximal_marginal_relevance (bool): Use maximal marginal relevance.
fetch_k (int): Number of Documents for MMR algorithm.
return_score (bool): Return the score.
exec_option (str, optional): Supports 3 ways to perform searching.
Could be "python", "compute_engine" or "tensor_db".
- ``python`` - Pure-python implementation for the client.
WARNING: not recommended for big datasets.
- ``compute_engine`` - C++ implementation of Deep Lake Compute
Engine for the client. Not for in-memory or local datasets.
- ``tensor_db`` - Hosted Managed Tensor Database for storage
and query execution. Only for data in Deep Lake Managed Database.
Use runtime = {"db_engine": True} during dataset creation.
deep_memory (bool): Whether to use the Deep Memory model for improving
search results. Defaults to False if deep_memory is not specified in
the Vector Store initialization. If True, the distance metric is set
to "deepmemory_distance", which represents the metric with which the
model was trained. The search is performed using the Deep Memory model.
If False, the distance metric is set to "COS" or whatever distance
metric user specifies.
kwargs: Additional keyword arguments.
Returns:
List of Documents by the specified distance metric,
if return_score True, return a tuple of (Document, score)
Raises:
ValueError: if both `embedding` and `embedding_function` are not specified.
"""
if kwargs.get("tql_query"):
logger.warning("`tql_query` is deprecated. Please use `tql` instead.")
kwargs["tql"] = kwargs.pop("tql_query")
if kwargs.get("tql"):
return self._search_tql(
tql=kwargs["tql"],
exec_option=exec_option,
return_score=return_score,
embedding=embedding,
embedding_function=embedding_function,
distance_metric=distance_metric,
use_maximal_marginal_relevance=use_maximal_marginal_relevance,
filter=filter,
)
self._validate_kwargs(kwargs, "search")
if embedding_function:
if isinstance(embedding_function, Embeddings):
_embedding_function = embedding_function.embed_query
else:
_embedding_function = embedding_function
elif self._embedding_function:
_embedding_function = self._embedding_function.embed_query
else:
_embedding_function = None
if embedding is None:
if _embedding_function is None:
raise ValueError(
"Either `embedding` or `embedding_function` needs to be"
" specified."
)
embedding = _embedding_function(query) if query else None
if isinstance(embedding, list):
embedding = np.array(embedding, dtype=np.float32)
if len(embedding.shape) > 1:
embedding = embedding[0]
result = self.vectorstore.search(
embedding=embedding,
k=fetch_k if use_maximal_marginal_relevance else k,
distance_metric=distance_metric,
filter=filter,
exec_option=exec_option,
return_tensors=["embedding", "metadata", "text", self._id_tensor_name],
deep_memory=deep_memory,
)
scores = result["score"]
embeddings = result["embedding"]
metadatas = result["metadata"]
texts = result["text"]
if use_maximal_marginal_relevance:
lambda_mult = kwargs.get("lambda_mult", 0.5)
indices = maximal_marginal_relevance( # type: ignore
embedding, # type: ignore
embeddings,
k=min(k, len(texts)),
lambda_mult=lambda_mult,
)
scores = [scores[i] for i in indices]
texts = [texts[i] for i in indices]
metadatas = [metadatas[i] for i in indices]
docs = [
Document(
page_content=text,
metadata=metadata,
)
for text, metadata in zip(texts, metadatas)
]
if return_score:
if not isinstance(scores, list):
scores = [scores]
return [(doc, score) for doc, score in zip(docs, scores)]
return docs
def similarity_search(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Document]:
"""
Return docs most similar to query.
Examples:
>>> # Search using an embedding
>>> data = vector_store.similarity_search(
... query=<your_query>,
... k=<num_items>,
... exec_option=<preferred_exec_option>,
... )
>>> # Run tql search:
>>> data = vector_store.similarity_search(
... query=None,
... tql="SELECT * WHERE id == <id>",
... exec_option="compute_engine",
... )
Args:
k (int): Number of Documents to return. Defaults to 4.
query (str): Text to look up similar documents.
kwargs: Additional keyword arguments include:
embedding (Callable): Embedding function to use. Defaults to None.
distance_metric (str): 'L2' for Euclidean, 'L1' for Nuclear, 'max'
for L-infinity, 'cos' for cosine, 'dot' for dot product.
Defaults to 'L2'.
filter (Union[Dict, Callable], optional): Additional filter
before embedding search.
- Dict: Key-value search on tensors of htype json,
(sample must satisfy all key-value filters)
Dict = {"tensor_1": {"key": value}, "tensor_2": {"key": value}}
- Function: Compatible with `deeplake.filter`.
Defaults to None.
exec_option (str): Supports 3 ways to perform searching.
'python', 'compute_engine', or 'tensor_db'. Defaults to 'python'.
- 'python': Pure-python implementation for the client.
WARNING: not recommended for big datasets.
- 'compute_engine': C++ implementation of the Compute Engine for
the client. Not for in-memory or local datasets.
- 'tensor_db': Managed Tensor Database for storage and query.
Only for data in Deep Lake Managed Database.
Use `runtime = {"db_engine": True}` during dataset creation.
deep_memory (bool): Whether to use the Deep Memory model for improving
search results. Defaults to False if deep_memory is not specified
in the Vector Store initialization. If True, the distance metric
is set to "deepmemory_distance", which represents the metric with
which the model was trained. The search is performed using the Deep
Memory model. If False, the distance metric is set to "COS" or
whatever distance metric user specifies.
Returns:
List[Document]: List of Documents most similar to the query vector.
"""
return self._search(
query=query,
k=k,
use_maximal_marginal_relevance=False,
return_score=False,
**kwargs,
)
| |
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|
def similarity_search_by_vector(
self,
embedding: Union[List[float], np.ndarray],
k: int = 4,
**kwargs: Any,
) -> List[Document]:
"""
Return docs most similar to embedding vector.
Examples:
>>> # Search using an embedding
>>> data = vector_store.similarity_search_by_vector(
... embedding=<your_embedding>,
... k=<num_items_to_return>,
... exec_option=<preferred_exec_option>,
... )
Args:
embedding (Union[List[float], np.ndarray]):
Embedding to find similar docs.
k (int): Number of Documents to return. Defaults to 4.
kwargs: Additional keyword arguments including:
filter (Union[Dict, Callable], optional):
Additional filter before embedding search.
- ``Dict`` - Key-value search on tensors of htype json. True
if all key-value filters are satisfied.
Dict = {"tensor_name_1": {"key": value},
"tensor_name_2": {"key": value}}
- ``Function`` - Any function compatible with
`deeplake.filter`.
Defaults to None.
exec_option (str): Options for search execution include
"python", "compute_engine", or "tensor_db". Defaults to
"python".
- "python" - Pure-python implementation running on the client.
Can be used for data stored anywhere. WARNING: using this
option with big datasets is discouraged due to potential
memory issues.
- "compute_engine" - Performant C++ implementation of the Deep
Lake Compute Engine. Runs on the client and can be used for
any data stored in or connected to Deep Lake. It cannot be
used with in-memory or local datasets.
- "tensor_db" - Performant, fully-hosted Managed Tensor Database.
Responsible for storage and query execution. Only available
for data stored in the Deep Lake Managed Database.
To store datasets in this database, specify
`runtime = {"db_engine": True}` during dataset creation.
distance_metric (str): `L2` for Euclidean, `L1` for Nuclear,
`max` for L-infinity distance, `cos` for cosine similarity,
'dot' for dot product. Defaults to `L2`.
deep_memory (bool): Whether to use the Deep Memory model for improving
search results. Defaults to False if deep_memory is not specified
in the Vector Store initialization. If True, the distance metric
is set to "deepmemory_distance", which represents the metric with
which the model was trained. The search is performed using the Deep
Memory model. If False, the distance metric is set to "COS" or
whatever distance metric user specifies.
Returns:
List[Document]: List of Documents most similar to the query vector.
"""
return self._search(
embedding=embedding,
k=k,
use_maximal_marginal_relevance=False,
return_score=False,
**kwargs,
)
def similarity_search_with_score(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""
Run similarity search with Deep Lake with distance returned.
Examples:
>>> data = vector_store.similarity_search_with_score(
... query=<your_query>,
... embedding=<your_embedding_function>
... k=<number_of_items_to_return>,
... exec_option=<preferred_exec_option>,
... )
Args:
query (str): Query text to search for.
k (int): Number of results to return. Defaults to 4.
kwargs: Additional keyword arguments. Some of these arguments are:
distance_metric: `L2` for Euclidean, `L1` for Nuclear, `max` L-infinity
distance, `cos` for cosine similarity, 'dot' for dot product.
Defaults to `L2`.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
embedding_function (Callable): Embedding function to use. Defaults
to None.
exec_option (str): DeepLakeVectorStore supports 3 ways to perform
searching. It could be either "python", "compute_engine" or
"tensor_db". Defaults to "python".
- "python" - Pure-python implementation running on the client.
Can be used for data stored anywhere. WARNING: using this
option with big datasets is discouraged due to potential
memory issues.
- "compute_engine" - Performant C++ implementation of the Deep
Lake Compute Engine. Runs on the client and can be used for
any data stored in or connected to Deep Lake. It cannot be used
with in-memory or local datasets.
- "tensor_db" - Performant, fully-hosted Managed Tensor Database.
Responsible for storage and query execution. Only available for
data stored in the Deep Lake Managed Database. To store datasets
in this database, specify `runtime = {"db_engine": True}`
during dataset creation.
deep_memory (bool): Whether to use the Deep Memory model for improving
search results. Defaults to False if deep_memory is not specified
in the Vector Store initialization. If True, the distance metric
is set to "deepmemory_distance", which represents the metric with
which the model was trained. The search is performed using the Deep
Memory model. If False, the distance metric is set to "COS" or
whatever distance metric user specifies.
Returns:
List[Tuple[Document, float]]: List of documents most similar to the query
text with distance in float."""
return self._search(
query=query,
k=k,
return_score=True,
**kwargs,
)
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
exec_option: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""
Return docs selected using the maximal marginal relevance. Maximal marginal
relevance optimizes for similarity to query AND diversity among selected docs.
Examples:
>>> data = vector_store.max_marginal_relevance_search_by_vector(
... embedding=<your_embedding>,
... fetch_k=<elements_to_fetch_before_mmr_search>,
... k=<number_of_items_to_return>,
... exec_option=<preferred_exec_option>,
... )
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch for MMR algorithm.
lambda_mult: Number between 0 and 1 determining the degree of diversity.
0 corresponds to max diversity and 1 to min diversity. Defaults to 0.5.
exec_option (str): DeepLakeVectorStore supports 3 ways for searching.
Could be "python", "compute_engine" or "tensor_db". Defaults to
"python".
- "python" - Pure-python implementation running on the client.
Can be used for data stored anywhere. WARNING: using this
option with big datasets is discouraged due to potential
memory issues.
- "compute_engine" - Performant C++ implementation of the Deep
Lake Compute Engine. Runs on the client and can be used for
any data stored in or connected to Deep Lake. It cannot be used
with in-memory or local datasets.
- "tensor_db" - Performant, fully-hosted Managed Tensor Database.
Responsible for storage and query execution. Only available for
data stored in the Deep Lake Managed Database. To store datasets
in this database, specify `runtime = {"db_engine": True}`
during dataset creation.
deep_memory (bool): Whether to use the Deep Memory model for improving
search results. Defaults to False if deep_memory is not specified
in the Vector Store initialization. If True, the distance metric
is set to "deepmemory_distance", which represents the metric with
which the model was trained. The search is performed using the Deep
Memory model. If False, the distance metric is set to "COS" or
whatever distance metric user specifies.
kwargs: Additional keyword arguments.
Returns:
List[Documents] - A list of documents.
"""
return self._search(
embedding=embedding,
k=k,
fetch_k=fetch_k,
use_maximal_marginal_relevance=True,
lambda_mult=lambda_mult,
exec_option=exec_option,
**kwargs,
)
| |
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|
def _similarity_search_with_score(
self,
embeddings: List[float],
k: int = 4,
pre_filter: Optional[Dict] = None,
with_embedding: bool = False,
) -> List[Tuple[Document, float]]:
query = "SELECT "
# If limit_offset_clause is not specified, add TOP clause
if pre_filter is None or pre_filter.get("limit_offset_clause") is None:
query += "TOP @limit "
query += (
"c.id, c.{}, c.text, c.metadata, "
"VectorDistance(c.@embeddingKey, @embeddings) AS SimilarityScore FROM c"
)
# Add where_clause if specified
if pre_filter is not None and pre_filter.get("where_clause") is not None:
query += " {}".format(pre_filter["where_clause"])
query += " ORDER BY VectorDistance(c.@embeddingKey, @embeddings)"
# Add limit_offset_clause if specified
if pre_filter is not None and pre_filter.get("limit_offset_clause") is not None:
query += " {}".format(pre_filter["limit_offset_clause"])
parameters = [
{"name": "@limit", "value": k},
{"name": "@embeddingKey", "value": self._embedding_key},
{"name": "@embeddings", "value": embeddings},
]
docs_and_scores = []
items = list(
self._container.query_items(
query=query, parameters=parameters, enable_cross_partition_query=True
)
)
for item in items:
text = item["text"]
metadata = item["metadata"]
score = item["SimilarityScore"]
if with_embedding:
metadata[self._embedding_key] = item[self._embedding_key]
docs_and_scores.append(
(Document(page_content=text, metadata=metadata), score)
)
return docs_and_scores
def similarity_search_with_score(
self,
query: str,
k: int = 4,
pre_filter: Optional[Dict] = None,
with_embedding: bool = False,
) -> List[Tuple[Document, float]]:
embeddings = self._embedding.embed_query(query)
docs_and_scores = self._similarity_search_with_score(
embeddings=embeddings,
k=k,
pre_filter=pre_filter,
with_embedding=with_embedding,
)
return docs_and_scores
def similarity_search(
self,
query: str,
k: int = 4,
pre_filter: Optional[Dict] = None,
with_embedding: bool = False,
**kwargs: Any,
) -> List[Document]:
docs_and_scores = self.similarity_search_with_score(
query,
k=k,
pre_filter=pre_filter,
with_embedding=with_embedding,
)
return [doc for doc, _ in docs_and_scores]
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
# Retrieves the docs with similarity scores
pre_filter = {}
with_embedding = False
if kwargs["pre_filter"]:
pre_filter = kwargs["pre_filter"]
if kwargs["with_embedding"]:
with_embedding = kwargs["with_embedding"]
docs = self._similarity_search_with_score(
embeddings=embedding,
k=fetch_k,
pre_filter=pre_filter,
with_embedding=with_embedding,
)
# Re-ranks the docs using MMR
mmr_doc_indexes = maximal_marginal_relevance(
np.array(embedding),
[doc.metadata[self._embedding_key] for doc, _ in docs],
k=k,
lambda_mult=lambda_mult,
)
mmr_docs = [docs[i][0] for i in mmr_doc_indexes]
return mmr_docs
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
# compute the embeddings vector from the query string
pre_filter = {}
with_embedding = False
if kwargs["pre_filter"]:
pre_filter = kwargs["pre_filter"]
if kwargs["with_embedding"]:
with_embedding = kwargs["with_embedding"]
embeddings = self._embedding.embed_query(query)
docs = self.max_marginal_relevance_search_by_vector(
embeddings,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
pre_filter=pre_filter,
with_embedding=with_embedding,
)
return docs
| |
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|
@override
def similarity_search_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Document]:
"""Returns the k most similar documents to the given embedding vector
Args:
embedding: The embedding vector to search for
k: The number of similar documents to return
Returns:
List of Document objects ordered by decreasing similarity to the query.
"""
from aperturedb.Descriptors import Descriptors
descriptors = Descriptors(self.connection)
start_time = time.time()
descriptors.find_similar(
set=self.descriptor_set, vector=embedding, k_neighbors=k
)
self.logger.info(
f"ApertureDB similarity search took {time.time() - start_time} seconds"
)
return [self._descriptor_to_document(d) for d in descriptors]
@override
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Returns similar documents to the query that also have diversity
This algorithm balances relevance and diversity in the search results.
Args:
query: Query string to search for.
k: Number of results to return.
fetch_k: Number of results to fetch.
lambda_mult: Lambda multiplier for MMR.
Returns:
List of Document objects ordered by decreasing similarity/diversty.
"""
self.logger.info(f"Max Marginal Relevance search for query: {query}")
embedding = self.embedding_function.embed_query(query)
return self.max_marginal_relevance_search_by_vector(
embedding, k, fetch_k, lambda_mult, **kwargs
)
@override
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Returns similar documents to the vector that also have diversity
This algorithm balances relevance and diversity in the search results.
Args:
embedding: Embedding vector to search for.
k: Number of results to return.
fetch_k: Number of results to fetch.
lambda_mult: Lambda multiplier for MMR.
Returns:
List of Document objects ordered by decreasing similarity/diversty.
"""
from aperturedb.Descriptors import Descriptors
descriptors = Descriptors(self.connection)
start_time = time.time()
descriptors.find_similar_mmr(
set=self.descriptor_set,
vector=embedding,
k_neighbors=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
)
self.logger.info(
f"ApertureDB similarity search mmr took {time.time() - start_time} seconds"
)
return [self._descriptor_to_document(d) for d in descriptors]
@classmethod
@override
def from_texts(
cls: Type[ApertureDB],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> ApertureDB:
"""Creates a new vectorstore from a list of texts
Args:
texts: List of text strings
embedding: Embeddings object as for constructing the vectorstore
metadatas: Optional list of metadatas associated with the texts.
kwargs: Additional arguments to pass to the constructor
"""
store = cls(embeddings=embedding, **kwargs)
store.add_texts(texts, metadatas)
return store
@classmethod
@override
def from_documents(
cls: Type[ApertureDB],
documents: List[Document],
embedding: Embeddings,
**kwargs: Any,
) -> ApertureDB:
"""Creates a new vectorstore from a list of documents
Args:
documents: List of Document objects
embedding: Embeddings object as for constructing the vectorstore
metadatas: Optional list of metadatas associated with the texts.
kwargs: Additional arguments to pass to the constructor
"""
store = cls(embeddings=embedding, **kwargs)
store.add_documents(documents)
return store
@classmethod
def delete_vectorstore(class_, descriptor_set: str) -> None:
"""Deletes a vectorstore and all its data from the database
Args:
descriptor_set: The name of the descriptor set to delete
"""
from aperturedb.Utils import Utils, create_connector
db = create_connector()
utils = Utils(db)
utils.remove_descriptorset(descriptor_set)
@classmethod
def list_vectorstores(class_) -> None:
"""Returns a list of all vectorstores in the database
Returns:
List of descriptor sets with properties
"""
from aperturedb.Utils import create_connector
db = create_connector()
query = [
{
"FindDescriptorSet": {
# Return all properties
"results": {"all_properties": True},
"engines": True,
"metrics": True,
"dimensions": True,
}
}
]
response, _ = db.query(query)
assert db.last_query_ok(), response
return response[0]["FindDescriptorSet"]["entities"]
def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
"""Add or update documents in the vectorstore.
