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"""Map-reduce chain. |
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Splits up a document, sends the smaller parts to the LLM with one prompt, |
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then combines the results with another one. |
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""" |
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from __future__ import annotations |
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from typing import Any, Dict, List, Mapping, Optional |
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from langchain_core._api import deprecated |
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from langchain_core.callbacks import CallbackManagerForChainRun, Callbacks |
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from langchain_core.documents import Document |
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from langchain_core.language_models import BaseLanguageModel |
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from langchain_core.prompts import BasePromptTemplate |
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from langchain_text_splitters import TextSplitter |
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from pydantic import ConfigDict |
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from langchain.chains import ReduceDocumentsChain |
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from langchain.chains.base import Chain |
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from langchain.chains.combine_documents.base import BaseCombineDocumentsChain |
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from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain |
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from langchain.chains.combine_documents.stuff import StuffDocumentsChain |
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from langchain.chains.llm import LLMChain |
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@deprecated( |
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since="0.2.13", |
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removal="1.0", |
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message=( |
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"Refer to migration guide here for a recommended implementation using " |
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"LangGraph: https://python.langchain.com/docs/versions/migrating_chains/map_reduce_chain/" |
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". See also LangGraph guides for map-reduce: " |
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"https://langchain-ai.github.io/langgraph/how-tos/map-reduce/." |
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), |
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) |
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class MapReduceChain(Chain): |
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"""Map-reduce chain.""" |
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combine_documents_chain: BaseCombineDocumentsChain |
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"""Chain to use to combine documents.""" |
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text_splitter: TextSplitter |
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"""Text splitter to use.""" |
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input_key: str = "input_text" |
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output_key: str = "output_text" |
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@classmethod |
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def from_params( |
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cls, |
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llm: BaseLanguageModel, |
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prompt: BasePromptTemplate, |
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text_splitter: TextSplitter, |
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callbacks: Callbacks = None, |
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combine_chain_kwargs: Optional[Mapping[str, Any]] = None, |
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reduce_chain_kwargs: Optional[Mapping[str, Any]] = None, |
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**kwargs: Any, |
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) -> MapReduceChain: |
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"""Construct a map-reduce chain that uses the chain for map and reduce.""" |
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llm_chain = LLMChain(llm=llm, prompt=prompt, callbacks=callbacks) |
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stuff_chain = StuffDocumentsChain( |
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llm_chain=llm_chain, |
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callbacks=callbacks, |
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**(reduce_chain_kwargs if reduce_chain_kwargs else {}), |
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) |
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reduce_documents_chain = ReduceDocumentsChain( |
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combine_documents_chain=stuff_chain |
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) |
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combine_documents_chain = MapReduceDocumentsChain( |
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llm_chain=llm_chain, |
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reduce_documents_chain=reduce_documents_chain, |
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callbacks=callbacks, |
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**(combine_chain_kwargs if combine_chain_kwargs else {}), |
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) |
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return cls( |
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combine_documents_chain=combine_documents_chain, |
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text_splitter=text_splitter, |
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callbacks=callbacks, |
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**kwargs, |
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) |
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model_config = ConfigDict( |
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arbitrary_types_allowed=True, |
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extra="forbid", |
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) |
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@property |
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def input_keys(self) -> List[str]: |
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"""Expect input key. |
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:meta private: |
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""" |
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return [self.input_key] |
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@property |
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def output_keys(self) -> List[str]: |
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"""Return output key. |
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:meta private: |
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""" |
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return [self.output_key] |
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def _call( |
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self, |
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inputs: Dict[str, str], |
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run_manager: Optional[CallbackManagerForChainRun] = None, |
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) -> Dict[str, str]: |
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_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() |
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doc_text = inputs.pop(self.input_key) |
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texts = self.text_splitter.split_text(doc_text) |
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docs = [Document(page_content=text) for text in texts] |
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_inputs: Dict[str, Any] = { |
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**inputs, |
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self.combine_documents_chain.input_key: docs, |
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} |
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outputs = self.combine_documents_chain.run( |
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_inputs, callbacks=_run_manager.get_child() |
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) |
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return {self.output_key: outputs} |
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