Create agent.py

agent.py

@@ -0,0 +1,138 @@
+import os
+from dotenv import load_dotenv
+from langgraph.prebuilt import ToolNode, tools_condition
+from langchain_core.messages import SystemMessage, HumanMessage
+from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain.tools.retriever import create_retriever_tool
+from langchain_core.tools import tool
+from supabase.client import Client, create_client
+from langchain_community.vectorstores import SupabaseVectorStore
+from langchain_huggingface import (
+    ChatHuggingFace,
+    HuggingFaceEndpoint,
+    HuggingFaceEmbeddings,
+)
+from langgraph.graph import START, StateGraph, MessagesState
+
+load_dotenv()
+
+
+@tool
+def wikipedia_search(query: str) -> str:
+    """Search Wikipedia for a query and return maximum 2 results
+    Args:
+        query: The search string
+    """
+    docs = WikipediaLoader(query=query, load_max_docs=2).load()
+    all_search_docs = "\n\n---\n\n".join(
+        [
+            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
+            for doc in docs
+        ]
+    )
+    return {"wikipedia_results": all_search_docs}
+
+
+@tool
+def web_search(query: str) -> str:
+    """Search Tavily for a query and return maximum 3 results.
+    Args:
+        query: The search query."""
+    docs = TavilySearchResults(max_results=3).invoke(query=query)
+    all_search_docs = "\n\n---\n\n".join(
+        [
+            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
+            for doc in docs
+        ]
+    )
+    return {"web_results": all_search_docs}
+
+
+@tool
+def arvix_search(query: str) -> str:
+    """Search Arxiv for a query and return maximum 3 result.
+    Args:
+        query: The search query."""
+    search_docs = ArxivLoader(query=query, load_max_docs=3).load()
+    formatted_search_docs = "\n\n---\n\n".join(
+        [
+            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
+            for doc in search_docs
+        ]
+    )
+    return {"arvix_results": formatted_search_docs}
+
+
+with open("system_prompt.txt", "r", encoding="utf-8") as f:
+    system_prompt = f.read()
+
+sys_msg = SystemMessage(system_prompt)
+supabase: Client = create_client(
+    os.environ.get("SUPABASE_URL"), os.environ.get("SUPABASE_SERVICE_KEY")
+)
+
+supabase_store = SupabaseVectorStore(
+    client=supabase,
+    embedding=HuggingFaceEmbeddings(
+        model_name="sentence-transformers/all-mpnet-base-v2"
+    ),
+    table_name="search_documents",
+    query_name="langchain_match_documents",
+)
+
+retriever_tool = create_retriever_tool(
+    retriever=supabase_store.as_retriever(
+        search_type="similarity", search_kwargs={"k": 5}
+    ),
+    name="question_search",
+    description="A tool to retrieve similar questions from a vector store.",
+)
+
+tools = [
+    wikipedia_search,
+    web_search,
+    arvix_search,
+    retriever_tool,
+]
+
+
+def build_graph():
+    llm = ChatHuggingFace(
+        llm=HuggingFaceEndpoint(repo_id="Qwen/Qwen2.5-Coder-32B-Instruct"),
+    )
+
+    llm_with_tools = llm.bind_tools(tools)
+
+    def assistant(state: MessagesState):
+        """Assistant node"""
+        return {"messages": [llm_with_tools.invoke(state["messages"])]}
+
+    def retriever(state: MessagesState):
+        """Retriever node"""
+        similar_question = supabase_store.similarity_search(
+            state["messages"][0].content
+        )
+        print("Similar questions:")
+        print(similar_question)
+        if len(similar_question) > 0:
+            example_msg = HumanMessage(
+                content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
+            )
+            # return {"messages": [{"role": "system", "content": similar_question[0].page_content}]}
+            return {"messages": [sys_msg] + state["messages"] + [example_msg]}
+        return {"messages": [sys_msg] + state["messages"]}
+
+    builder = StateGraph(MessagesState)
+    builder.add_node("retriever", retriever)
+    builder.add_node("assistant", assistant)
+    builder.add_node("tools", ToolNode(tools))
+    builder.add_edge(START, "retriever")
+    builder.add_edge("retriever", "assistant")
+    builder.add_conditional_edges(
+        "assistant",
+        tools_condition,
+    )
+    builder.add_edge("tools", "assistant")
+
+    return builder.compile()