Args:
documents: Documents to add to the vectorstore.
kwargs: Additional keyword arguments.
if kwargs contains ids and documents contain ids,
the ids in the kwargs will receive precedence.
Returns:
List of IDs of the added texts.
Raises:
ValueError: If the number of ids does not match the number of documents.
"""
if "ids" in kwargs:
ids = kwargs.pop("ids")
if ids and len(ids) != len(documents):
raise ValueError(
"The number of ids must match the number of documents. "
"Got {len(ids)} ids and {len(documents)} documents."
)
documents_ = []
for id_, document in zip(ids, documents):
doc_with_id = Document(
page_content=document.page_content,
metadata=document.metadata,
id=id_,
)
documents_.append(doc_with_id)
else:
documents_ = documents
# If upsert has been implemented, we can use it to add documents
return self.upsert(documents_, **kwargs)["succeeded"]
def upsert(self, items: Sequence[Document], /, **kwargs: Any) -> UpsertResponse:
"""Insert or update items
Updating documents is dependent on the documents' `id` attribute.
Args:
items: List of Document objects to upsert
Returns:
UpsertResponse object with succeeded and failed
"""
# For now, simply delete and add
# We could do something more efficient to update metadata,
# but we don't support changing the embedding of a descriptor.
from aperturedb.ParallelLoader import ParallelLoader
ids_to_delete: List[str] = [
item.id for item in items if hasattr(item, "id") and item.id is not None
]
if ids_to_delete:
self.delete(ids_to_delete)
texts = [doc.page_content for doc in items]
metadatas = [
doc.metadata if getattr(doc, "metadata", None) is not None else {}
for doc in items
]
embeddings = self.embedding_function.embed_documents(texts)
ids: List[str] = [
doc.id if hasattr(doc, "id") and doc.id is not None else str(uuid.uuid4())
for doc in items
]
data = []
for text, embedding, metadata, unique_id in zip(
texts, embeddings, metadatas, ids
):
properties = {PROPERTY_PREFIX + k: v for k, v in metadata.items()}
properties[TEXT_PROPERTY] = text
properties[UNIQUEID_PROPERTY] = unique_id
command = {
"AddDescriptor": {
"set": self.descriptor_set,
"properties": properties,
}
}
query = [command]
blobs = [np.array(embedding, dtype=np.float32).tobytes()]
data.append((query, blobs))
loader = ParallelLoader(self.connection)
loader.ingest(data, batchsize=BATCHSIZE)
return UpsertResponse(succeeded=ids, failed=[])
| |
159313
|
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[dict]] = None,
user_id: Optional[str] = None,
app_id: Optional[str] = None,
number_of_docs: Optional[int] = None,
pat: Optional[str] = None,
token: Optional[str] = None,
**kwargs: Any,
) -> Clarifai:
"""Create a Clarifai vectorstore from a list of texts.
Args:
user_id (str): User ID.
app_id (str): App ID.
texts (List[str]): List of texts to add.
number_of_docs (Optional[int]): Number of documents
to return during vector search. Defaults to None.
pat (Optional[str], optional): Personal access token.
Defaults to None.
token (Optional[str], optional): Session token. Defaults to None.
metadatas (Optional[List[dict]]): Optional list
of metadatas. Defaults to None.
kwargs: Additional keyword arguments to be passed to the Search.
Returns:
Clarifai: Clarifai vectorstore.
"""
clarifai_vector_db = cls(
user_id=user_id,
app_id=app_id,
number_of_docs=number_of_docs,
pat=pat,
token=token,
**kwargs,
)
clarifai_vector_db.add_texts(texts=texts, metadatas=metadatas)
return clarifai_vector_db
@classmethod
def from_documents(
cls,
documents: List[Document],
embedding: Optional[Embeddings] = None,
user_id: Optional[str] = None,
app_id: Optional[str] = None,
number_of_docs: Optional[int] = None,
pat: Optional[str] = None,
token: Optional[str] = None,
**kwargs: Any,
) -> Clarifai:
"""Create a Clarifai vectorstore from a list of documents.
Args:
user_id (str): User ID.
app_id (str): App ID.
documents (List[Document]): List of documents to add.
number_of_docs (Optional[int]): Number of documents
to return during vector search. Defaults to None.
pat (Optional[str], optional): Personal access token. Defaults to None.
token (Optional[str], optional): Session token. Defaults to None.
kwargs: Additional keyword arguments to be passed to the Search.
Returns:
Clarifai: Clarifai vectorstore.
"""
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return cls.from_texts(
user_id=user_id,
app_id=app_id,
texts=texts,
number_of_docs=number_of_docs,
pat=pat,
metadatas=metadatas,
token=token,
**kwargs,
)
| |
159318
|
from __future__ import annotations
import base64
import logging
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
)
import numpy as np
from langchain_core._api import deprecated
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.utils import xor_args
from langchain_core.vectorstores import VectorStore
from langchain_community.vectorstores.utils import maximal_marginal_relevance
if TYPE_CHECKING:
import chromadb
import chromadb.config
from chromadb.api.types import ID, OneOrMany, Where, WhereDocument
logger = logging.getLogger()
DEFAULT_K = 4 # Number of Documents to return.
def _results_to_docs(results: Any) -> List[Document]:
return [doc for doc, _ in _results_to_docs_and_scores(results)]
def _results_to_docs_and_scores(results: Any) -> List[Tuple[Document, float]]:
return [
# TODO: Chroma can do batch querying,
# we shouldn't hard code to the 1st result
(Document(page_content=result[0], metadata=result[1] or {}), result[2])
for result in zip(
results["documents"][0],
results["metadatas"][0],
results["distances"][0],
)
]
@deprecated(since="0.2.9", removal="1.0", alternative_import="langchain_chroma.Chroma")
class Chroma(VectorStore):
"""`ChromaDB` vector store.
To use, you should have the ``chromadb`` python package installed.
Example:
.. code-block:: python
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = Chroma("langchain_store", embeddings)
"""
_LANGCHAIN_DEFAULT_COLLECTION_NAME: str = "langchain"
def __init__(
self,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
embedding_function: Optional[Embeddings] = None,
persist_directory: Optional[str] = None,
client_settings: Optional[chromadb.config.Settings] = None,
collection_metadata: Optional[Dict] = None,
client: Optional[chromadb.Client] = None,
relevance_score_fn: Optional[Callable[[float], float]] = None,
) -> None:
"""Initialize with a Chroma client."""
try:
import chromadb
import chromadb.config
except ImportError:
raise ImportError(
"Could not import chromadb python package. "
"Please install it with `pip install chromadb`."
)
if client is not None:
self._client_settings = client_settings
self._client = client
self._persist_directory = persist_directory
else:
if client_settings:
# If client_settings is provided with persist_directory specified,
# then it is "in-memory and persisting to disk" mode.
client_settings.persist_directory = (
persist_directory or client_settings.persist_directory
)
if client_settings.persist_directory is not None:
# Maintain backwards compatibility with chromadb < 0.4.0
major, minor, _ = chromadb.__version__.split(".")
if int(major) == 0 and int(minor) < 4:
client_settings.chroma_db_impl = "duckdb+parquet"
_client_settings = client_settings
elif persist_directory:
# Maintain backwards compatibility with chromadb < 0.4.0
major, minor, _ = chromadb.__version__.split(".")
if int(major) == 0 and int(minor) < 4:
_client_settings = chromadb.config.Settings(
chroma_db_impl="duckdb+parquet",
)
else:
_client_settings = chromadb.config.Settings(is_persistent=True)
_client_settings.persist_directory = persist_directory
else:
_client_settings = chromadb.config.Settings()
self._client_settings = _client_settings
self._client = chromadb.Client(_client_settings)
self._persist_directory = (
_client_settings.persist_directory or persist_directory
)
self._embedding_function = embedding_function
self._collection = self._client.get_or_create_collection(
name=collection_name,
embedding_function=None,
metadata=collection_metadata,
)
self.override_relevance_score_fn = relevance_score_fn
@property
def embeddings(self) -> Optional[Embeddings]:
return self._embedding_function
@xor_args(("query_texts", "query_embeddings"))
def __query_collection(
self,
query_texts: Optional[List[str]] = None,
query_embeddings: Optional[List[List[float]]] = None,
n_results: int = 4,
where: Optional[Dict[str, str]] = None,
where_document: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Query the chroma collection."""
try:
import chromadb # noqa: F401
except ImportError:
raise ImportError(
"Could not import chromadb python package. "
"Please install it with `pip install chromadb`."
)
return self._collection.query(
query_texts=query_texts,
query_embeddings=query_embeddings,
n_results=n_results,
where=where,
where_document=where_document,
**kwargs,
)
def encode_image(self, uri: str) -> str:
"""Get base64 string from image URI."""
with open(uri, "rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
def add_images(
self,
uris: List[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more images through the embeddings and add to the vectorstore.
Args:
uris List[str]: File path to the image.
metadatas (Optional[List[dict]], optional): Optional list of metadatas.
ids (Optional[List[str]], optional): Optional list of IDs.
Returns:
List[str]: List of IDs of the added images.
"""
# Map from uris to b64 encoded strings
b64_texts = [self.encode_image(uri=uri) for uri in uris]
# Populate IDs
if ids is None:
ids = [str(uuid.uuid4()) for _ in uris]
embeddings = None
# Set embeddings
if self._embedding_function is not None and hasattr(
self._embedding_function, "embed_image"
):
embeddings = self._embedding_function.embed_image(uris=uris)
if metadatas:
# fill metadatas with empty dicts if somebody
# did not specify metadata for all images
length_diff = len(uris) - len(metadatas)
if length_diff:
metadatas = metadatas + [{}] * length_diff
empty_ids = []
non_empty_ids = []
for idx, m in enumerate(metadatas):
if m:
non_empty_ids.append(idx)
else:
empty_ids.append(idx)
if non_empty_ids:
metadatas = [metadatas[idx] for idx in non_empty_ids]
images_with_metadatas = [b64_texts[idx] for idx in non_empty_ids]
embeddings_with_metadatas = (
[embeddings[idx] for idx in non_empty_ids] if embeddings else None
)
ids_with_metadata = [ids[idx] for idx in non_empty_ids]
try:
self._collection.upsert(
metadatas=metadatas,
embeddings=embeddings_with_metadatas,
documents=images_with_metadatas,
ids=ids_with_metadata,
)
except ValueError as e:
if "Expected metadata value to be" in str(e):
msg = (
"Try filtering complex metadata using "
"langchain_community.vectorstores.utils.filter_complex_metadata."
)
raise ValueError(e.args[0] + "\n\n" + msg)
else:
raise e
if empty_ids:
images_without_metadatas = [b64_texts[j] for j in empty_ids]
embeddings_without_metadatas = (
[embeddings[j] for j in empty_ids] if embeddings else None
)
ids_without_metadatas = [ids[j] for j in empty_ids]
self._collection.upsert(
embeddings=embeddings_without_metadatas,
documents=images_without_metadatas,
ids=ids_without_metadatas,
)
else:
self._collection.upsert(
embeddings=embeddings,
documents=b64_texts,
ids=ids,
)
return ids
| |
159319
|
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts (Iterable[str]): Texts to add to the vectorstore.
metadatas (Optional[List[dict]], optional): Optional list of metadatas.
ids (Optional[List[str]], optional): Optional list of IDs.
Returns:
List[str]: List of IDs of the added texts.
"""
# TODO: Handle the case where the user doesn't provide ids on the Collection
if ids is None:
ids = [str(uuid.uuid4()) for _ in texts]
embeddings = None
texts = list(texts)
if self._embedding_function is not None:
embeddings = self._embedding_function.embed_documents(texts)
if metadatas:
# fill metadatas with empty dicts if somebody
# did not specify metadata for all texts
length_diff = len(texts) - len(metadatas)
if length_diff:
metadatas = metadatas + [{}] * length_diff
empty_ids = []
non_empty_ids = []
for idx, m in enumerate(metadatas):
if m:
non_empty_ids.append(idx)
else:
empty_ids.append(idx)
if non_empty_ids:
metadatas = [metadatas[idx] for idx in non_empty_ids]
texts_with_metadatas = [texts[idx] for idx in non_empty_ids]
embeddings_with_metadatas = (
[embeddings[idx] for idx in non_empty_ids] if embeddings else None
)
ids_with_metadata = [ids[idx] for idx in non_empty_ids]
try:
self._collection.upsert(
metadatas=metadatas,
embeddings=embeddings_with_metadatas,
documents=texts_with_metadatas,
ids=ids_with_metadata,
)
except ValueError as e:
if "Expected metadata value to be" in str(e):
msg = (
"Try filtering complex metadata from the document using "
"langchain_community.vectorstores.utils.filter_complex_metadata."
)
raise ValueError(e.args[0] + "\n\n" + msg)
else:
raise e
if empty_ids:
texts_without_metadatas = [texts[j] for j in empty_ids]
embeddings_without_metadatas = (
[embeddings[j] for j in empty_ids] if embeddings else None
)
ids_without_metadatas = [ids[j] for j in empty_ids]
self._collection.upsert(
embeddings=embeddings_without_metadatas,
documents=texts_without_metadatas,
ids=ids_without_metadatas,
)
else:
self._collection.upsert(
embeddings=embeddings,
documents=texts,
ids=ids,
)
return ids
def similarity_search(
self,
query: str,
k: int = DEFAULT_K,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Run similarity search with Chroma.
Args:
query (str): Query text to search for.
k (int): Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List[Document]: List of documents most similar to the query text.
"""
docs_and_scores = self.similarity_search_with_score(
query, k, filter=filter, **kwargs
)
return [doc for doc, _ in docs_and_scores]
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = DEFAULT_K,
filter: Optional[Dict[str, str]] = None,
where_document: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding (List[float]): Embedding to look up documents similar to.
k (int): Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List of Documents most similar to the query vector.
"""
results = self.__query_collection(
query_embeddings=embedding,
n_results=k,
where=filter,
where_document=where_document,
**kwargs,
)
return _results_to_docs(results)
def similarity_search_by_vector_with_relevance_scores(
self,
embedding: List[float],
k: int = DEFAULT_K,
filter: Optional[Dict[str, str]] = None,
where_document: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""
Return docs most similar to embedding vector and similarity score.
Args:
embedding (List[float]): Embedding to look up documents similar to.
k (int): Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List[Tuple[Document, float]]: List of documents most similar to
the query text and cosine distance in float for each.
Lower score represents more similarity.
"""
results = self.__query_collection(
query_embeddings=embedding,
n_results=k,
where=filter,
where_document=where_document,
**kwargs,
)
return _results_to_docs_and_scores(results)
def similarity_search_with_score(
self,
query: str,
k: int = DEFAULT_K,
filter: Optional[Dict[str, str]] = None,
where_document: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Run similarity search with Chroma with distance.
Args:
query (str): Query text to search for.
k (int): Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List[Tuple[Document, float]]: List of documents most similar to
the query text and cosine distance in float for each.
Lower score represents more similarity.
"""
if self._embedding_function is None:
results = self.__query_collection(
query_texts=[query],
n_results=k,
where=filter,
where_document=where_document,
**kwargs,
)
else:
query_embedding = self._embedding_function.embed_query(query)
results = self.__query_collection(
query_embeddings=[query_embedding],
n_results=k,
where=filter,
where_document=where_document,
**kwargs,
)
return _results_to_docs_and_scores(results)
def _select_relevance_score_fn(self) -> Callable[[float], float]:
"""
The 'correct' relevance function
may differ depending on a few things, including:
- the distance / similarity metric used by the VectorStore
- the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
- embedding dimensionality
- etc.
"""
if self.override_relevance_score_fn:
return self.override_relevance_score_fn
distance = "l2"
distance_key = "hnsw:space"
metadata = self._collection.metadata
if metadata and distance_key in metadata:
distance = metadata[distance_key]
if distance == "cosine":
return self._cosine_relevance_score_fn
elif distance == "l2":
return self._euclidean_relevance_score_fn
elif distance == "ip":
return self._max_inner_product_relevance_score_fn
else:
raise ValueError(
"No supported normalization function"
f" for distance metric of type: {distance}."
"Consider providing relevance_score_fn to Chroma constructor."
)
| |
159320
|
def similarity_search_by_image(
self,
uri: str,
k: int = DEFAULT_K,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Search for similar images based on the given image URI.
Args:
uri (str): URI of the image to search for.
k (int, optional): Number of results to return. Defaults to DEFAULT_K.
filter (Optional[Dict[str, str]], optional): Filter by metadata.
**kwargs (Any): Additional arguments to pass to function.
Returns:
List of Images most similar to the provided image.
Each element in list is a Langchain Document Object.
The page content is b64 encoded image, metadata is default or
as defined by user.
Raises:
ValueError: If the embedding function does not support image embeddings.
"""
if self._embedding_function is None or not hasattr(
self._embedding_function, "embed_image"
):
raise ValueError("The embedding function must support image embedding.")
# Obtain image embedding
# Assuming embed_image returns a single embedding
image_embedding = self._embedding_function.embed_image(uris=[uri])
# Perform similarity search based on the obtained embedding
results = self.similarity_search_by_vector(
embedding=image_embedding,
k=k,
filter=filter,
**kwargs,
)
return results
def similarity_search_by_image_with_relevance_score(
self,
uri: str,
k: int = DEFAULT_K,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Search for similar images based on the given image URI.
Args:
uri (str): URI of the image to search for.
k (int, optional): Number of results to return.
Defaults to DEFAULT_K.
filter (Optional[Dict[str, str]], optional): Filter by metadata.
**kwargs (Any): Additional arguments to pass to function.
Returns:
List[Tuple[Document, float]]: List of tuples containing documents similar
to the query image and their similarity scores.
0th element in each tuple is a Langchain Document Object.
The page content is b64 encoded img, metadata is default or defined by user.
Raises:
ValueError: If the embedding function does not support image embeddings.
"""
if self._embedding_function is None or not hasattr(
self._embedding_function, "embed_image"
):
raise ValueError("The embedding function must support image embedding.")
# Obtain image embedding
# Assuming embed_image returns a single embedding
image_embedding = self._embedding_function.embed_image(uris=[uri])
# Perform similarity search based on the obtained embedding
results = self.similarity_search_by_vector_with_relevance_scores(
embedding=image_embedding,
k=k,
filter=filter,
**kwargs,
)
return results
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = DEFAULT_K,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, str]] = None,
where_document: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List of Documents selected by maximal marginal relevance.
"""
results = self.__query_collection(
query_embeddings=embedding,
n_results=fetch_k,
where=filter,
where_document=where_document,
include=["metadatas", "documents", "distances", "embeddings"],
**kwargs,
)
mmr_selected = maximal_marginal_relevance(
np.array(embedding, dtype=np.float32),
results["embeddings"][0],
k=k,
lambda_mult=lambda_mult,
)
candidates = _results_to_docs(results)
selected_results = [r for i, r in enumerate(candidates) if i in mmr_selected]
return selected_results
def max_marginal_relevance_search(
self,
query: str,
k: int = DEFAULT_K,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, str]] = None,
where_document: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List of Documents selected by maximal marginal relevance.
"""
if self._embedding_function is None:
raise ValueError(
"For MMR search, you must specify an embedding function on" "creation."
