"""LangGraph Agent""" import os from dotenv import load_dotenv from langgraph.graph import START, StateGraph, MessagesState from langgraph.prebuilt import tools_condition from langgraph.prebuilt import ToolNode from langchain_google_genai import ChatGoogleGenerativeAI from langchain_groq import ChatGroq from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings from langchain_community.tools.tavily_search import TavilySearchResults from langchain_community.document_loaders import WikipediaLoader from langchain_community.document_loaders import ArxivLoader from langchain_community.vectorstores import SupabaseVectorStore from langchain_core.messages import SystemMessage, HumanMessage from langchain_core.tools import tool from langchain.tools.retriever import create_retriever_tool from supabase.client import Client, create_client load_dotenv() print("GROQ_API_KEY:", os.getenv("GROQ_API_KEY")) print("SUPABASE_URL:", os.getenv("SUPABASE_URL")) # === 原有的数学工具 === @tool def multiply(a: int, b: int) -> int: """Multiply two numbers. Args: a: first int b: second int """ return a * b @tool def add(a: int, b: int) -> int: """Add two numbers. Args: a: first int b: second int """ return a + b @tool def subtract(a: int, b: int) -> int: """Subtract two numbers. Args: a: first int b: second int """ return a - b @tool def divide(a: int, b: int) -> int: """Divide two numbers. Args: a: first int b: second int """ if b == 0: raise ValueError("Cannot divide by zero.") return a / b @tool def modulus(a: int, b: int) -> int: """Get the modulus of two numbers. Args: a: first int b: second int """ return a % b # === 原有的搜索工具 === @tool def wiki_search(query: str) -> str: """Search Wikipedia for a query and return maximum 2 results. Args: query: The search query.""" search_docs = WikipediaLoader(query=query, load_max_docs=2).load() formatted_search_docs = "\n\n---\n\n".join( [ f'\n{doc.page_content}\n' for doc in search_docs ]) return {"wiki_results": formatted_search_docs} @tool def web_search(query: str) -> str: """Search Tavily for a query and return maximum 3 results. Args: query: The search query.""" search_docs = TavilySearchResults(max_results=3).invoke(query=query) formatted_search_docs = "\n\n---\n\n".join( [ f'\n{doc.page_content}\n' for doc in search_docs ]) return {"web_results": formatted_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'\n{doc.page_content[:1000]}\n' for doc in search_docs ]) return {"arvix_results": formatted_search_docs} # === 新增：Supabase 工具 === @tool def supabase_vector_search(query: str, max_results: int = 3) -> str: """Search the Supabase knowledge base using vector similarity. Args: query: The search query max_results: Maximum number of results to return (default: 3) """ try: embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") supabase: Client = create_client( os.environ.get("SUPABASE_URL"), os.environ.get("SUPABASE_SERVICE_KEY") ) vector_store = SupabaseVectorStore( client=supabase, embedding=embeddings, table_name="supabase_docs", # 使用您的实际表名 query_name="match_documents", # 使用我们创建的函数 ) results = vector_store.similarity_search(query, k=max_results) if results: formatted_results = "\n\n---\n\n".join([ f'\n{doc.page_content[:800]}...\n' for doc in results ]) return {"supabase_vector_results": formatted_results} else: return {"message": "No relevant documents found in knowledge base"} except Exception as e: return {"error": f"Supabase vector search failed: {str(e)}"} @tool def supabase_text_search(query: str, max_results: int = 3) -> str: """Search the Supabase knowledge base using text search. Args: query: The search query max_results: Maximum number of results to return (default: 3) """ try: supabase: Client = create_client( os.environ.get("SUPABASE_URL"), os.environ.get("SUPABASE_SERVICE_KEY") ) # 使用我们创建的混合搜索函数，只用文本搜索 result = supabase.rpc('hybrid_search', { 'search_query': query, 'search_type': 'text', 'max_results': max_results }).execute() if result.data: formatted_results = "\n\n---\n\n".join([ f'\n{item["content"][:800]}...