Update agent.py

agent.py

@@ -17,7 +17,10 @@ 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.
@@ -70,6 +73,7 @@ def modulus(a: int, b: int) -> 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.
@@ -112,34 +116,165 @@ def arvix_search(query: str) -> str:
         ])
     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'<Document similarity="high"/>\n{doc.page_content[:800]}...\n</Document>'
+                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'<Document similarity="{item.get("similarity", 0):.3f}"/>\n{item["content"][:800]}...\n</Document>'
+                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
-with open("system_prompt.txt", "r", encoding="utf-8") as f:
-    system_prompt = f.read()
+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���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)
 
-# build a retriever
-embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") #  dim=768
-supabase: Client = create_client(
-    os.environ.get("SUPABASE_URL"), 
-    os.environ.get("SUPABASE_SERVICE_KEY"))
-vector_store = SupabaseVectorStore(
-    client=supabase,
-    embedding= embeddings,
-    table_name="documents",
-    query_name="match_documents_langchain",
-)
-create_retriever_tool = create_retriever_tool(
-    retriever=vector_store.as_retriever(),
-    name="Question Search",
-    description="A tool to retrieve similar questions from a vector store.",
-)
-
+# === 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,
@@ -149,20 +284,26 @@ tools = [
     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"""
-    # Load environment variables from .env file
     if provider == "google":
-        # Google Gemini
         llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
     elif provider == "groq":
-        # Groq https://console.groq.com/docs/models
-        llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
+        llm = ChatGroq(model="qwen-qwq-32b", temperature=0)
     elif provider == "huggingface":
-        # TODO: Add huggingface endpoint
         llm = ChatHuggingFace(
             llm=HuggingFaceEndpoint(
                 url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
@@ -171,6 +312,7 @@ def build_graph(provider: str = "groq"):
         )
     else:
         raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
+    
     # Bind tools to LLM
     llm_with_tools = llm.bind_tools(tools)
 
@@ -180,12 +322,28 @@ def build_graph(provider: str = "groq"):
         return {"messages": [llm_with_tools.invoke(state["messages"])]}
     
     def retriever(state: MessagesState):
-        """Retriever node"""
-        similar_question = vector_store.similarity_search(state["messages"][0].content)
-        example_msg = HumanMessage(
-            content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
-        )
-        return {"messages": [sys_msg] + state["messages"] + [example_msg]}
+        """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)
@@ -204,11 +362,34 @@ def build_graph(provider: str = "groq"):
 
 # test
 if __name__ == "__main__":
-    question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
+    # 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")
-    # Run the graph
-    messages = [HumanMessage(content=question)]
-    messages = graph.invoke({"messages": messages})
-    for m in messages["messages"]:
-        m.pretty_print()
+    
+    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！")