)
embedding = self._embedding_function.embed_query(query)
docs = self.max_marginal_relevance_search_by_vector(
embedding,
k,
fetch_k,
lambda_mult=lambda_mult,
filter=filter,
where_document=where_document,
)
return docs
def delete_collection(self) -> None:
"""Delete the collection."""
self._client.delete_collection(self._collection.name)
def get(
self,
ids: Optional[OneOrMany[ID]] = None,
where: Optional[Where] = None,
limit: Optional[int] = None,
offset: Optional[int] = None,
where_document: Optional[WhereDocument] = None,
include: Optional[List[str]] = None,
) -> Dict[str, Any]:
"""Gets the collection.
Args:
ids: The ids of the embeddings to get. Optional.
where: A Where type dict used to filter results by.
E.g. `{"color" : "red", "price": 4.20}`. Optional.
limit: The number of documents to return. Optional.
offset: The offset to start returning results from.
Useful for paging results with limit. Optional.
where_document: A WhereDocument type dict used to filter by the documents.
E.g. `{$contains: "hello"}`. Optional.
include: A list of what to include in the results.
Can contain `"embeddings"`, `"metadatas"`, `"documents"`.
Ids are always included.
Defaults to `["metadatas", "documents"]`. Optional.
"""
kwargs = {
"ids": ids,
"where": where,
"limit": limit,
"offset": offset,
"where_document": where_document,
}
if include is not None:
kwargs["include"] = include
return self._collection.get(**kwargs)
@deprecated(
since="0.1.17",
message=(
"Since Chroma 0.4.x the manual persistence method is no longer "
"supported as docs are automatically persisted."
),
removal="1.0",
)
def persist(self) -> None:
"""Persist the collection.
This can be used to explicitly persist the data to disk.
It will also be called automatically when the object is destroyed.
Since Chroma 0.4.x the manual persistence method is no longer
supported as docs are automatically persisted.
"""
if self._persist_directory is None:
raise ValueError(
"You must specify a persist_directory on"
"creation to persist the collection."
)
import chromadb
# Maintain backwards compatibility with chromadb < 0.4.0
major, minor, _ = chromadb.__version__.split(".")
if int(major) == 0 and int(minor) < 4:
self._client.persist()
| |
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def update_document(self, document_id: str, document: Document) -> None:
"""Update a document in the collection.
Args:
document_id (str): ID of the document to update.
document (Document): Document to update.
"""
return self.update_documents([document_id], [document])
def update_documents(self, ids: List[str], documents: List[Document]) -> None:
"""Update a document in the collection.
Args:
ids (List[str]): List of ids of the document to update.
documents (List[Document]): List of documents to update.
"""
text = [document.page_content for document in documents]
metadata = [document.metadata for document in documents]
if self._embedding_function is None:
raise ValueError(
"For update, you must specify an embedding function on creation."
)
embeddings = self._embedding_function.embed_documents(text)
if hasattr(
self._collection._client, "max_batch_size"
): # for Chroma 0.4.10 and above
from chromadb.utils.batch_utils import create_batches
for batch in create_batches(
api=self._collection._client,
ids=ids,
metadatas=metadata,
documents=text,
embeddings=embeddings,
):
self._collection.update(
ids=batch[0],
embeddings=batch[1],
documents=batch[3],
metadatas=batch[2],
)
else:
self._collection.update(
ids=ids,
embeddings=embeddings,
documents=text,
metadatas=metadata,
)
@classmethod
def from_texts(
cls: Type[Chroma],
texts: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
persist_directory: Optional[str] = None,
client_settings: Optional[chromadb.config.Settings] = None,
client: Optional[chromadb.Client] = None,
collection_metadata: Optional[Dict] = None,
**kwargs: Any,
) -> Chroma:
"""Create a Chroma vectorstore from a raw documents.
If a persist_directory is specified, the collection will be persisted there.
Otherwise, the data will be ephemeral in-memory.
Args:
texts (List[str]): List of texts to add to the collection.
collection_name (str): Name of the collection to create.
persist_directory (Optional[str]): Directory to persist the collection.
embedding (Optional[Embeddings]): Embedding function. Defaults to None.
metadatas (Optional[List[dict]]): List of metadatas. Defaults to None.
ids (Optional[List[str]]): List of document IDs. Defaults to None.
client_settings (Optional[chromadb.config.Settings]): Chroma client settings
collection_metadata (Optional[Dict]): Collection configurations.
Defaults to None.
Returns:
Chroma: Chroma vectorstore.
"""
chroma_collection = cls(
collection_name=collection_name,
embedding_function=embedding,
persist_directory=persist_directory,
client_settings=client_settings,
client=client,
collection_metadata=collection_metadata,
**kwargs,
)
if ids is None:
ids = [str(uuid.uuid4()) for _ in texts]
if hasattr(
chroma_collection._client, "max_batch_size"
): # for Chroma 0.4.10 and above
from chromadb.utils.batch_utils import create_batches
for batch in create_batches(
api=chroma_collection._client,
ids=ids,
metadatas=metadatas,
documents=texts,
):
chroma_collection.add_texts(
texts=batch[3] if batch[3] else [],
metadatas=batch[2] if batch[2] else None,
ids=batch[0],
)
else:
chroma_collection.add_texts(texts=texts, metadatas=metadatas, ids=ids)
return chroma_collection
@classmethod
def from_documents(
cls: Type[Chroma],
documents: List[Document],
embedding: Optional[Embeddings] = None,
ids: Optional[List[str]] = None,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
persist_directory: Optional[str] = None,
client_settings: Optional[chromadb.config.Settings] = None,
client: Optional[chromadb.Client] = None, # Add this line
collection_metadata: Optional[Dict] = None,
**kwargs: Any,
) -> Chroma:
"""Create a Chroma vectorstore from a list of documents.
If a persist_directory is specified, the collection will be persisted there.
Otherwise, the data will be ephemeral in-memory.
Args:
collection_name (str): Name of the collection to create.
persist_directory (Optional[str]): Directory to persist the collection.
ids (Optional[List[str]]): List of document IDs. Defaults to None.
documents (List[Document]): List of documents to add to the vectorstore.
embedding (Optional[Embeddings]): Embedding function. Defaults to None.
client_settings (Optional[chromadb.config.Settings]): Chroma client settings
collection_metadata (Optional[Dict]): Collection configurations.
Defaults to None.
Returns:
Chroma: Chroma vectorstore.
"""
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return cls.from_texts(
texts=texts,
embedding=embedding,
metadatas=metadatas,
ids=ids,
collection_name=collection_name,
persist_directory=persist_directory,
client_settings=client_settings,
client=client,
collection_metadata=collection_metadata,
**kwargs,
)
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> None:
"""Delete by vector IDs.
Args:
ids: List of ids to delete.
"""
self._collection.delete(ids=ids, **kwargs)
def __len__(self) -> int:
"""Count the number of documents in the collection."""
return self._collection.count()
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def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
kwargs: vectorstore specific parameters
Returns:
List of ids from adding the texts into the vectorstore.
"""
embeddings = self.embedding.embed_documents(list(texts))
return self.add_embeddings(
texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs
)
def similarity_search(
self,
query: str,
k: int = 4,
params: Dict[str, Any] = {},
filter: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> List[Document]:
"""Run similarity search with Neo4jVector.
Args:
query (str): Query text to search for.
k (int): Number of results to return. Defaults to 4.
params (Dict[str, Any]): The search params for the index type.
Defaults to empty dict.
filter (Optional[Dict[str, Any]]): Dictionary of argument(s) to
filter on metadata.
Defaults to None.
Returns:
List of Documents most similar to the query.
"""
embedding = self.embedding.embed_query(text=query)
return self.similarity_search_by_vector(
embedding=embedding,
k=k,
query=query,
params=params,
filter=filter,
**kwargs,
)
def similarity_search_with_score(
self,
query: str,
k: int = 4,
params: Dict[str, Any] = {},
filter: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
params (Dict[str, Any]): The search params for the index type.
Defaults to empty dict.
filter (Optional[Dict[str, Any]]): Dictionary of argument(s) to
filter on metadata.
Defaults to None.
Returns:
List of Documents most similar to the query and score for each
"""
embedding = self.embedding.embed_query(query)
docs = self.similarity_search_with_score_by_vector(
embedding=embedding,
k=k,
query=query,
params=params,
filter=filter,
**kwargs,
)
return docs
def similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[Dict[str, Any]] = None,
params: Dict[str, Any] = {},
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""
Perform a similarity search in the Neo4j database using a
given vector and return the top k similar documents with their scores.
This method uses a Cypher query to find the top k documents that
are most similar to a given embedding. The similarity is measured
using a vector index in the Neo4j database. The results are returned
as a list of tuples, each containing a Document object and
its similarity score.
Args:
embedding (List[float]): The embedding vector to compare against.
k (int, optional): The number of top similar documents to retrieve.
filter (Optional[Dict[str, Any]]): Dictionary of argument(s) to
filter on metadata.
Defaults to None.
params (Dict[str, Any]): The search params for the index type.
Defaults to empty dict.
Returns:
List[Tuple[Document, float]]: A list of tuples, each containing
a Document object and its similarity score.
"""
if filter:
# Verify that 5.18 or later is used
if not self.support_metadata_filter:
raise ValueError(
"Metadata filtering is only supported in "
"Neo4j version 5.18 or greater"
)
# Metadata filtering and hybrid doesn't work
if self.search_type == SearchType.HYBRID:
raise ValueError(
"Metadata filtering can't be use in combination with "
"a hybrid search approach"
)
parallel_query = (
"CYPHER runtime = parallel parallelRuntimeSupport=all "
if self._is_enterprise
else ""
)
base_index_query = parallel_query + (
f"MATCH (n:`{self.node_label}`) WHERE "
f"n.`{self.embedding_node_property}` IS NOT NULL AND "
f"size(n.`{self.embedding_node_property}`) = "
f"toInteger({self.embedding_dimension}) AND "
)
base_cosine_query = (
" WITH n as node, vector.similarity.cosine("
f"n.`{self.embedding_node_property}`, "
"$embedding) AS score ORDER BY score DESC LIMIT toInteger($k) "
)
filter_snippets, filter_params = construct_metadata_filter(filter)
index_query = base_index_query + filter_snippets + base_cosine_query
else:
index_query = _get_search_index_query(self.search_type, self._index_type)
filter_params = {}
if self._index_type == IndexType.RELATIONSHIP:
if kwargs.get("return_embeddings"):
default_retrieval = (
f"RETURN relationship.`{self.text_node_property}` AS text, score, "
f"relationship {{.*, `{self.text_node_property}`: Null, "
f"`{self.embedding_node_property}`: Null, id: Null, "
f"_embedding_: relationship.`{self.embedding_node_property}`}} "
"AS metadata"
)
else:
default_retrieval = (
f"RETURN relationship.`{self.text_node_property}` AS text, score, "
f"relationship {{.*, `{self.text_node_property}`: Null, "
f"`{self.embedding_node_property}`: Null, id: Null }} AS metadata"
)
else:
if kwargs.get("return_embeddings"):
default_retrieval = (
f"RETURN node.`{self.text_node_property}` AS text, score, "
f"node {{.*, `{self.text_node_property}`: Null, "
f"`{self.embedding_node_property}`: Null, id: Null, "
f"_embedding_: node.`{self.embedding_node_property}`}} AS metadata"
)
else:
default_retrieval = (
f"RETURN node.`{self.text_node_property}` AS text, score, "
f"node {{.*, `{self.text_node_property}`: Null, "
f"`{self.embedding_node_property}`: Null, id: Null }} AS metadata"
)
retrieval_query = (
self.retrieval_query if self.retrieval_query else default_retrieval
)
read_query = index_query + retrieval_query
parameters = {
"index": self.index_name,
"k": k,
"embedding": embedding,
"keyword_index": self.keyword_index_name,
"query": remove_lucene_chars(kwargs["query"]),
**params,
**filter_params,
}
results = self.query(read_query, params=parameters)
if any(result["text"] is None for result in results):
if not self.retrieval_query:
raise ValueError(
f"Make sure that none of the `{self.text_node_property}` "
f"properties on nodes with label `{self.node_label}` "
"are missing or empty"
)
else:
raise ValueError(
"Inspect the `retrieval_query` and ensure it doesn't "
"return None for the `text` column"
)
if kwargs.get("return_embeddings") and any(
result["metadata"]["_embedding_"] is None for result in results
):
if not self.retrieval_query:
raise ValueError(
f"Make sure that none of the `{self.embedding_node_property}` "
f"properties on nodes with label `{self.node_label}` "
"are missing or empty"
)
else:
raise ValueError(
"Inspect the `retrieval_query` and ensure it doesn't "
"return None for the `_embedding_` metadata column"
)
docs = [
(
Document(
page_content=dict_to_yaml_str(result["text"])
if isinstance(result["text"], dict)
else result["text"],
metadata={
k: v for k, v in result["metadata"].items() if v is not None
},
),
result["score"],
)
for result in results
]
return docs
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|
import json
import logging
import numbers
from hashlib import sha1
from typing import Any, Dict, Iterable, List, Optional, Tuple
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore
logger = logging.getLogger()
class AlibabaCloudOpenSearchSettings:
"""Alibaba Cloud Opensearch` client configuration.
Attribute:
endpoint (str) : The endpoint of opensearch instance, You can find it
from the console of Alibaba Cloud OpenSearch.
instance_id (str) : The identify of opensearch instance, You can find
it from the console of Alibaba Cloud OpenSearch.
username (str) : The username specified when purchasing the instance.
password (str) : The password specified when purchasing the instance，
After the instance is created, you can modify it on the console.
tablename (str): The table name specified during instance configuration.
field_name_mapping (Dict) : Using field name mapping between opensearch
vector store and opensearch instance configuration table field names:
{
'id': 'The id field name map of index document.',
'document': 'The text field name map of index document.',
'embedding': 'In the embedding field of the opensearch instance,
the values must be in float type and separated by separator,
default is comma.',
'metadata_field_x': 'Metadata field mapping includes the mapped
field name and operator in the mapping value, separated by a comma
between the mapped field name and the operator.',
}
protocol (str): Communication Protocol between SDK and Server, default is http.
namespace (str) : The instance data will be partitioned based on the "namespace"
field,If the namespace is enabled, you need to specify the namespace field
name during initialization, Otherwise, the queries cannot be executed
correctly.
embedding_field_separator(str): Delimiter specified for writing vector
field data, default is comma.
output_fields: Specify the field list returned when invoking OpenSearch,
by default it is the value list of the field mapping field.
"""
def __init__(
self,
endpoint: str,
instance_id: str,
username: str,
password: str,
table_name: str,
field_name_mapping: Dict[str, str],
protocol: str = "http",
namespace: str = "",
embedding_field_separator: str = ",",
output_fields: Optional[List[str]] = None,
) -> None:
self.endpoint = endpoint
self.instance_id = instance_id
self.protocol = protocol
self.username = username
self.password = password
self.namespace = namespace
self.table_name = table_name
self.opt_table_name = "_".join([self.instance_id, self.table_name])
self.field_name_mapping = field_name_mapping
self.embedding_field_separator = embedding_field_separator
if output_fields is None:
self.output_fields = [
field.split(",")[0] for field in self.field_name_mapping.values()
]
self.inverse_field_name_mapping: Dict[str, str] = {}
for key, value in self.field_name_mapping.items():
self.inverse_field_name_mapping[value.split(",")[0]] = key
def __getitem__(self, item: str) -> Any:
return getattr(self, item)
def create_metadata(fields: Dict[str, Any]) -> Dict[str, Any]:
"""Create metadata from fields.
Args:
fields: The fields of the document. The fields must be a dict.
Returns:
metadata: The metadata of the document. The metadata must be a dict.
"""
metadata: Dict[str, Any] = {}
for key, value in fields.items():
if key == "id" or key == "document" or key == "embedding":
continue
metadata[key] = value
return metadata
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|
def __init__(
self,
embedding: Embeddings,
*,
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
table_name: str = "embeddings",
content_field: str = "content",
metadata_field: str = "metadata",
vector_field: str = "vector",
id_field: str = "id",
use_vector_index: bool = False,
vector_index_name: str = "",
vector_index_options: Optional[dict] = None,
vector_size: int = 1536,
use_full_text_search: bool = False,
pool_size: int = 5,
max_overflow: int = 10,
timeout: float = 30,
**kwargs: Any,
):
"""Initialize with necessary components.
Args:
embedding (Embeddings): A text embedding model.
distance_strategy (DistanceStrategy, optional):
Determines the strategy employed for calculating
the distance between vectors in the embedding space.
Defaults to DOT_PRODUCT.
Available options are:
- DOT_PRODUCT: Computes the scalar product of two vectors.
This is the default behavior
- EUCLIDEAN_DISTANCE: Computes the Euclidean distance between
two vectors. This metric considers the geometric distance in
the vector space, and might be more suitable for embeddings
that rely on spatial relationships. This metric is not
compatible with the WEIGHTED_SUM search strategy.
table_name (str, optional): Specifies the name of the table in use.
Defaults to "embeddings".
content_field (str, optional): Specifies the field to store the content.
Defaults to "content".
metadata_field (str, optional): Specifies the field to store metadata.
Defaults to "metadata".
vector_field (str, optional): Specifies the field to store the vector.
Defaults to "vector".
id_field (str, optional): Specifies the field to store the id.
Defaults to "id".
use_vector_index (bool, optional): Toggles the use of a vector index.
Works only with SingleStoreDB 8.5 or later. Defaults to False.
If set to True, vector_size parameter is required to be set to
a proper value.
vector_index_name (str, optional): Specifies the name of the vector index.
Defaults to empty. Will be ignored if use_vector_index is set to False.
vector_index_options (dict, optional): Specifies the options for
the vector index. Defaults to {}.
Will be ignored if use_vector_index is set to False. The options are:
index_type (str, optional): Specifies the type of the index.
Defaults to IVF_PQFS.
For more options, please refer to the SingleStoreDB documentation:
https://docs.singlestore.com/cloud/reference/sql-reference/vector-functions/vector-indexing/
vector_size (int, optional): Specifies the size of the vector.
Defaults to 1536. Required if use_vector_index is set to True.
Should be set to the same value as the size of the vectors
stored in the vector_field.
use_full_text_search (bool, optional): Toggles the use a full-text index
on the document content. Defaults to False. If set to True, the table
will be created with a full-text index on the content field,
and the simularity_search method will all using TEXT_ONLY,
FILTER_BY_TEXT, FILTER_BY_VECTOR, and WIGHTED_SUM search strategies.
If set to False, the simularity_search method will only allow
VECTOR_ONLY search strategy.
Following arguments pertain to the connection pool:
pool_size (int, optional): Determines the number of active connections in
the pool. Defaults to 5.
max_overflow (int, optional): Determines the maximum number of connections
allowed beyond the pool_size. Defaults to 10.
timeout (float, optional): Specifies the maximum wait time in seconds for
establishing a connection. Defaults to 30.
Following arguments pertain to the database connection:
host (str, optional): Specifies the hostname, IP address, or URL for the
database connection. The default scheme is "mysql".
user (str, optional): Database username.
password (str, optional): Database password.
port (int, optional): Database port. Defaults to 3306 for non-HTTP
connections, 80 for HTTP connections, and 443 for HTTPS connections.
database (str, optional): Database name.