\n' for item in result.data ]) return {"supabase_text_results": formatted_results} else: return {"message": "No relevant documents found in knowledge base"} except Exception as e: return {"error": f"Supabase text search failed: {str(e)}"} @tool def get_knowledge_context(query: str) -> str: """Get contextual information from the knowledge base for better understanding. Args: query: The user's question """ try: supabase: Client = create_client( os.environ.get("SUPABASE_URL"), os.environ.get("SUPABASE_SERVICE_KEY") ) result = supabase.rpc('get_agent_context', { 'user_query': query, 'context_limit': 2 }).execute() if result.data and len(result.data) > 0: context_data = result.data[0] context_text = context_data.get("context_text", "") confidence = context_data.get("confidence_score", 0) source_count = context_data.get("source_count", 0) if context_text and source_count > 0: return { "context": context_text[:1000], # 限制长度 "confidence": f"{confidence:.2f}", "sources": source_count } else: return {"message": "No relevant context found"} else: return {"message": "No context available"} except Exception as e: return {"error": f"Context retrieval failed: {str(e)}"} # load the system prompt from the file try: with open("system_prompt.txt", "r", encoding="utf-8") as f: system_prompt = f.read() except FileNotFoundError: # 如果文件不存在，使用默认系统提示 system_prompt = """你是一个智能助手，可以使用多种工具来回答用户的问题。 可用工具包括： 1. 数学计算工具（加减乘除等） 2. 网络搜索工具（Wikipedia, Arxiv, Web搜索） 3. Supabase 知识库工具（向量搜索、文本搜索、上下文获取） 请根据用户的问题选择最合适的工具，并提供准确、有用的答案。对于知识库中的信息，优先使用 Supabase 工具。""" # System message sys_msg = SystemMessage(content=system_prompt) # === 更新 retriever 设置 === def setup_vector_store(): """设置向量存储""" try: embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") supabase: Client = create_client( os.environ.get("SUPABASE_URL"), os.environ.get("SUPABASE_SERVICE_KEY") ) vector_store = SupabaseVectorStore( client=supabase, embedding=embeddings, table_name="supabase_docs", # 修改为正确的表名 query_name="match_documents", # 使用我们创建的函数 ) retriever_tool = create_retriever_tool( retriever=vector_store.as_retriever(search_kwargs={"k": 3}), name="Knowledge Base Search", description="Search the knowledge base for similar questions and answers.", ) return vector_store, retriever_tool except Exception as e: print(f"❌ Vector store setup failed: {e}") return None, None # 设置向量存储 vector_store, retriever_tool = setup_vector_store() # === 更新工具列表 === tools = [ multiply, add, subtract, divide, modulus, wiki_search, web_search, arvix_search, supabase_vector_search, # 新增 supabase_text_search, # 新增 get_knowledge_context, # 新增 ] # 如果 retriever 设置成功，添加到工具列表 if retriever_tool: tools.append(retriever_tool) print("✅ Knowledge base retriever tool added") else: print("⚠️ Knowledge base retriever tool not available") # Build graph function def build_graph(provider: str = "groq"): """Build the graph""" if provider == "google": llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0) elif provider == "groq": llm = ChatGroq(model="qwen-qwq-32b", temperature=0) elif provider == "huggingface": llm = ChatHuggingFace( llm=HuggingFaceEndpoint( url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf", temperature=0, ), ) else: raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.") # Bind tools to LLM llm_with_tools = llm.bind_tools(tools) # Node def assistant(state: MessagesState): """Assistant node""" return {"messages": [llm_with_tools.invoke(state["messages"])]} def retriever(state: MessagesState): """Enhanced retriever node with Supabase integration""" try: if vector_store and len(state["messages"]) > 0: user_query = state["messages"][-1].content similar_questions = vector_store.similarity_search(user_query, k=2) if similar_questions: example_content = "\n\n".join([ f"Similar Q&A {i+1}: {doc.page_content[:400]}..." for i, doc in enumerate(similar_questions) ]) example_msg = HumanMessage( content=f"Here are similar questions and answers from the knowledge base for reference:\n\n{example_content}", ) return {"messages": [sys_msg] + state["messages"] + [example_msg]} # 如果没有向量存储或搜索失败，返回原始消息 return {"messages": [sys_msg] + state["messages"]} except Exception as e: print(f"Retriever error: {e}") 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") # Compile graph return builder.compile() # test if __name__ == "__main__": # 测试多种类型的问题 test_questions = [ "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?", "What is the area of the green polygon?", # 测试知识库搜索 "Calculate 25 times 17", # 测试数学工具 ] print("🚀 开始测试 Agent...") # Build the graph graph = build_graph(provider="groq") for i, question in enumerate(test_questions, 1): print(f"\n{'='*60}") print(f"测试 {i}/3: {question}") print(f"{'='*60}") try: messages = [HumanMessage(content=question)] result = graph.invoke({"messages": messages}) print("\n📋 对话历史:") for m in result["messages"]: m.pretty_print() except Exception as e: print(f"❌ 处理问题时出错: {e}") print(f"\n{'-'*60}") print("\n🎉 测试完成！")