Additional optional arguments provide further customization over the
database connection:
pure_python (bool, optional): Toggles the connector mode. If True,
operates in pure Python mode.
local_infile (bool, optional): Allows local file uploads.
charset (str, optional): Specifies the character set for string values.
ssl_key (str, optional): Specifies the path of the file containing the SSL
key.
ssl_cert (str, optional): Specifies the path of the file containing the SSL
certificate.
ssl_ca (str, optional): Specifies the path of the file containing the SSL
certificate authority.
ssl_cipher (str, optional): Sets the SSL cipher list.
ssl_disabled (bool, optional): Disables SSL usage.
ssl_verify_cert (bool, optional): Verifies the server's certificate.
Automatically enabled if ``ssl_ca`` is specified.
ssl_verify_identity (bool, optional): Verifies the server's identity.
conv (dict[int, Callable], optional): A dictionary of data conversion
functions.
credential_type (str, optional): Specifies the type of authentication to
use: auth.PASSWORD, auth.JWT, or auth.BROWSER_SSO.
autocommit (bool, optional): Enables autocommits.
results_type (str, optional): Determines the structure of the query results:
tuples, namedtuples, dicts.
results_format (str, optional): Deprecated. This option has been renamed to
results_type.
Examples:
Basic Usage:
.. code-block:: python
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import SingleStoreDB
vectorstore = SingleStoreDB(
OpenAIEmbeddings(),
host="https://user:password@127.0.0.1:3306/database"
)
Advanced Usage:
.. code-block:: python
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import SingleStoreDB
vectorstore = SingleStoreDB(
OpenAIEmbeddings(),
distance_strategy=DistanceStrategy.EUCLIDEAN_DISTANCE,
host="127.0.0.1",
port=3306,
user="user",
password="password",
database="db",
table_name="my_custom_table",
pool_size=10,
timeout=60,
)
Using environment variables:
.. code-block:: python
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import SingleStoreDB
os.environ['SINGLESTOREDB_URL'] = 'me:p455w0rd@s2-host.com/my_db'
vectorstore = SingleStoreDB(OpenAIEmbeddings())
Using vector index:
.. code-block:: python
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import SingleStoreDB
os.environ['SINGLESTOREDB_URL'] = 'me:p455w0rd@s2-host.com/my_db'
vectorstore = SingleStoreDB(
OpenAIEmbeddings(),
use_vector_index=True,
)
Using full-text index:
.. code-block:: python
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import SingleStoreDB
os.environ['SINGLESTOREDB_URL'] = 'me:p455w0rd@s2-host.com/my_db'
vectorstore = SingleStoreDB(
OpenAIEmbeddings(),
use_full_text_search=True,
)
"""
self.embedding = embedding
self.distance_strategy = distance_strategy
self.table_name = self._sanitize_input(table_name)
self.content_field = self._sanitize_input(content_field)
self.metadata_field = self._sanitize_input(metadata_field)
self.vector_field = self._sanitize_input(vector_field)
self.id_field = self._sanitize_input(id_field)
self.use_vector_index = bool(use_vector_index)
self.vector_index_name = self._sanitize_input(vector_index_name)
self.vector_index_options = dict(vector_index_options or {})
self.vector_index_options["metric_type"] = self.distance_strategy
self.vector_size = int(vector_size)
self.use_full_text_search = bool(use_full_text_search)
# Pass the rest of the kwargs to the connection.
self.connection_kwargs = kwargs
# Add program name and version to connection attributes.
if "conn_attrs" not in self.connection_kwargs:
self.connection_kwargs["conn_attrs"] = dict()
self.connection_kwargs["conn_attrs"]["_connector_name"] = "langchain python sdk"
self.connection_kwargs["conn_attrs"]["_connector_version"] = "2.1.0"
# Create connection pool.
self.connection_pool = QueuePool(
self._get_connection,
max_overflow=max_overflow,
pool_size=pool_size,
timeout=timeout,
)
self._create_table()
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|
def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
search_strategy: SearchStrategy = SearchStrategy.VECTOR_ONLY,
filter_threshold: float = 0,
text_weight: float = 0.5,
vector_weight: float = 0.5,
vector_select_count_multiplier: int = 10,
**kwargs: Any,
) -> List[Document]:
"""Returns the most similar indexed documents to the query text.
Uses cosine similarity.
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
filter (dict): A dictionary of metadata fields and values to filter by.
Default is None.
search_strategy (SearchStrategy): The search strategy to use.
Default is SearchStrategy.VECTOR_ONLY.
Available options are:
- SearchStrategy.VECTOR_ONLY: Searches only by vector similarity.
- SearchStrategy.TEXT_ONLY: Searches only by text similarity. This
option is only available if use_full_text_search is True.
- SearchStrategy.FILTER_BY_TEXT: Filters by text similarity and
searches by vector similarity. This option is only available if
use_full_text_search is True.
- SearchStrategy.FILTER_BY_VECTOR: Filters by vector similarity and
searches by text similarity. This option is only available if
use_full_text_search is True.
- SearchStrategy.WEIGHTED_SUM: Searches by a weighted sum of text and
vector similarity. This option is only available if
use_full_text_search is True and distance_strategy is DOT_PRODUCT.
filter_threshold (float): The threshold for filtering by text or vector
similarity. Default is 0. This option has effect only if search_strategy
is SearchStrategy.FILTER_BY_TEXT or SearchStrategy.FILTER_BY_VECTOR.
text_weight (float): The weight of text similarity in the weighted sum
search strategy. Default is 0.5. This option has effect only if
search_strategy is SearchStrategy.WEIGHTED_SUM.
vector_weight (float): The weight of vector similarity in the weighted sum
search strategy. Default is 0.5. This option has effect only if
search_strategy is SearchStrategy.WEIGHTED_SUM.
vector_select_count_multiplier (int): The multiplier for the number of
vectors to select when using the vector index. Default is 10.
This parameter has effect only if use_vector_index is True and
search_strategy is SearchStrategy.WEIGHTED_SUM or
SearchStrategy.FILTER_BY_TEXT.
The number of vectors selected will
be k * vector_select_count_multiplier.
This is needed due to the limitations of the vector index.
Returns:
List[Document]: A list of documents that are most similar to the query text.
Examples:
Basic Usage:
.. code-block:: python
from langchain_community.vectorstores import SingleStoreDB
from langchain_openai import OpenAIEmbeddings
s2 = SingleStoreDB.from_documents(
docs,
OpenAIEmbeddings(),
host="username:password@localhost:3306/database"
)
results = s2.similarity_search("query text", 1,
{"metadata_field": "metadata_value"})
Different Search Strategies:
.. code-block:: python
from langchain_community.vectorstores import SingleStoreDB
from langchain_openai import OpenAIEmbeddings
s2 = SingleStoreDB.from_documents(
docs,
OpenAIEmbeddings(),
host="username:password@localhost:3306/database",
use_full_text_search=True,
use_vector_index=True,
)
results = s2.similarity_search("query text", 1,
search_strategy=SingleStoreDB.SearchStrategy.FILTER_BY_TEXT,
filter_threshold=0.5)
Weighted Sum Search Strategy:
.. code-block:: python
from langchain_community.vectorstores import SingleStoreDB
from langchain_openai import OpenAIEmbeddings
s2 = SingleStoreDB.from_documents(
docs,
OpenAIEmbeddings(),
host="username:password@localhost:3306/database",
use_full_text_search=True,
use_vector_index=True,
)
results = s2.similarity_search("query text", 1,
search_strategy=SingleStoreDB.SearchStrategy.WEIGHTED_SUM,
text_weight=0.3,
vector_weight=0.7)
"""
docs_and_scores = self.similarity_search_with_score(
query=query,
k=k,
filter=filter,
search_strategy=search_strategy,
filter_threshold=filter_threshold,
text_weight=text_weight,
vector_weight=vector_weight,
vector_select_count_multiplier=vector_select_count_multiplier,
**kwargs,
)
return [doc for doc, _ in docs_and_scores]
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|
def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
search_strategy: SearchStrategy = SearchStrategy.VECTOR_ONLY,
filter_threshold: float = 1,
text_weight: float = 0.5,
vector_weight: float = 0.5,
vector_select_count_multiplier: int = 10,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query. Uses cosine similarity.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: A dictionary of metadata fields and values to filter by.
Defaults to None.
search_strategy (SearchStrategy): The search strategy to use.
Default is SearchStrategy.VECTOR_ONLY.
Available options are:
- SearchStrategy.VECTOR_ONLY: Searches only by vector similarity.
- SearchStrategy.TEXT_ONLY: Searches only by text similarity. This
option is only available if use_full_text_search is True.
- SearchStrategy.FILTER_BY_TEXT: Filters by text similarity and
searches by vector similarity. This option is only available if
use_full_text_search is True.
- SearchStrategy.FILTER_BY_VECTOR: Filters by vector similarity and
searches by text similarity. This option is only available if
use_full_text_search is True.
- SearchStrategy.WEIGHTED_SUM: Searches by a weighted sum of text and
vector similarity. This option is only available if
use_full_text_search is True and distance_strategy is DOT_PRODUCT.
filter_threshold (float): The threshold for filtering by text or vector
similarity. Default is 0. This option has effect only if search_strategy
is SearchStrategy.FILTER_BY_TEXT or SearchStrategy.FILTER_BY_VECTOR.
text_weight (float): The weight of text similarity in the weighted sum
search strategy. Default is 0.5. This option has effect only if
search_strategy is SearchStrategy.WEIGHTED_SUM.
vector_weight (float): The weight of vector similarity in the weighted sum
search strategy. Default is 0.5. This option has effect only if
search_strategy is SearchStrategy.WEIGHTED_SUM.
vector_select_count_multiplier (int): The multiplier for the number of
vectors to select when using the vector index. Default is 10.
This parameter has effect only if use_vector_index is True and
search_strategy is SearchStrategy.WEIGHTED_SUM or
SearchStrategy.FILTER_BY_TEXT.
The number of vectors selected will
be k * vector_select_count_multiplier.
This is needed due to the limitations of the vector index.
Returns:
List of Documents most similar to the query and score for each
document.
Raises:
ValueError: If the search strategy is not supported with the
distance strategy.
Examples:
Basic Usage:
.. code-block:: python
from langchain_community.vectorstores import SingleStoreDB
from langchain_openai import OpenAIEmbeddings
s2 = SingleStoreDB.from_documents(
docs,
OpenAIEmbeddings(),
host="username:password@localhost:3306/database"
)
results = s2.similarity_search_with_score("query text", 1,
{"metadata_field": "metadata_value"})
Different Search Strategies:
.. code-block:: python
from langchain_community.vectorstores import SingleStoreDB
from langchain_openai import OpenAIEmbeddings
s2 = SingleStoreDB.from_documents(
docs,
OpenAIEmbeddings(),
host="username:password@localhost:3306/database",
use_full_text_search=True,
use_vector_index=True,
)
results = s2.similarity_search_with_score("query text", 1,
search_strategy=SingleStoreDB.SearchStrategy.FILTER_BY_VECTOR,
filter_threshold=0.5)
Weighted Sum Search Strategy:
.. code-block:: python
from langchain_community.vectorstores import SingleStoreDB
from langchain_openai import OpenAIEmbeddings
s2 = SingleStoreDB.from_documents(
docs,
OpenAIEmbeddings(),
host="username:password@localhost:3306/database",
use_full_text_search=True,
use_vector_index=True,
)
results = s2.similarity_search_with_score("query text", 1,
search_strategy=SingleStoreDB.SearchStrategy.WEIGHTED_SUM,
text_weight=0.3,
vector_weight=0.7)
"""
if (
search_strategy != SingleStoreDB.SearchStrategy.VECTOR_ONLY
and not self.use_full_text_search
):
raise ValueError(
"""Search strategy {} is not supported
when use_full_text_search is False""".format(search_strategy)
)
if (
search_strategy == SingleStoreDB.SearchStrategy.WEIGHTED_SUM
and self.distance_strategy != DistanceStrategy.DOT_PRODUCT
):
raise ValueError(
"Search strategy {} is not supported with distance strategy {}".format(
search_strategy, self.distance_strategy
)
)
# Creates embedding vector from user query
embedding = []
if search_strategy != SingleStoreDB.SearchStrategy.TEXT_ONLY:
embedding = self.embedding.embed_query(query)
self.embedding.embed_query(query)
conn = self.connection_pool.connect()
result = []
where_clause: str = ""
where_clause_values: List[Any] = []
if filter or search_strategy in [
SingleStoreDB.SearchStrategy.FILTER_BY_TEXT,
SingleStoreDB.SearchStrategy.FILTER_BY_VECTOR,
]:
where_clause = "WHERE "
arguments = []
if search_strategy == SingleStoreDB.SearchStrategy.FILTER_BY_TEXT:
arguments.append(
"MATCH ({}) AGAINST (%s) > %s".format(self.content_field)
)
where_clause_values.append(query)
where_clause_values.append(float(filter_threshold))
if search_strategy == SingleStoreDB.SearchStrategy.FILTER_BY_VECTOR:
condition = "{}({}, JSON_ARRAY_PACK(%s)) ".format(
self.distance_strategy.name
if isinstance(self.distance_strategy, DistanceStrategy)
else self.distance_strategy,
self.vector_field,
)
if self.distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE:
condition += "< %s"
else:
condition += "> %s"
arguments.append(condition)
where_clause_values.append("[{}]".format(",".join(map(str, embedding))))
where_clause_values.append(float(filter_threshold))
def build_where_clause(
where_clause_values: List[Any],
sub_filter: dict,
prefix_args: Optional[List[str]] = None,
) -> None:
prefix_args = prefix_args or []
for key in sub_filter.keys():
if isinstance(sub_filter[key], dict):
build_where_clause(
where_clause_values, sub_filter[key], prefix_args + [key]
)
else:
arguments.append(
"JSON_EXTRACT_JSON({}, {}) = %s".format(
self.metadata_field,
", ".join(["%s"] * (len(prefix_args) + 1)),
)
)
where_clause_values += prefix_args + [key]
where_clause_values.append(json.dumps(sub_filter[key]))
if filter:
build_where_clause(where_clause_values, filter)
where_clause += " AND ".join(arguments)
| |
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|
from __future__ import annotations
import logging
import uuid
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Union, cast
import numpy as np
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.utils.iter import batch_iterate
from langchain_core.vectorstores import VectorStore
from langchain_community.vectorstores.utils import (
maximal_marginal_relevance,
)
if TYPE_CHECKING:
from upstash_vector import AsyncIndex, Index
from upstash_vector.types import InfoResult
logger = logging.getLogger(__name__)
class UpstashVectorStore(VectorStore):
"""Upstash Vector vector store
To use, the ``upstash-vector`` python package must be installed.
Also an Upstash Vector index is required. First create a new Upstash Vector index
and copy the `index_url` and `index_token` variables. Then either pass
them through the constructor or set the environment
variables `UPSTASH_VECTOR_REST_URL` and `UPSTASH_VECTOR_REST_TOKEN`.
Example:
.. code-block:: python
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import UpstashVectorStore
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
vectorstore = UpstashVectorStore(
embedding=embeddings,
index_url="...",
index_token="..."
)
# or
import os
os.environ["UPSTASH_VECTOR_REST_URL"] = "..."
os.environ["UPSTASH_VECTOR_REST_TOKEN"] = "..."
vectorstore = UpstashVectorStore(
embedding=embeddings
)
"""
def __init__(
self,
text_key: str = "text",
index: Optional[Index] = None,
async_index: Optional[AsyncIndex] = None,
index_url: Optional[str] = None,
index_token: Optional[str] = None,
embedding: Optional[Union[Embeddings, bool]] = None,
*,
namespace: str = "",
):
"""
Constructor for UpstashVectorStore.
If index or index_url and index_token are not provided, the constructor will
attempt to create an index using the environment variables
`UPSTASH_VECTOR_REST_URL`and `UPSTASH_VECTOR_REST_TOKEN`.
Args:
text_key: Key to store the text in metadata.
index: UpstashVector Index object.
async_index: UpstashVector AsyncIndex object, provide only if async
functions are needed
index_url: URL of the UpstashVector index.
index_token: Token of the UpstashVector index.
embedding: Embeddings object or a boolean. When false, no embedding
is applied. If true, Upstash embeddings are used. When Upstash
embeddings are used, text is sent directly to Upstash and
embedding is applied there instead of embedding in Langchain.
namespace: Namespace to use from the index.
Example:
.. code-block:: python
from langchain_community.vectorstores.upstash import UpstashVectorStore
from langchain_community.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = UpstashVectorStore(
embedding=embeddings,
index_url="...",
index_token="...",
namespace="..."
)
# With an existing index
from upstash_vector import Index
index = Index(url="...", token="...")
vectorstore = UpstashVectorStore(
embedding=embeddings,
index=index,
namespace="..."
)
"""
try:
from upstash_vector import AsyncIndex, Index
except ImportError:
raise ImportError(
"Could not import upstash_vector python package. "
"Please install it with `pip install upstash_vector`."
)
if index:
if not isinstance(index, Index):
raise ValueError(
"Passed index object should be an "
"instance of upstash_vector.Index, "
f"got {type(index)}"
)
self._index = index
logger.info("Using the index passed as parameter")
if async_index:
if not isinstance(async_index, AsyncIndex):
raise ValueError(
"Passed index object should be an "
"instance of upstash_vector.AsyncIndex, "
f"got {type(async_index)}"
)
self._async_index = async_index
logger.info("Using the async index passed as parameter")
if index_url and index_token:
self._index = Index(url=index_url, token=index_token)
self._async_index = AsyncIndex(url=index_url, token=index_token)
logger.info("Created index from the index_url and index_token parameters")
elif not index and not async_index:
self._index = Index.from_env()
self._async_index = AsyncIndex.from_env()
logger.info("Created index using environment variables")
self._embeddings = embedding
self._text_key = text_key
self._namespace = namespace
@property
def embeddings(self) -> Optional[Union[Embeddings, bool]]: # type: ignore
"""Access the query embedding object if available."""
return self._embeddings
def _embed_documents(
self, texts: Iterable[str]
) -> Union[List[List[float]], List[str]]:
"""Embed strings using the embeddings object"""
if not self._embeddings:
raise ValueError(
"No embeddings object provided. "
"Pass an embeddings object to the constructor."
)
if isinstance(self._embeddings, Embeddings):
return self._embeddings.embed_documents(list(texts))
# using self._embeddings is True, Upstash embeddings will be used.
# returning list of text as List[str]
return list(texts)
def _embed_query(self, text: str) -> Union[List[float], str]:
"""Embed query text using the embeddings object."""
if not self._embeddings:
raise ValueError(
"No embeddings object provided. "
"Pass an embeddings object to the constructor."
)
if isinstance(self._embeddings, Embeddings):
return self._embeddings.embed_query(text)
# using self._embeddings is True, Upstash embeddings will be used.
# returning query as it is
return text
def add_documents(
self,
documents: List[Document],
ids: Optional[List[str]] = None,
batch_size: int = 32,
embedding_chunk_size: int = 1000,
*,
namespace: Optional[str] = None,
**kwargs: Any,
) -> List[str]:
"""
Get the embeddings for the documents and add them to the vectorstore.
Documents are sent to the embeddings object
in batches of size `embedding_chunk_size`.
The embeddings are then upserted into the vectorstore
in batches of size `batch_size`.
Args:
documents: Iterable of Documents to add to the vectorstore.
batch_size: Batch size to use when upserting the embeddings.
Upstash supports at max 1000 vectors per request.
embedding_batch_size: Chunk size to use when embedding the texts.
namespace: Namespace to use from the index.
Returns:
List of ids from adding the texts into the vectorstore.
"""
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return self.add_texts(
texts,
metadatas=metadatas,
batch_size=batch_size,
ids=ids,
embedding_chunk_size=embedding_chunk_size,
namespace=namespace,
**kwargs,
)
async def aadd_documents(
self,
documents: Iterable[Document],
ids: Optional[List[str]] = None,
batch_size: int = 32,
embedding_chunk_size: int = 1000,
*,
namespace: Optional[str] = None,
**kwargs: Any,
) -> List[str]:
"""
Get the embeddings for the documents and add them to the vectorstore.
Documents are sent to the embeddings object
in batches of size `embedding_chunk_size`.
The embeddings are then upserted into the vectorstore
in batches of size `batch_size`.
Args:
documents: Iterable of Documents to add to the vectorstore.
batch_size: Batch size to use when upserting the embeddings.
Upstash supports at max 1000 vectors per request.
embedding_batch_size: Chunk size to use when embedding the texts.
namespace: Namespace to use from the index.
Returns:
List of ids from adding the texts into the vectorstore.
"""
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return await self.aadd_texts(
texts,
metadatas=metadatas,
ids=ids,
batch_size=batch_size,
embedding_chunk_size=embedding_chunk_size,
namespace=namespace,
**kwargs,
)
| |
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|
from __future__ import annotations
import logging
from typing import Any, Dict, List, Optional
from langchain_core.embeddings import Embeddings
from langchain_community.vectorstores.milvus import Milvus
logger = logging.getLogger(__name__)
class Zilliz(Milvus):
"""`Zilliz` vector store.
You need to have `pymilvus` installed and a
running Zilliz database.
See the following documentation for how to run a Zilliz instance:
https://docs.zilliz.com/docs/create-cluster
IF USING L2/IP metric IT IS HIGHLY SUGGESTED TO NORMALIZE YOUR DATA.
Args:
embedding_function (Embeddings): Function used to embed the text.
collection_name (str): Which Zilliz collection to use. Defaults to
"LangChainCollection".
connection_args (Optional[dict[str, any]]): The connection args used for
this class comes in the form of a dict.
consistency_level (str): The consistency level to use for a collection.
Defaults to "Session".
index_params (Optional[dict]): Which index params to use. Defaults to
HNSW/AUTOINDEX depending on service.
search_params (Optional[dict]): Which search params to use. Defaults to
default of index.
drop_old (Optional[bool]): Whether to drop the current collection. Defaults
to False.
auto_id (bool): Whether to enable auto id for primary key. Defaults to False.
If False, you needs to provide text ids (string less than 65535 bytes).
If True, Milvus will generate unique integers as primary keys.
The connection args used for this class comes in the form of a dict,
here are a few of the options:
address (str): The actual address of Zilliz
instance. Example address: "localhost:19530"
uri (str): The uri of Zilliz instance. Example uri:
"https://in03-ba4234asae.api.gcp-us-west1.zillizcloud.com",
host (str): The host of Zilliz instance. Default at "localhost",
PyMilvus will fill in the default host if only port is provided.
port (str/int): The port of Zilliz instance. Default at 19530, PyMilvus
will fill in the default port if only host is provided.
user (str): Use which user to connect to Zilliz instance. If user and
password are provided, we will add related header in every RPC call.
password (str): Required when user is provided. The password
corresponding to the user.
token (str): API key, for serverless clusters which can be used as
replacements for user and password.
secure (bool): Default is false. If set to true, tls will be enabled.
client_key_path (str): If use tls two-way authentication, need to
write the client.key path.
client_pem_path (str): If use tls two-way authentication, need to
write the client.pem path.
ca_pem_path (str): If use tls two-way authentication, need to write
the ca.pem path.
server_pem_path (str): If use tls one-way authentication, need to
write the server.pem path.
server_name (str): If use tls, need to write the common name.
Example:
.. code-block:: python
from langchain_community.vectorstores import Zilliz
from langchain_community.embeddings import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
# Connect to a Zilliz instance
milvus_store = Milvus(
embedding_function = embedding,
collection_name = "LangChainCollection",
connection_args = {
"uri": "https://in03-ba4234asae.api.gcp-us-west1.zillizcloud.com",
"user": "temp",
"password": "temp",
"token": "temp", # API key as replacements for user and password
"secure": True
}
drop_old: True,
)
Raises:
ValueError: If the pymilvus python package is not installed.
"""
def _create_index(self) -> None:
"""Create a index on the collection"""
from pymilvus import Collection, MilvusException
if isinstance(self.col, Collection) and self._get_index() is None:
try:
# If no index params, use a default AutoIndex based one
if self.index_params is None:
self.index_params = {
"metric_type": "L2",
"index_type": "AUTOINDEX",
"params": {},
}
try:
self.col.create_index(
self._vector_field,
index_params=self.index_params,
using=self.alias,
)
# If default did not work, most likely Milvus self-hosted
except MilvusException:
# Use HNSW based index
self.index_params = {
"metric_type": "L2",
"index_type": "HNSW",
"params": {"M": 8, "efConstruction": 64},
}
self.col.create_index(
self._vector_field,
index_params=self.index_params,
using=self.alias,
)
logger.debug(
"Successfully created an index on collection: %s",
self.collection_name,
)
except MilvusException as e:
logger.error(
"Failed to create an index on collection: %s", self.collection_name
)
raise e
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
collection_name: str = "LangChainCollection",
connection_args: Optional[Dict[str, Any]] = None,
consistency_level: str = "Session",
index_params: Optional[dict] = None,
search_params: Optional[dict] = None,
drop_old: bool = False,
*,
ids: Optional[List[str]] = None,
auto_id: bool = False,
**kwargs: Any,
) -> Zilliz:
"""Create a Zilliz collection, indexes it with HNSW, and insert data.
Args:
texts (List[str]): Text data.
embedding (Embeddings): Embedding function.
metadatas (Optional[List[dict]]): Metadata for each text if it exists.
Defaults to None.
collection_name (str, optional): Collection name to use. Defaults to
"LangChainCollection".
connection_args (dict[str, Any], optional): Connection args to use. Defaults
to DEFAULT_MILVUS_CONNECTION.
consistency_level (str, optional): Which consistency level to use. Defaults
to "Session".
index_params (Optional[dict], optional): Which index_params to use.
Defaults to None.
search_params (Optional[dict], optional): Which search params to use.
Defaults to None.
drop_old (Optional[bool], optional): Whether to drop the collection with
that name if it exists. Defaults to False.
ids (Optional[List[str]]): List of text ids.
auto_id (bool): Whether to enable auto id for primary key. Defaults to
False. If False, you needs to provide text ids (string less than 65535
bytes). If True, Milvus will generate unique integers as primary keys.
Returns:
Zilliz: Zilliz Vector Store
"""
vector_db = cls(
embedding_function=embedding,
collection_name=collection_name,
connection_args=connection_args or {},
consistency_level=consistency_level,
index_params=index_params,
search_params=search_params,
drop_old=drop_old,
auto_id=auto_id,
**kwargs,
)
vector_db.add_texts(texts=texts, metadatas=metadatas, ids=ids)
return vector_db
| |
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|
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[Dict[str, Any]]] = None,
ids: Optional[List[str]] = None,
batch_size: Optional[int] = None,
**kwargs: Any,
) -> List[str]:
"""Run texts through the embeddings and persist in vectorstore.
If the document IDs are passed, the existing documents (if any) will be
overwritten with the new ones.
Args:
texts (Iterable[str]): Iterable of strings to add to the vectorstore.
metadatas (Optional[List[Dict]]): Optional list of metadatas associated
with the texts.
ids (Optional[List[str]]): Optional list of ids associated with the texts.
IDs have to be unique strings across the collection.
If it is not specified uuids are generated and used as ids.
batch_size (Optional[int]): Optional batch size for bulk insertions.
Default is 100.
Returns:
List[str]:List of ids from adding the texts into the vectorstore.
"""
from couchbase.exceptions import DocumentExistsException
if not batch_size:
batch_size = self.DEFAULT_BATCH_SIZE
doc_ids: List[str] = []
if ids is None:
ids = [uuid.uuid4().hex for _ in texts]
if metadatas is None:
metadatas = [{} for _ in texts]
embedded_texts = self._embedding_function.embed_documents(list(texts))
documents_to_insert = [
{
id: {
self._text_key: text,
self._embedding_key: vector,
self._metadata_key: metadata,
}
for id, text, vector, metadata in zip(
ids, texts, embedded_texts, metadatas
)
}
]
# Insert in batches
for i in range(0, len(documents_to_insert), batch_size):
batch = documents_to_insert[i : i + batch_size]
try:
result = self._collection.upsert_multi(batch[0])
if result.all_ok:
doc_ids.extend(batch[0].keys())
except DocumentExistsException as e:
raise ValueError(f"Document already exists: {e}")
return doc_ids
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
"""Delete documents from the vector store by ids.
Args:
ids (List[str]): List of IDs of the documents to delete.
batch_size (Optional[int]): Optional batch size for bulk deletions.
Returns:
bool: True if all the documents were deleted successfully, False otherwise.
"""
from couchbase.exceptions import DocumentNotFoundException
if ids is None:
raise ValueError("No document ids provided to delete.")
batch_size = kwargs.get("batch_size", self.DEFAULT_BATCH_SIZE)
deletion_status = True
# Delete in batches
for i in range(0, len(ids), batch_size):
batch = ids[i : i + batch_size]
try:
result = self._collection.remove_multi(batch)
except DocumentNotFoundException as e:
deletion_status = False
raise ValueError(f"Document not found: {e}")
deletion_status &= result.all_ok
return deletion_status
@property
def embeddings(self) -> Embeddings:
"""Return the query embedding object."""
return self._embedding_function
def _format_metadata(self, row_fields: Dict[str, Any]) -> Dict[str, Any]:
"""Helper method to format the metadata from the Couchbase Search API.
Args:
row_fields (Dict[str, Any]): The fields to format.
Returns:
Dict[str, Any]: The formatted metadata.
"""
metadata = {}
for key, value in row_fields.items():
# Couchbase Search returns the metadata key with a prefix
# `metadata.` We remove it to get the original metadata key
if key.startswith(self._metadata_key):
new_key = key.split(self._metadata_key + ".")[-1]
metadata[new_key] = value
else:
metadata[key] = value
return metadata
def similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
search_options: Optional[Dict[str, Any]] = {},
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to embedding vector with their scores.
Args:
embedding (List[float]): Embedding vector to look up documents similar to.
k (int): Number of Documents to return.
Defaults to 4.
search_options (Optional[Dict[str, Any]]): Optional search options that are
passed to Couchbase search.
Defaults to empty dictionary.
fields (Optional[List[str]]): Optional list of fields to include in the
metadata of results. Note that these need to be stored in the index.
If nothing is specified, defaults to all the fields stored in the index.
Returns:
List of (Document, score) that are the most similar to the query vector.
"""
import couchbase.search as search
from couchbase.options import SearchOptions
from couchbase.vector_search import VectorQuery, VectorSearch
fields = kwargs.get("fields", ["*"])
# Document text field needs to be returned from the search
if fields != ["*"] and self._text_key not in fields:
fields.append(self._text_key)
search_req = search.SearchRequest.create(
VectorSearch.from_vector_query(
VectorQuery(
self._embedding_key,
embedding,
k,
)
)
)
try:
if self._scoped_index:
search_iter = self._scope.search(
self._index_name,
search_req,
SearchOptions(
limit=k,
fields=fields,
raw=search_options,
),
)
else:
search_iter = self._cluster.search(
index=self._index_name,
request=search_req,
options=SearchOptions(limit=k, fields=fields, raw=search_options),
)
docs_with_score = []
# Parse the results
for row in search_iter.rows():
text = row.fields.pop(self._text_key, "")
# Format the metadata from Couchbase
metadata = self._format_metadata(row.fields)
score = row.score
doc = Document(page_content=text, metadata=metadata)
docs_with_score.append((doc, score))
except Exception as e:
raise ValueError(f"Search failed with error: {e}")
return docs_with_score
def similarity_search(
self,
query: str,
k: int = 4,
search_options: Optional[Dict[str, Any]] = {},
**kwargs: Any,
) -> List[Document]:
"""Return documents most similar to embedding vector with their scores.
Args:
query (str): Query to look up for similar documents
k (int): Number of Documents to return.
Defaults to 4.
search_options (Optional[Dict[str, Any]]): Optional search options that are
passed to Couchbase search.
Defaults to empty dictionary
fields (Optional[List[str]]): Optional list of fields to include in the
metadata of results. Note that these need to be stored in the index.
If nothing is specified, defaults to all the fields stored in the index.
Returns:
List of Documents most similar to the query.
"""
query_embedding = self.embeddings.embed_query(query)
docs_with_scores = self.similarity_search_with_score_by_vector(
query_embedding, k, search_options, **kwargs
)
return [doc for doc, _ in docs_with_scores]
def similarity_search_with_score(
self,
query: str,
k: int = 4,
search_options: Optional[Dict[str, Any]] = {},
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return documents that are most similar to the query with their scores.
Args:
query (str): Query to look up for similar documents
k (int): Number of Documents to return.
Defaults to 4.
search_options (Optional[Dict[str, Any]]): Optional search options that are
passed to Couchbase search.
Defaults to empty dictionary.
fields (Optional[List[str]]): Optional list of fields to include in the
metadata of results. Note that these need to be stored in the index.
If nothing is specified, defaults to text and metadata fields.
Returns:
List of (Document, score) that are most similar to the query.
"""
query_embedding = self.embeddings.embed_query(query)
docs_with_score = self.similarity_search_with_score_by_vector(
query_embedding, k, search_options, **kwargs
)
return docs_with_score
| |
159375
|
@deprecated(
"0.0.27",
alternative="Use ElasticsearchStore class in langchain-elasticsearch package",
pending=True,
)
class ElasticVectorSearch(VectorStore):
"""
ElasticVectorSearch uses the brute force method of searching on vectors.
Recommended to use ElasticsearchStore instead, which gives you the option
to uses the approx HNSW algorithm which performs better on large datasets.
ElasticsearchStore also supports metadata filtering, customising the
query retriever and much more!
You can read more on ElasticsearchStore:
https://python.langchain.com/docs/integrations/vectorstores/elasticsearch
To connect to an `Elasticsearch` instance that does not require
login credentials, pass the Elasticsearch URL and index name along with the
embedding object to the constructor.
Example:
.. code-block:: python
from langchain_community.vectorstores import ElasticVectorSearch
from langchain_community.embeddings import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
elastic_vector_search = ElasticVectorSearch(
elasticsearch_url="http://localhost:9200",
index_name="test_index",
embedding=embedding
)
To connect to an Elasticsearch instance that requires login credentials,
including Elastic Cloud, use the Elasticsearch URL format
https://username:password@es_host:9243. For example, to connect to Elastic
Cloud, create the Elasticsearch URL with the required authentication details and
pass it to the ElasticVectorSearch constructor as the named parameter
elasticsearch_url.
You can obtain your Elastic Cloud URL and login credentials by logging in to the
Elastic Cloud console at https://cloud.elastic.co, selecting your deployment, and
navigating to the "Deployments" page.
To obtain your Elastic Cloud password for the default "elastic" user:
1. Log in to the Elastic Cloud console at https://cloud.elastic.co
2. Go to "Security" > "Users"
3. Locate the "elastic" user and click "Edit"
4. Click "Reset password"
5. Follow the prompts to reset the password
The format for Elastic Cloud URLs is
https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.
Example:
.. code-block:: python
from langchain_community.vectorstores import ElasticVectorSearch
from langchain_community.embeddings import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
elastic_host = "cluster_id.region_id.gcp.cloud.es.io"
elasticsearch_url = f"https://username:password@{elastic_host}:9243"
elastic_vector_search = ElasticVectorSearch(
elasticsearch_url=elasticsearch_url,
index_name="test_index",
embedding=embedding
)
Args:
elasticsearch_url (str): The URL for the Elasticsearch instance.
index_name (str): The name of the Elasticsearch index for the embeddings.
embedding (Embeddings): An object that provides the ability to embed text.
It should be an instance of a class that subclasses the Embeddings
abstract base class, such as OpenAIEmbeddings()
Raises:
ValueError: If the elasticsearch python package is not installed.
"""
def __init__(
self,
elasticsearch_url: str,
index_name: str,
embedding: Embeddings,
*,
ssl_verify: Optional[Dict[str, Any]] = None,
):
"""Initialize with necessary components."""
warnings.warn(
"ElasticVectorSearch will be removed in a future release. See"
"Elasticsearch integration docs on how to upgrade."
)
try:
import elasticsearch
except ImportError:
raise ImportError(
"Could not import elasticsearch python package. "
"Please install it with `pip install elasticsearch`."
)
self.embedding = embedding
self.index_name = index_name
_ssl_verify = ssl_verify or {}
try:
self.client = elasticsearch.Elasticsearch(
elasticsearch_url,
**_ssl_verify,
headers={"user-agent": self.get_user_agent()},
)
except ValueError as e:
raise ValueError(
f"Your elasticsearch client string is mis-formatted. Got error: {e} "
)
@staticmethod
def get_user_agent() -> str:
from langchain_community import __version__
return f"langchain-py-dvs/{__version__}"
@property
def embeddings(self) -> Embeddings:
return self.embedding
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
refresh_indices: bool = True,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of unique IDs.
refresh_indices: bool to refresh ElasticSearch indices
Returns:
List of ids from adding the texts into the vectorstore.
"""
try:
from elasticsearch.exceptions import NotFoundError
from elasticsearch.helpers import bulk
except ImportError:
raise ImportError(
"Could not import elasticsearch python package. "
"Please install it with `pip install elasticsearch`."
)
requests = []
ids = ids or [str(uuid.uuid4()) for _ in texts]
embeddings = self.embedding.embed_documents(list(texts))
dim = len(embeddings[0])
mapping = _default_text_mapping(dim)
# check to see if the index already exists
try:
self.client.indices.get(index=self.index_name)
except NotFoundError:
# TODO would be nice to create index before embedding,
# just to save expensive steps for last
self.create_index(self.client, self.index_name, mapping)
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
request = {
"_op_type": "index",
"_index": self.index_name,
"vector": embeddings[i],
"text": text,
"metadata": metadata,
"_id": ids[i],
}
requests.append(request)
bulk(self.client, requests)
if refresh_indices:
self.client.indices.refresh(index=self.index_name)
return ids
def similarity_search(
self, query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.
"""
docs_and_scores = self.similarity_search_with_score(query, k, filter=filter)
documents = [d[0] for d in docs_and_scores]
return documents
def similarity_search_with_score(
self, query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.
"""
embedding = self.embedding.embed_query(query)
script_query = _default_script_query(embedding, filter)
response = self.client_search(
self.client, self.index_name, script_query, size=k
)
hits = [hit for hit in response["hits"]["hits"]]
docs_and_scores = [
(
Document(
page_content=hit["_source"]["text"],
metadata=hit["_source"]["metadata"],
),
hit["_score"],
)
for hit in hits
]
return docs_and_scores
| |
159376
|
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
index_name: Optional[str] = None,
refresh_indices: bool = True,
**kwargs: Any,
) -> ElasticVectorSearch:
"""Construct ElasticVectorSearch wrapper from raw documents.
This is a user-friendly interface that:
1. Embeds documents.
2. Creates a new index for the embeddings in the Elasticsearch instance.
3. Adds the documents to the newly created Elasticsearch index.
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain_community.vectorstores import ElasticVectorSearch
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
elastic_vector_search = ElasticVectorSearch.from_texts(
texts,
embeddings,
elasticsearch_url="http://localhost:9200"
)
"""
elasticsearch_url = get_from_dict_or_env(
kwargs, "elasticsearch_url", "ELASTICSEARCH_URL"
)
if "elasticsearch_url" in kwargs:
del kwargs["elasticsearch_url"]
index_name = index_name or uuid.uuid4().hex
vectorsearch = cls(elasticsearch_url, index_name, embedding, **kwargs)
vectorsearch.add_texts(
texts, metadatas=metadatas, ids=ids, refresh_indices=refresh_indices
)
return vectorsearch
def create_index(self, client: Any, index_name: str, mapping: Dict) -> None:
version_num = client.info()["version"]["number"][0]
version_num = int(version_num)
if version_num >= 8:
client.indices.create(index=index_name, mappings=mapping)
else:
client.indices.create(index=index_name, body={"mappings": mapping})
def client_search(
self, client: Any, index_name: str, script_query: Dict, size: int
) -> Any:
version_num = client.info()["version"]["number"][0]
version_num = int(version_num)
if version_num >= 8:
response = client.search(index=index_name, query=script_query, size=size)
else:
response = client.search(
index=index_name, body={"query": script_query, "size": size}
)
return response
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> None:
"""Delete by vector IDs.
Args:
ids: List of ids to delete.
"""
if ids is None:
raise ValueError("No ids provided to delete.")
# TODO: Check if this can be done in bulk
for id in ids:
self.client.delete(index=self.index_name, id=id)
| |
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|
class Redis(VectorStore):
"""Redis vector database.
Deployment Options:
Below, we will use a local deployment as an example. However, Redis can be deployed in all of the following ways:
- [Redis Cloud](https://redis.com/redis-enterprise-cloud/overview/)
- [Docker (Redis Stack)](https://hub.docker.com/r/redis/redis-stack)
- Cloud marketplaces: [AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-e6y7ork67pjwg?sr=0-2&ref_=beagle&applicationId=AWSMPContessa), [Google Marketplace](https://console.cloud.google.com/marketplace/details/redislabs-public/redis-enterprise?pli=1), or [Azure Marketplace](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/garantiadata.redis_enterprise_1sp_public_preview?tab=Overview)
- On-premise: [Redis Enterprise Software](https://redis.com/redis-enterprise-software/overview/)
- Kubernetes: [Redis Enterprise Software on Kubernetes](https://docs.redis.com/latest/kubernetes/)
Setup:
Install ``redis``, ``redisvl``, and ``langchain-community`` and run Redis locally.
.. code-block:: bash
pip install -qU redis redisvl langchain-community
docker run -d -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
Key init args — indexing params:
index_name: str
Name of the index.
index_schema: Optional[Union[Dict[str, ListOfDict], str, os.PathLike]]
Schema of the index and the vector schema. Can be a dict, or path to yaml file.
embedding: Embeddings
Embedding function to use.
Key init args — client params:
redis_url: str
Redis connection url.
Instantiate:
.. code-block:: python
from langchain_community.vectorstores.redis import Redis
from langchain_openai import OpenAIEmbeddings
vector_store = Redis(
redis_url="redis://localhost:6379",
embedding=OpenAIEmbeddings(),
index_name="users",
)
Add Documents:
.. code-block:: python
from langchain_core.documents import Document
document_1 = Document(page_content="foo", metadata={"baz": "bar"})
document_2 = Document(page_content="thud", metadata={"bar": "baz"})
document_3 = Document(page_content="i will be deleted :(")
documents = [document_1, document_2, document_3]
ids = ["1", "2", "3"]
vector_store.add_documents(documents=documents, ids=ids)
Delete Documents:
.. code-block:: python
vector_store.delete(ids=["3"])
Search:
.. code-block:: python
results = vector_store.similarity_search(query="thud",k=1)
for doc in results:
print(f"* {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* thud [{'id': 'doc:users:2'}]
Search with filter:
.. code-block:: python
from langchain_community.vectorstores.redis import RedisTag
results = vector_store.similarity_search(query="thud",k=1,filter=(RedisTag("baz") != "bar"))
for doc in results:
print(f"* {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* thud [{'id': 'doc:users:2'}]
Search with score:
.. code-block:: python
results = vector_store.similarity_search_with_score(query="qux",k=1)
for doc, score in results:
print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* [SIM=0.167700] foo [{'id': 'doc:users:1'}]
Async:
.. code-block:: python
# add documents
# await vector_store.aadd_documents(documents=documents, ids=ids)
# delete documents
# await vector_store.adelete(ids=["3"])
# search
# results = vector_store.asimilarity_search(query="thud",k=1)
# search with score
results = await vector_store.asimilarity_search_with_score(query="qux",k=1)
for doc,score in results:
print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* [SIM=0.167700] foo [{'id': 'doc:users:1'}]
Use as Retriever:
.. code-block:: python
retriever = vector_store.as_retriever(
search_type="mmr",
search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5},
)
retriever.invoke("thud")
.. code-block:: python
[Document(metadata={'id': 'doc:users:2'}, page_content='thud')]
**Advanced examples:**
Custom vector schema can be supplied to change the way that
Redis creates the underlying vector schema. This is useful
for production use cases where you want to optimize the
vector schema for your use case. ex. using HNSW instead of
FLAT (knn) which is the default
.. code-block:: python
vector_schema = {
"algorithm": "HNSW"
}
rds = Redis.from_texts(
texts, # a list of strings
metadata, # a list of metadata dicts
embeddings, # an Embeddings object
vector_schema=vector_schema,
redis_url="redis://localhost:6379",
)
Custom index schema can be supplied to change the way that the
metadata is indexed. This is useful for you would like to use the
hybrid querying (filtering) capability of Redis.
By default, this implementation will automatically generate the index
schema according to the following rules:
- All strings are indexed as text fields
- All numbers are indexed as numeric fields
- All lists of strings are indexed as tag fields (joined by
langchain_community.vectorstores.redis.constants.REDIS_TAG_SEPARATOR)
- All None values are not indexed but still stored in Redis these are
not retrievable through the interface here, but the raw Redis client
can be used to retrieve them.
- All other types are not indexed
To override these rules, you can pass in a custom index schema like the following
.. code-block:: yaml
tag:
- name: credit_score
text:
- name: user
- name: job
Typically, the ``credit_score`` field would be a text field since it's a string,
however, we can override this behavior by specifying the field type as shown with
the yaml config (can also be a dictionary) above and the code below.
.. code-block:: python
rds = Redis.from_texts(
texts, # a list of strings
metadata, # a list of metadata dicts
embeddings, # an Embeddings object
index_schema="path/to/index_schema.yaml", # can also be a dictionary
redis_url="redis://localhost:6379",
)
When connecting to an existing index where a custom schema has been applied, it's
important to pass in the same schema to the ``from_existing_index`` method.
Otherwise, the schema for newly added samples will be incorrect and metadata
will not be returned.
""" # noqa: E501
DEFAULT_VECTOR_SCHEMA = {
"name": "content_vector",
"algorithm": "FLAT",
"dims": 1536,
"distance_metric": "COSINE",
"datatype": "FLOAT32",
}
| |
159390
|
ssmethod
def from_texts(
cls: Type[Redis],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
index_name: Optional[str] = None,
index_schema: Optional[Union[Dict[str, ListOfDict], str, os.PathLike]] = None,
vector_schema: Optional[Dict[str, Union[str, int]]] = None,
**kwargs: Any,
) -> Redis:
"""Create a Redis vectorstore from a list of texts.
This is a user-friendly interface that:
1. Embeds documents.
2. Creates a new Redis index if it doesn't already exist
3. Adds the documents to the newly created Redis index.
This method will generate schema based on the metadata passed in
if the `index_schema` is not defined. If the `index_schema` is defined,
it will compare against the generated schema and warn if there are
differences. If you are purposefully defining the schema for the
metadata, then you can ignore that warning.
To examine the schema options, initialize an instance of this class
and print out the schema using the `Redis.schema`` property. This
will include the content and content_vector classes which are
always present in the langchain schema.
Example:
.. code-block:: python
from langchain_community.vectorstores import Redis
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
redisearch = RediSearch.from_texts(
texts,
embeddings,
redis_url="redis://username:password@localhost:6379"
)
Args:
texts (List[str]): List of texts to add to the vectorstore.
embedding (Embeddings): Embedding model class (i.e. OpenAIEmbeddings)
for embedding queries.
metadatas (Optional[List[dict]], optional): Optional list of metadata dicts
to add to the vectorstore. Defaults to None.
index_name (Optional[str], optional): Optional name of the index to create
or add to. Defaults to None.
index_schema (Optional[Union[Dict[str, ListOfDict], str, os.PathLike]],
optional):
Optional fields to index within the metadata. Overrides generated
schema. Defaults to None.
vector_schema (Optional[Dict[str, Union[str, int]]], optional): Optional
vector schema to use. Defaults to None.
**kwargs (Any): Additional keyword arguments to pass to the Redis client.
Returns:
Redis: Redis VectorStore instance.
Raises:
ValueError: If the number of metadatas does not match the number of texts.
ImportError: If the redis python package is not installed.
"""
instance, _ = cls.from_texts_return_keys(
texts,
embedding,
metadatas=metadatas,
index_name=index_name,
index_schema=index_schema,
vector_schema=vector_schema,
**kwargs,
)
return instance
@classmethod
def from_existing_index(
cls,
embedding: Embeddings,
index_name: str,
schema: Union[Dict[str, ListOfDict], str, os.PathLike, Dict[str, ListOfDict]],
key_prefix: Optional[str] = None,
**kwargs: Any,
) -> Redis:
"""Connect to an existing Redis index.
Example:
.. code-block:: python
from langchain_community.vectorstores import Redis
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
# must pass in schema and key_prefix from another index
existing_rds = Redis.from_existing_index(
embeddings,
index_name="my-index",
schema=rds.schema, # schema dumped from another index
key_prefix=rds.key_prefix, # key prefix from another index
redis_url="redis://username:password@localhost:6379",
)
Args:
embedding (Embeddings): Embedding model class (i.e. OpenAIEmbeddings)
for embedding queries.
index_name (str): Name of the index to connect to.
schema (Union[Dict[str, str], str, os.PathLike, Dict[str, ListOfDict]]):
Schema of the index and the vector schema. Can be a dict, or path to
yaml file.
key_prefix (Optional[str]): Prefix to use for all keys in Redis associated
with this index.
**kwargs (Any): Additional keyword arguments to pass to the Redis client.
Returns:
Redis: Redis VectorStore instance.
Raises:
ValueError: If the index does not exist.
ImportError: If the redis python package is not installed.
"""
redis_url = get_from_dict_or_env(kwargs, "redis_url", "REDIS_URL")
# We need to first remove redis_url from kwargs,
# otherwise passing it to Redis will result in an error.
if "redis_url" in kwargs:
kwargs.pop("redis_url")
# Create instance
# init the class -- if Redis is unavailable, will throw exception
instance = cls(
redis_url,
index_name,
embedding,
index_schema=schema,
key_prefix=key_prefix,
**kwargs,
)
# Check for existence of the declared index
if not check_index_exists(instance.client, index_name):
# Will only raise if the running Redis server does not
# have a record of this particular index
raise ValueError(
f"Redis failed to connect: Index {index_name} does not exist."
)
return instance
@property
def schema(self) -> Dict[str, List[Any]]:
"""Return the schema of the index."""
return self._schema.as_dict()
def write_schema(self, path: Union[str, os.PathLike]) -> None:
"""Write the schema to a yaml file."""
with open(path, "w+") as f:
yaml.dump(self.schema, f)
def delete(
self,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> bool:
"""
Delete a Redis entry.
Args:
ids: List of ids (keys in redis) to delete.
redis_url: Redis connection url. This should be passed in the kwargs
or set as an environment variable: REDIS_URL.
Returns:
bool: Whether or not the deletions were successful.
Raises:
ValueError: If the redis python package is not installed.
ValueError: If the ids (keys in redis) are not provided
"""
client = self.client
# Check if index exists
try:
if ids:
client.delete(*ids)
logger.info("Entries deleted")
return True
except: # noqa: E722
# ids does not exist
return False
@staticmethod
def drop_index(
index_name: str,
delete_documents: bool,
**kwargs: Any,
) -> bool:
"""
Drop a Redis search index.
Args:
index_name (str): Name of the index to drop.
delete_documents (bool): Whether to drop the associated documents.
Returns:
bool: Whether or not the drop was successful.
"""
redis_url = get_from_dict_or_env(kwargs, "redis_url", "REDIS_URL")
try:
import redis # noqa: F401
except ImportError:
raise ImportError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
)
try:
# We need to first remove redis_url from kwargs,
# otherwise passing it to Redis will result in an error.
if "redis_url" in kwargs:
kwargs.pop("redis_url")
client = get_client(redis_url=redis_url, **kwargs)
except ValueError as e:
raise ValueError(f"Your redis connected error: {e}")
# Check if index exists
try:
client.ft(index_name).dropindex(delete_documents)
logger.info("Drop index")
return True
except: # noqa: E722
# Index not exist
return False
| |
159392
|
similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[RedisFilterExpression] = None,
return_metadata: bool = True,
distance_threshold: Optional[float] = None,
**kwargs: Any,
) -> List[Document]:
"""Run similarity search between a query vector and the indexed vectors.
Args:
embedding (List[float]): The query vector for which to find similar
documents.
k (int): The number of documents to return. Default is 4.
filter (RedisFilterExpression, optional): Optional metadata filter.
Defaults to None.
return_metadata (bool, optional): Whether to return metadata.
Defaults to True.
distance_threshold (Optional[float], optional): Maximum vector distance
between selected documents and the query vector. Defaults to None.
Returns:
List[Document]: A list of documents that are most similar to the query
text.
"""
try:
import redis
except ImportError as e:
raise ImportError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
) from e
if "score_threshold" in kwargs:
logger.warning(
"score_threshold is deprecated. Use distance_threshold instead."
+ "score_threshold should only be used in "
+ "similarity_search_with_relevance_scores."
+ "score_threshold will be removed in a future release.",
)
redis_query, params_dict = self._prepare_query(
embedding,
k=k,
filter=filter,
distance_threshold=distance_threshold,
with_metadata=return_metadata,
with_distance=False,
)
# Perform vector search
# ignore type because redis-py is wrong about bytes
try:
results = self.client.ft(self.index_name).search(redis_query, params_dict) # type: ignore
except redis.exceptions.ResponseError as e:
# split error message and see if it starts with "Syntax"
if str(e).split(" ")[0] == "Syntax":
raise ValueError(
"Query failed with syntax error. "
+ "This is likely due to malformation of "
+ "filter, vector, or query argument"
) from e
raise e
# Prepare document results
docs = []
for result in results.docs:
metadata = {}
if return_metadata:
metadata = {"id": result.id}
metadata.update(self._collect_metadata(result))
content_key = self._schema.content_key
docs.append(
Document(page_content=getattr(result, content_key), metadata=metadata)
)
return docs
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[RedisFilterExpression] = None,
return_metadata: bool = True,
distance_threshold: Optional[float] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query (str): Text to look up documents similar to.
k (int): Number of Documents to return. Defaults to 4.
fetch_k (int): Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float): Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter (RedisFilterExpression, optional): Optional metadata filter.
Defaults to None.
return_metadata (bool, optional): Whether to return metadata.
Defaults to True.
distance_threshold (Optional[float], optional): Maximum vector distance
between selected documents and the query vector. Defaults to None.
Returns:
List[Document]: A list of Documents selected by maximal marginal relevance.
"""
# Embed the query
query_embedding = self._embeddings.embed_query(query)
# Fetch the initial documents
prefetch_docs = self.similarity_search_by_vector(
query_embedding,
k=fetch_k,
filter=filter,
return_metadata=return_metadata,
distance_threshold=distance_threshold,
**kwargs,
)
prefetch_ids = [doc.metadata["id"] for doc in prefetch_docs]
# Get the embeddings for the fetched documents
prefetch_embeddings = [
_buffer_to_array(
cast(
bytes,
self.client.hget(prefetch_id, self._schema.content_vector_key),
),
dtype=self._schema.vector_dtype,
)
for prefetch_id in prefetch_ids
]
# Select documents using maximal marginal relevance
selected_indices = maximal_marginal_relevance(
np.array(query_embedding), prefetch_embeddings, lambda_mult=lambda_mult, k=k
)
selected_docs = [prefetch_docs[i] for i in selected_indices]
return selected_docs
def _collect_metadata(self, result: "Document") -> Dict[str, Any]:
"""Collect metadata from Redis.
Method ensures that there isn't a mismatch between the metadata
and the index schema passed to this class by the user or generated
by this class.
Args:
result (Document): redis.commands.search.Document object returned
from Redis.
Returns:
Dict[str, Any]: Collected metadata.
"""
# new metadata dict as modified by this method
meta = {}
for key in self._schema.metadata_keys:
try:
meta[key] = getattr(result, key)
except AttributeError:
# warning about attribute missing
logger.warning(
f"Metadata key {key} not found in metadata. "
+ "Setting to None. \n"
+ "Metadata fields defined for this instance: "
+ f"{self._schema.metadata_keys}"
)
meta[key] = None
return meta
def _prepare_query(
self,
query_embedding: List[float],
k: int = 4,
filter: Optional[RedisFilterExpression] = None,
distance_threshold: Optional[float] = None,
with_metadata: bool = True,
with_distance: bool = False,
) -> Tuple["Query", Dict[str, Any]]:
# Creates Redis query
params_dict: Dict[str, Union[str, bytes, float]] = {
"vector": _array_to_buffer(query_embedding, self._schema.vector_dtype),
}
# prepare return fields including score
return_fields = [self._schema.content_key]
if with_distance:
return_fields.append("distance")
if with_metadata:
return_fields.extend(self._schema.metadata_keys)
if distance_threshold:
params_dict["distance_threshold"] = distance_threshold
return (
self._prepare_range_query(
k, filter=filter, return_fields=return_fields
),
params_dict,
)
return (
self._prepare_vector_query(k, filter=filter, return_fields=return_fields),
params_dict,
)
def _prepare_range_query(
self,
k: int,
filter: Optional[RedisFilterExpression] = None,
return_fields: Optional[List[str]] = None,
) -> "Query":
try:
from redis.commands.search.query import Query
except ImportError as e:
raise ImportError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
) from e
return_fields = return_fields or []
vector_key = self._schema.content_vector_key
base_query = f"@{vector_key}:[VECTOR_RANGE $distance_threshold $vector]"
if filter:
base_query = str(filter) + " " + base_query
query_string = base_query + "=>{$yield_distance_as: distance}"
return (
Query(query_string)
.return_fields(*return_fields)
.sort_by("distance")
.paging(0, k)
.dialect(2)
)
def _prepare_vector_query(
self,
k: int,
filter: Optional[RedisFilterExpression] = None,
return_fields: Optional[List[str]] = None,
) -> "Query":
"""Prepare query for vector search.
Args:
k: Number of results to return.
filter: Optional metadata filter.
Returns:
query: Query object.
"""
try:
from redis.commands.search.query import Query
except ImportError as e:
raise ImportError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
) from e
return_fields = return_fields or []
query_prefix = "*"
if filter:
query_prefix = f"{str(filter)}"
vector_key = self._schema.content_vector_key
base_query = f"({query_prefix})=>[KNN {k} @{vector_key} $vector AS distance]"
query = (
Query(base_query)
.return_fields(*return_fields)
.sort_by("distance")
.paging(0, k)
.dialect(2)
)
return query
| |
159394
|
s RedisVectorStoreRetriever(VectorStoreRetriever):
"""Retriever for Redis VectorStore."""
vectorstore: Redis
"""Redis VectorStore."""
search_type: str = "similarity"
"""Type of search to perform. Can be either
'similarity',
'similarity_distance_threshold',
'similarity_score_threshold'
"""
search_kwargs: Dict[str, Any] = {
"k": 4,
"score_threshold": 0.9,
# set to None to avoid distance used in score_threshold search
"distance_threshold": None,
}
"""Default search kwargs."""
allowed_search_types = [
"similarity",
"similarity_distance_threshold",
"similarity_score_threshold",
"mmr",
]
"""Allowed search types."""
model_config = ConfigDict(
arbitrary_types_allowed=True,
)
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
if self.search_type == "similarity":
docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
elif self.search_type == "similarity_distance_threshold":
if self.search_kwargs["distance_threshold"] is None:
raise ValueError(
"distance_threshold must be provided for "
+ "similarity_distance_threshold retriever"
)
docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
elif self.search_type == "similarity_score_threshold":
docs_and_similarities = (
self.vectorstore.similarity_search_with_relevance_scores(
query, **self.search_kwargs
)
)
docs = [doc for doc, _ in docs_and_similarities]
elif self.search_type == "mmr":
docs = self.vectorstore.max_marginal_relevance_search(
query, **self.search_kwargs
)
else:
raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
async def _aget_relevant_documents(
self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
) -> List[Document]:
if self.search_type == "similarity":
docs = await self.vectorstore.asimilarity_search(
query, **self.search_kwargs
)
elif self.search_type == "similarity_distance_threshold":
if self.search_kwargs["distance_threshold"] is None:
raise ValueError(
"distance_threshold must be provided for "
+ "similarity_distance_threshold retriever"
)
docs = await self.vectorstore.asimilarity_search(
query, **self.search_kwargs
)
elif self.search_type == "similarity_score_threshold":
docs_and_similarities = (
await self.vectorstore.asimilarity_search_with_relevance_scores(
query, **self.search_kwargs
)
)
docs = [doc for doc, _ in docs_and_similarities]
elif self.search_type == "mmr":
docs = await self.vectorstore.amax_marginal_relevance_search(
query, **self.search_kwargs
)
else:
raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
"""Add documents to vectorstore."""
return self.vectorstore.add_documents(documents, **kwargs)
async def aadd_documents(
self, documents: List[Document], **kwargs: Any
) -> List[str]:
"""Add documents to vectorstore."""
return await self.vectorstore.aadd_documents(documents, **kwargs)
| |
159402
|
from abc import ABC
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Type
import numpy as np
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore
from pydantic import Field
from langchain_community.vectorstores.utils import maximal_marginal_relevance
if TYPE_CHECKING:
from docarray import BaseDoc
from docarray.index.abstract import BaseDocIndex
def _check_docarray_import() -> None:
try:
import docarray
da_version = docarray.__version__.split(".")
if int(da_version[0]) == 0 and int(da_version[1]) <= 31:
raise ImportError(
f"To use the DocArrayHnswSearch VectorStore the docarray "
f"version >=0.32.0 is expected, received: {docarray.__version__}."
f"To upgrade, please run: `pip install -U docarray`."
)
except ImportError:
raise ImportError(
"Could not import docarray python package. "
"Please install it with `pip install docarray`."
)
class DocArrayIndex(VectorStore, ABC):
"""Base class for `DocArray` based vector stores."""
def __init__(
self,
doc_index: "BaseDocIndex",
embedding: Embeddings,
):
"""Initialize a vector store from DocArray's DocIndex."""
self.doc_index = doc_index
self.embedding = embedding
@staticmethod
def _get_doc_cls(**embeddings_params: Any) -> Type["BaseDoc"]:
"""Get docarray Document class describing the schema of DocIndex."""
from docarray import BaseDoc
from docarray.typing import NdArray
class DocArrayDoc(BaseDoc):
text: Optional[str] = Field(default=None)
embedding: Optional[NdArray] = Field(**embeddings_params)
metadata: Optional[dict] = Field(default=None)
return DocArrayDoc
@property
def doc_cls(self) -> Type["BaseDoc"]:
if self.doc_index._schema is None:
raise ValueError("doc_index expected to have non-null _schema attribute.")
return self.doc_index._schema
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Embed texts and add to the vector store.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
Returns:
List of ids from adding the texts into the vectorstore.
"""
ids: List[str] = []
embeddings = self.embedding.embed_documents(list(texts))
for i, (t, e) in enumerate(zip(texts, embeddings)):
m = metadatas[i] if metadatas else {}
doc = self.doc_cls(text=t, embedding=e, metadata=m)
self.doc_index.index([doc])
ids.append(str(doc.id))
return ids
def similarity_search_with_score(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of documents most similar to the query text and
cosine distance in float for each.
Lower score represents more similarity.
"""
query_embedding = self.embedding.embed_query(query)
query_doc = self.doc_cls(embedding=query_embedding) # type: ignore
docs, scores = self.doc_index.find(query_doc, search_field="embedding", limit=k)
result = [
(Document(page_content=doc.text, metadata=doc.metadata), score)
for doc, score in zip(docs, scores)
]
return result
def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.
"""
results = self.similarity_search_with_score(query, k=k, **kwargs)
return [doc for doc, _ in results]
def _similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores, normalized on a scale from 0 to 1.
0 is dissimilar, 1 is most similar.
"""
raise NotImplementedError()
def similarity_search_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query vector.
"""
query_doc = self.doc_cls(embedding=embedding) # type: ignore
docs = self.doc_index.find(
query_doc, search_field="embedding", limit=k
).documents
result = [
Document(page_content=doc.text, metadata=doc.metadata) for doc in docs
]
return result
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
query_embedding = self.embedding.embed_query(query)
query_doc = self.doc_cls(embedding=query_embedding) # type: ignore
docs = self.doc_index.find(
query_doc, search_field="embedding", limit=fetch_k
).documents
mmr_selected = maximal_marginal_relevance(
np.array(query_embedding), docs.embedding, k=k
)
results = [
Document(page_content=docs[idx].text, metadata=docs[idx].metadata)
for idx in mmr_selected
]
return results
| |
159461
|
class ConfluenceLoader(BaseLoader):
"""Load `Confluence` pages.
Port of https://llamahub.ai/l/confluence
This currently supports username/api_key, Oauth2 login or personal access token
authentication.
Specify a list page_ids and/or space_key to load in the corresponding pages into
Document objects, if both are specified the union of both sets will be returned.
You can also specify a boolean `include_attachments` to include attachments, this
is set to False by default, if set to True all attachments will be downloaded and
ConfluenceLoader will extract the text from the attachments and add it to the
Document object. Currently supported attachment types are: PDF, PNG, JPEG/JPG,
SVG, Word and Excel.
Confluence API supports difference format of page content. The storage format is the
raw XML representation for storage. The view format is the HTML representation for
viewing with macros are rendered as though it is viewed by users. You can pass
a enum `content_format` argument to specify the content format, this is
set to `ContentFormat.STORAGE` by default, the supported values are:
`ContentFormat.EDITOR`, `ContentFormat.EXPORT_VIEW`,
`ContentFormat.ANONYMOUS_EXPORT_VIEW`, `ContentFormat.STORAGE`,
and `ContentFormat.VIEW`.
Hint: space_key and page_id can both be found in the URL of a page in Confluence
- https://yoursite.atlassian.com/wiki/spaces/<space_key>/pages/<page_id>
Example:
.. code-block:: python
from langchain_community.document_loaders import ConfluenceLoader
loader = ConfluenceLoader(
url="https://yoursite.atlassian.com/wiki",
username="me",
api_key="12345",
space_key="SPACE",
limit=50,
)
documents = loader.load()
# Server on perm
loader = ConfluenceLoader(
url="https://confluence.yoursite.com/",
username="me",
api_key="your_password",
cloud=False,
space_key="SPACE",
limit=50,
)
documents = loader.load()
:param url: _description_
:type url: str
:param api_key: _description_, defaults to None
:type api_key: str, optional
:param username: _description_, defaults to None
:type username: str, optional
:param oauth2: _description_, defaults to {}
:type oauth2: dict, optional
:param token: _description_, defaults to None
:type token: str, optional
:param cloud: _description_, defaults to True
:type cloud: bool, optional
:param number_of_retries: How many times to retry, defaults to 3
:type number_of_retries: Optional[int], optional
:param min_retry_seconds: defaults to 2
:type min_retry_seconds: Optional[int], optional
:param max_retry_seconds: defaults to 10
:type max_retry_seconds: Optional[int], optional
:param confluence_kwargs: additional kwargs to initialize confluence with
:type confluence_kwargs: dict, optional
:param space_key: Space key retrieved from a confluence URL, defaults to None
:type space_key: Optional[str], optional
:param page_ids: List of specific page IDs to load, defaults to None
:type page_ids: Optional[List[str]], optional
:param label: Get all pages with this label, defaults to None
:type label: Optional[str], optional
:param cql: CQL Expression, defaults to None
:type cql: Optional[str], optional
:param include_restricted_content: defaults to False
:type include_restricted_content: bool, optional
:param include_archived_content: Whether to include archived content,
defaults to False
:type include_archived_content: bool, optional
:param include_attachments: defaults to False
:type include_attachments: bool, optional
:param include_comments: defaults to False
:type include_comments: bool, optional
:param content_format: Specify content format, defaults to
ContentFormat.STORAGE, the supported values are:
`ContentFormat.EDITOR`, `ContentFormat.EXPORT_VIEW`,
`ContentFormat.ANONYMOUS_EXPORT_VIEW`,
`ContentFormat.STORAGE`, and `ContentFormat.VIEW`.
:type content_format: ContentFormat
:param limit: Maximum number of pages to retrieve per request, defaults to 50
:type limit: int, optional
:param max_pages: Maximum number of pages to retrieve in total, defaults 1000
:type max_pages: int, optional
:param ocr_languages: The languages to use for the Tesseract agent. To use a
language, you'll first need to install the appropriate
Tesseract language pack.
:type ocr_languages: str, optional
:param keep_markdown_format: Whether to keep the markdown format, defaults to
False
:type keep_markdown_format: bool
:param keep_newlines: Whether to keep the newlines format, defaults to
False
:type keep_newlines: bool
:raises ValueError: Errors while validating input
:raises ImportError: Required dependencies not installed.
"""
def __init__(
self,
url: str,
api_key: Optional[str] = None,
username: Optional[str] = None,
session: Optional[requests.Session] = None,
oauth2: Optional[dict] = None,
token: Optional[str] = None,
cloud: Optional[bool] = True,
number_of_retries: Optional[int] = 3,
min_retry_seconds: Optional[int] = 2,
max_retry_seconds: Optional[int] = 10,
confluence_kwargs: Optional[dict] = None,
*,
space_key: Optional[str] = None,
page_ids: Optional[List[str]] = None,
label: Optional[str] = None,
cql: Optional[str] = None,
include_restricted_content: bool = False,
include_archived_content: bool = False,
include_attachments: bool = False,
include_comments: bool = False,
content_format: ContentFormat = ContentFormat.STORAGE,
limit: Optional[int] = 50,
max_pages: Optional[int] = 1000,
ocr_languages: Optional[str] = None,
keep_markdown_format: bool = False,
keep_newlines: bool = False,
):
self.space_key = space_key
self.page_ids = page_ids
self.label = label
self.cql = cql
self.include_restricted_content = include_restricted_content
self.include_archived_content = include_archived_content
self.include_attachments = include_attachments
self.include_comments = include_comments
self.content_format = content_format
self.limit = limit
self.max_pages = max_pages
self.ocr_languages = ocr_languages
self.keep_markdown_format = keep_markdown_format
self.keep_newlines = keep_newlines
confluence_kwargs = confluence_kwargs or {}
errors = ConfluenceLoader.validate_init_args(
url=url,
api_key=api_key,
username=username,
session=session,
oauth2=oauth2,
token=token,
)
if errors:
raise ValueError(f"Error(s) while validating input: {errors}")
try:
from atlassian import Confluence
except ImportError:
raise ImportError(
"`atlassian` package not found, please run "
"`pip install atlassian-python-api`"
)
self.base_url = url
self.number_of_retries = number_of_retries
self.min_retry_seconds = min_retry_seconds
self.max_retry_seconds = max_retry_seconds
if session:
self.confluence = Confluence(url=url, session=session, **confluence_kwargs)
elif oauth2:
self.confluence = Confluence(
url=url, oauth2=oauth2, cloud=cloud, **confluence_kwargs
)
elif token:
self.confluence = Confluence(
url=url, token=token, cloud=cloud, **confluence_kwargs
)
else:
self.confluence = Confluence(
url=url,
username=username,
password=api_key,
cloud=cloud,
**confluence_kwargs,
)
| |
159493
|
from __future__ import annotations
from pathlib import Path
from typing import (
TYPE_CHECKING,
Any,
Iterator,
List,
Literal,
Optional,
Sequence,
Union,
)
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseBlobParser, BaseLoader
from langchain_community.document_loaders.blob_loaders import (
BlobLoader,
FileSystemBlobLoader,
)
from langchain_community.document_loaders.parsers.registry import get_parser
if TYPE_CHECKING:
from langchain_text_splitters import TextSplitter
_PathLike = Union[str, Path]
DEFAULT = Literal["default"]
class GenericLoader(BaseLoader):
"""Generic Document Loader.
A generic document loader that allows combining an arbitrary blob loader with
a blob parser.
Examples:
Parse a specific PDF file:
.. code-block:: python
from langchain_community.document_loaders import GenericLoader
from langchain_community.document_loaders.parsers.pdf import PyPDFParser
# Recursively load all text files in a directory.
loader = GenericLoader.from_filesystem(
"my_lovely_pdf.pdf",
parser=PyPDFParser()
)
.. code-block:: python
from langchain_community.document_loaders import GenericLoader
from langchain_community.document_loaders.blob_loaders import FileSystemBlobLoader
loader = GenericLoader.from_filesystem(
path="path/to/directory",
glob="**/[!.]*",
suffixes=[".pdf"],
show_progress=True,
)
docs = loader.lazy_load()
next(docs)
Example instantiations to change which files are loaded:
.. code-block:: python
# Recursively load all text files in a directory.
loader = GenericLoader.from_filesystem("/path/to/dir", glob="**/*.txt")
# Recursively load all non-hidden files in a directory.
loader = GenericLoader.from_filesystem("/path/to/dir", glob="**/[!.]*")
# Load all files in a directory without recursion.
loader = GenericLoader.from_filesystem("/path/to/dir", glob="*")
Example instantiations to change which parser is used:
.. code-block:: python
from langchain_community.document_loaders.parsers.pdf import PyPDFParser
# Recursively load all text files in a directory.
loader = GenericLoader.from_filesystem(
"/path/to/dir",
glob="**/*.pdf",
parser=PyPDFParser()
)
""" # noqa: E501
def __init__(
self,
blob_loader: BlobLoader, # type: ignore[valid-type]
blob_parser: BaseBlobParser,
) -> None:
"""A generic document loader.
Args:
blob_loader: A blob loader which knows how to yield blobs
blob_parser: A blob parser which knows how to parse blobs into documents
"""
self.blob_loader = blob_loader
self.blob_parser = blob_parser
def lazy_load(
self,
) -> Iterator[Document]:
"""Load documents lazily. Use this when working at a large scale."""
for blob in self.blob_loader.yield_blobs(): # type: ignore[attr-defined]
yield from self.blob_parser.lazy_parse(blob)
def load_and_split(
self, text_splitter: Optional[TextSplitter] = None
) -> List[Document]:
"""Load all documents and split them into sentences."""
raise NotImplementedError(
"Loading and splitting is not yet implemented for generic loaders. "
"When they will be implemented they will be added via the initializer. "
"This method should not be used going forward."
)
@classmethod
def from_filesystem(
cls,
path: _PathLike,
*,
glob: str = "**/[!.]*",
exclude: Sequence[str] = (),
suffixes: Optional[Sequence[str]] = None,
show_progress: bool = False,
parser: Union[DEFAULT, BaseBlobParser] = "default",
parser_kwargs: Optional[dict] = None,
) -> GenericLoader:
"""Create a generic document loader using a filesystem blob loader.
Args:
path: The path to the directory to load documents from OR the path to a
single file to load. If this is a file, glob, exclude, suffixes
will be ignored.
glob: The glob pattern to use to find documents.
suffixes: The suffixes to use to filter documents. If None, all files
matching the glob will be loaded.
exclude: A list of patterns to exclude from the loader.
show_progress: Whether to show a progress bar or not (requires tqdm).
Proxies to the file system loader.
parser: A blob parser which knows how to parse blobs into documents,
will instantiate a default parser if not provided.
The default can be overridden by either passing a parser or
setting the class attribute `blob_parser` (the latter
should be used with inheritance).
parser_kwargs: Keyword arguments to pass to the parser.
Returns:
A generic document loader.
"""
blob_loader = FileSystemBlobLoader( # type: ignore[attr-defined, misc]
path,
glob=glob,
exclude=exclude,
suffixes=suffixes,
show_progress=show_progress,
)
if isinstance(parser, str):
if parser == "default":
try:
# If there is an implementation of get_parser on the class, use it.
blob_parser = cls.get_parser(**(parser_kwargs or {}))
except NotImplementedError:
# if not then use the global registry.
blob_parser = get_parser(parser)
else:
blob_parser = get_parser(parser)
else:
blob_parser = parser
return cls(blob_loader, blob_parser)
@staticmethod
def get_parser(**kwargs: Any) -> BaseBlobParser:
"""Override this method to associate a default parser with the class."""
raise NotImplementedError()
| |
159501
|
"""Loader that uses unstructured to load files."""
from __future__ import annotations
import logging
import os
from abc import ABC, abstractmethod
from pathlib import Path
from typing import IO, Any, Callable, Iterator, List, Optional, Sequence, Union
from langchain_core._api.deprecation import deprecated
from langchain_core.documents import Document
from typing_extensions import TypeAlias
from langchain_community.document_loaders.base import BaseLoader
Element: TypeAlias = Any
logger = logging.getLogger(__file__)
def satisfies_min_unstructured_version(min_version: str) -> bool:
"""Check if the installed `Unstructured` version exceeds the minimum version
for the feature in question."""
from unstructured.__version__ import __version__ as __unstructured_version__
min_version_tuple = tuple([int(x) for x in min_version.split(".")])
# NOTE(MthwRobinson) - enables the loader to work when you're using pre-release
# versions of unstructured like 0.4.17-dev1
_unstructured_version = __unstructured_version__.split("-")[0]
unstructured_version_tuple = tuple(
[int(x) for x in _unstructured_version.split(".")]
)
return unstructured_version_tuple >= min_version_tuple
def validate_unstructured_version(min_unstructured_version: str) -> None:
"""Raise an error if the `Unstructured` version does not exceed the
specified minimum."""
if not satisfies_min_unstructured_version(min_unstructured_version):
raise ValueError(
f"unstructured>={min_unstructured_version} is required in this loader."
)
class UnstructuredBaseLoader(BaseLoader, ABC):
"""Base Loader that uses `Unstructured`."""
def __init__(
self,
mode: str = "single", # deprecated
post_processors: Optional[List[Callable[[str], str]]] = None,
**unstructured_kwargs: Any,
):
"""Initialize with file path."""
try:
import unstructured # noqa:F401
except ImportError:
raise ImportError(
"unstructured package not found, please install it with "
"`pip install unstructured`"
)
# `single` - elements are combined into one (default)
# `elements` - maintain individual elements
# `paged` - elements are combined by page
_valid_modes = {"single", "elements", "paged"}
if mode not in _valid_modes:
raise ValueError(
f"Got {mode} for `mode`, but should be one of `{_valid_modes}`"
)
if not satisfies_min_unstructured_version("0.5.4"):
if "strategy" in unstructured_kwargs:
unstructured_kwargs.pop("strategy")
self._check_if_both_mode_and_chunking_strategy_are_by_page(
mode, unstructured_kwargs
)
self.mode = mode
self.unstructured_kwargs = unstructured_kwargs
self.post_processors = post_processors or []
@abstractmethod
def _get_elements(self) -> List[Element]:
"""Get elements."""
@abstractmethod
def _get_metadata(self) -> dict[str, Any]:
"""Get file_path metadata if available."""
def _post_process_elements(self, elements: List[Element]) -> List[Element]:
"""Apply post processing functions to extracted unstructured elements.
Post processing functions are str -> str callables passed
in using the post_processors kwarg when the loader is instantiated.
"""
for element in elements:
for post_processor in self.post_processors:
element.apply(post_processor)
return elements
def lazy_load(self) -> Iterator[Document]:
"""Load file."""
elements = self._get_elements()
self._post_process_elements(elements)
if self.mode == "elements":
for element in elements:
metadata = self._get_metadata()
# NOTE(MthwRobinson) - the attribute check is for backward compatibility
# with unstructured<0.4.9. The metadata attributed was added in 0.4.9.
if hasattr(element, "metadata"):
metadata.update(element.metadata.to_dict())
if hasattr(element, "category"):
metadata["category"] = element.category
if element.to_dict().get("element_id"):
metadata["element_id"] = element.to_dict().get("element_id")
yield Document(page_content=str(element), metadata=metadata)
elif self.mode == "paged":
logger.warning(
"`mode='paged'` is deprecated in favor of the 'by_page' chunking"
" strategy. Learn more about chunking here:"
" https://docs.unstructured.io/open-source/core-functionality/chunking"
)
text_dict: dict[int, str] = {}
meta_dict: dict[int, dict[str, Any]] = {}
for element in elements:
metadata = self._get_metadata()
if hasattr(element, "metadata"):
metadata.update(element.metadata.to_dict())
page_number = metadata.get("page_number", 1)
# Check if this page_number already exists in text_dict
if page_number not in text_dict:
# If not, create new entry with initial text and metadata
text_dict[page_number] = str(element) + "\n\n"
meta_dict[page_number] = metadata
else:
# If exists, append to text and update the metadata
text_dict[page_number] += str(element) + "\n\n"
meta_dict[page_number].update(metadata)
# Convert the dict to a list of Document objects
for key in text_dict.keys():
yield Document(page_content=text_dict[key], metadata=meta_dict[key])
elif self.mode == "single":
metadata = self._get_metadata()
text = "\n\n".join([str(el) for el in elements])
yield Document(page_content=text, metadata=metadata)
else:
raise ValueError(f"mode of {self.mode} not supported.")
def _check_if_both_mode_and_chunking_strategy_are_by_page(
self, mode: str, unstructured_kwargs: dict[str, Any]
) -> None:
if (
mode == "paged"
and unstructured_kwargs.get("chunking_strategy") == "by_page"
):
raise ValueError(
"Only one of `chunking_strategy='by_page'` or `mode='paged'` may be"
" set. `chunking_strategy` is preferred."
)
@deprecated(
since="0.2.8",
removal="1.0",
alternative_import="langchain_unstructured.UnstructuredLoader",
)
class UnstructuredFileLoader(UnstructuredBaseLoader):
"""Load files using `Unstructured`.
The file loader uses the unstructured partition function and will automatically
detect the file type. You can run the loader in different modes: "single",
"elements", and "paged". The default "single" mode will return a single langchain
Document object. If you use "elements" mode, the unstructured library will split
the document into elements such as Title and NarrativeText and return those as
individual langchain Document objects. In addition to these post-processing modes
(which are specific to the LangChain Loaders), Unstructured has its own "chunking"
parameters for post-processing elements into more useful chunks for uses cases such
as Retrieval Augmented Generation (RAG). You can pass in additional unstructured
kwargs to configure different unstructured settings.
Examples
--------
from langchain_community.document_loaders import UnstructuredFileLoader
loader = UnstructuredFileLoader(
"example.pdf", mode="elements", strategy="fast",
)
docs = loader.load()
References
----------
https://docs.unstructured.io/open-source/core-functionality/partitioning
https://docs.unstructured.io/open-source/core-functionality/chunking
"""
def __init__(
self,
file_path: Union[str, List[str], Path, List[Path]],
*,
mode: str = "single",
**unstructured_kwargs: Any,
):
"""Initialize with file path."""
self.file_path = file_path
super().__init__(mode=mode, **unstructured_kwargs)
def _get_elements(self) -> List[Element]:
from unstructured.partition.auto import partition
if isinstance(self.file_path, list):
elements: List[Element] = []
for file in self.file_path:
if isinstance(file, Path):
file = str(file)
elements.extend(partition(filename=file, **self.unstructured_kwargs))
return elements
else:
if isinstance(self.file_path, Path):
self.file_path = str(self.file_path)
return partition(filename=self.file_path, **self.unstructured_kwargs)
def _get_metadata(self) -> dict[str, Any]:
return {"source": self.file_path}
| |
159504
|
import json
import logging
import time
from typing import Iterator, List
import requests
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
logger = logging.getLogger(__name__)
class CubeSemanticLoader(BaseLoader):
"""Load `Cube semantic layer` metadata.
Args:
cube_api_url: REST API endpoint.
Use the REST API of your Cube's deployment.
Please find out more information here:
https://cube.dev/docs/http-api/rest#configuration-base-path
cube_api_token: Cube API token.
Authentication tokens are generated based on your Cube's API secret.
Please find out more information here:
https://cube.dev/docs/security#generating-json-web-tokens-jwt
load_dimension_values: Whether to load dimension values for every string
dimension or not.
dimension_values_limit: Maximum number of dimension values to load.
dimension_values_max_retries: Maximum number of retries to load dimension
values.
dimension_values_retry_delay: Delay between retries to load dimension values.
"""
def __init__(
self,
cube_api_url: str,
cube_api_token: str,
load_dimension_values: bool = True,
dimension_values_limit: int = 10_000,
dimension_values_max_retries: int = 10,
dimension_values_retry_delay: int = 3,
):
self.cube_api_url = cube_api_url
self.cube_api_token = cube_api_token
self.load_dimension_values = load_dimension_values
self.dimension_values_limit = dimension_values_limit
self.dimension_values_max_retries = dimension_values_max_retries
self.dimension_values_retry_delay = dimension_values_retry_delay
def _get_dimension_values(self, dimension_name: str) -> List[str]:
"""Makes a call to Cube's REST API load endpoint to retrieve
values for dimensions.
These values can be used to achieve a more accurate filtering.
"""
logger.info("Loading dimension values for: {dimension_name}...")
headers = {
"Content-Type": "application/json",
"Authorization": self.cube_api_token,
}
query = {
"query": {
"dimensions": [dimension_name],
"limit": self.dimension_values_limit,
}
}
retries = 0
while retries < self.dimension_values_max_retries:
response = requests.request(
"POST",
f"{self.cube_api_url}/load",
headers=headers,
data=json.dumps(query),
)
if response.status_code == 200:
response_data = response.json()
if (
"error" in response_data
and response_data["error"] == "Continue wait"
):
logger.info("Retrying...")
retries += 1
time.sleep(self.dimension_values_retry_delay)
continue
else:
dimension_values = [
item[dimension_name] for item in response_data["data"]
]
return dimension_values
else:
logger.error("Request failed with status code:", response.status_code)
break
if retries == self.dimension_values_max_retries:
logger.info("Maximum retries reached.")
return []
def lazy_load(self) -> Iterator[Document]:
"""Makes a call to Cube's REST API metadata endpoint.
Returns:
A list of documents with attributes:
- page_content=column_title + column_description
- metadata
- table_name
- column_name
- column_data_type
- column_member_type
- column_title
- column_description
- column_values
- cube_data_obj_type
"""
headers = {
"Content-Type": "application/json",
"Authorization": self.cube_api_token,
}
logger.info(f"Loading metadata from {self.cube_api_url}...")
response = requests.get(f"{self.cube_api_url}/meta", headers=headers)
response.raise_for_status()
raw_meta_json = response.json()
cube_data_objects = raw_meta_json.get("cubes", [])
logger.info(f"Found {len(cube_data_objects)} cube data objects in metadata.")
if not cube_data_objects:
raise ValueError("No cubes found in metadata.")
for cube_data_obj in cube_data_objects:
cube_data_obj_name = cube_data_obj.get("name")
cube_data_obj_type = cube_data_obj.get("type")
cube_data_obj_is_public = cube_data_obj.get("public")
measures = cube_data_obj.get("measures", [])
dimensions = cube_data_obj.get("dimensions", [])
logger.info(f"Processing {cube_data_obj_name}...")
if not cube_data_obj_is_public:
logger.info(f"Skipping {cube_data_obj_name} because it is not public.")
continue
for item in measures + dimensions:
column_member_type = "measure" if item in measures else "dimension"
dimension_values = []
item_name = str(item.get("name"))
item_type = str(item.get("type"))
if (
self.load_dimension_values
and column_member_type == "dimension"
and item_type == "string"
):
dimension_values = self._get_dimension_values(item_name)
metadata = dict(
table_name=str(cube_data_obj_name),
column_name=item_name,
column_data_type=item_type,
column_title=str(item.get("title")),
column_description=str(item.get("description")),
column_member_type=column_member_type,
column_values=dimension_values,
cube_data_obj_type=cube_data_obj_type,
)
page_content = f"{str(item.get('title'))}, "
page_content += f"{str(item.get('description'))}"
yield Document(page_content=page_content, metadata=metadata)
| |
159511
|
from langchain_community.document_loaders.notiondb import (
NotionDBLoader,
)
from langchain_community.document_loaders.obs_directory import (
OBSDirectoryLoader,
)
from langchain_community.document_loaders.obs_file import (
OBSFileLoader,
)
from langchain_community.document_loaders.obsidian import (
ObsidianLoader,
)
from langchain_community.document_loaders.odt import (
UnstructuredODTLoader,
)
from langchain_community.document_loaders.onedrive import (
OneDriveLoader,
)
from langchain_community.document_loaders.onedrive_file import (
OneDriveFileLoader,
)
from langchain_community.document_loaders.open_city_data import (
OpenCityDataLoader,
)
from langchain_community.document_loaders.oracleadb_loader import (
OracleAutonomousDatabaseLoader,
)
from langchain_community.document_loaders.oracleai import (
OracleDocLoader,
OracleTextSplitter,
)
from langchain_community.document_loaders.org_mode import (
UnstructuredOrgModeLoader,
)
from langchain_community.document_loaders.pdf import (
AmazonTextractPDFLoader,
DedocPDFLoader,
MathpixPDFLoader,
OnlinePDFLoader,
PagedPDFSplitter,
PDFMinerLoader,
PDFMinerPDFasHTMLLoader,
PDFPlumberLoader,
PyMuPDFLoader,
PyPDFDirectoryLoader,
PyPDFium2Loader,
PyPDFLoader,
UnstructuredPDFLoader,
)
from langchain_community.document_loaders.pebblo import (
PebbloSafeLoader,
PebbloTextLoader,
)
from langchain_community.document_loaders.polars_dataframe import (
PolarsDataFrameLoader,
)
from langchain_community.document_loaders.powerpoint import (
UnstructuredPowerPointLoader,
)
from langchain_community.document_loaders.psychic import (
PsychicLoader,
)
from langchain_community.document_loaders.pubmed import (
PubMedLoader,
)
from langchain_community.document_loaders.pyspark_dataframe import (
PySparkDataFrameLoader,
)
from langchain_community.document_loaders.python import (
PythonLoader,
)
from langchain_community.document_loaders.readthedocs import (
ReadTheDocsLoader,
)
from langchain_community.document_loaders.recursive_url_loader import (
RecursiveUrlLoader,
)
from langchain_community.document_loaders.reddit import (
RedditPostsLoader,
)
from langchain_community.document_loaders.roam import (
RoamLoader,
)
from langchain_community.document_loaders.rocksetdb import (
RocksetLoader,
)
from langchain_community.document_loaders.rss import (
RSSFeedLoader,
)
from langchain_community.document_loaders.rst import (
UnstructuredRSTLoader,
)
from langchain_community.document_loaders.rtf import (
UnstructuredRTFLoader,
)
from langchain_community.document_loaders.s3_directory import (
S3DirectoryLoader,
)
from langchain_community.document_loaders.s3_file import (
S3FileLoader,
)
from langchain_community.document_loaders.scrapfly import (
ScrapflyLoader,
)
from langchain_community.document_loaders.scrapingant import (
ScrapingAntLoader,
)
from langchain_community.document_loaders.sharepoint import (
SharePointLoader,
)
from langchain_community.document_loaders.sitemap import (
SitemapLoader,
)
from langchain_community.document_loaders.slack_directory import (
SlackDirectoryLoader,
)
from langchain_community.document_loaders.snowflake_loader import (
SnowflakeLoader,
)
from langchain_community.document_loaders.spider import (
SpiderLoader,
)
from langchain_community.document_loaders.spreedly import (
SpreedlyLoader,
)
from langchain_community.document_loaders.sql_database import (
SQLDatabaseLoader,
)
from langchain_community.document_loaders.srt import (
SRTLoader,
)
from langchain_community.document_loaders.stripe import (
StripeLoader,
)
from langchain_community.document_loaders.surrealdb import (
SurrealDBLoader,
)
from langchain_community.document_loaders.telegram import (
TelegramChatApiLoader,
TelegramChatFileLoader,
TelegramChatLoader,
)
from langchain_community.document_loaders.tencent_cos_directory import (
TencentCOSDirectoryLoader,
)
from langchain_community.document_loaders.tencent_cos_file import (
TencentCOSFileLoader,
)
from langchain_community.document_loaders.tensorflow_datasets import (
TensorflowDatasetLoader,
)
from langchain_community.document_loaders.text import (
TextLoader,
)
from langchain_community.document_loaders.tidb import (
TiDBLoader,
)
from langchain_community.document_loaders.tomarkdown import (
ToMarkdownLoader,
)
from langchain_community.document_loaders.toml import (
TomlLoader,
)
from langchain_community.document_loaders.trello import (
TrelloLoader,
)
from langchain_community.document_loaders.tsv import (
UnstructuredTSVLoader,
)
from langchain_community.document_loaders.twitter import (
TwitterTweetLoader,
)
from langchain_community.document_loaders.unstructured import (
UnstructuredAPIFileIOLoader,
UnstructuredAPIFileLoader,
UnstructuredFileIOLoader,
UnstructuredFileLoader,
)
from langchain_community.document_loaders.url import (
UnstructuredURLLoader,
)
from langchain_community.document_loaders.url_playwright import (
PlaywrightURLLoader,
)
from langchain_community.document_loaders.url_selenium import (
SeleniumURLLoader,
)
from langchain_community.document_loaders.vsdx import (
VsdxLoader,
)
from langchain_community.document_loaders.weather import (
WeatherDataLoader,
)
from langchain_community.document_loaders.web_base import (
WebBaseLoader,
)
from langchain_community.document_loaders.whatsapp_chat import (
WhatsAppChatLoader,
)
from langchain_community.document_loaders.wikipedia import (
WikipediaLoader,
)
from langchain_community.document_loaders.word_document import (
Docx2txtLoader,
UnstructuredWordDocumentLoader,
)
from langchain_community.document_loaders.xml import (
UnstructuredXMLLoader,
)
from langchain_community.document_loaders.xorbits import (
XorbitsLoader,
)
from langchain_community.document_loaders.youtube import (
GoogleApiClient,
GoogleApiYoutubeLoader,
YoutubeLoader,
)
from langchain_community.document_loaders.yuque import (
YuqueLoader,
)
_module_lookup =
| |
159518
|
import os
import tempfile
from typing import List
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
from langchain_community.document_loaders.unstructured import UnstructuredFileLoader
class AzureBlobStorageFileLoader(BaseLoader):
"""Load from `Azure Blob Storage` files."""
def __init__(self, conn_str: str, container: str, blob_name: str):
"""Initialize with connection string, container and blob name."""
self.conn_str = conn_str
"""Connection string for Azure Blob Storage."""
self.container = container
"""Container name."""
self.blob = blob_name
"""Blob name."""
def load(self) -> List[Document]:
"""Load documents."""
try:
from azure.storage.blob import BlobClient
except ImportError as exc:
raise ImportError(
"Could not import azure storage blob python package. "
"Please install it with `pip install azure-storage-blob`."
) from exc
client = BlobClient.from_connection_string(
conn_str=self.conn_str, container_name=self.container, blob_name=self.blob
)
with tempfile.TemporaryDirectory() as temp_dir:
file_path = f"{temp_dir}/{self.container}/{self.blob}"
os.makedirs(os.path.dirname(file_path), exist_ok=True)
with open(f"{file_path}", "wb") as file:
blob_data = client.download_blob()
blob_data.readinto(file)
loader = UnstructuredFileLoader(file_path)
return loader.load()
|
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