Update agent.py

agent.py

@@ -16,214 +16,104 @@ from langchain_core.tools import tool
 from langchain.tools.retriever import create_retriever_tool
 from supabase import create_client, Client
 
-
-supabase_url = os.getenv("SUPABASE_URL")
-supabase_key = os.getenv("SUPABASE_KEY")
-
-# Obtener credenciales de entorno (funcionará con ambos métodos)
-supabase_url = os.environ.get('SUPABASE_URL')
-supabase_key = os.environ.get('SUPABASE_KEY')
-
-if not supabase_url or not supabase_key:
-    raise ValueError("""
+# Cargar variables de entorno
+load_dotenv()
+
+# Configuración de Supabase (segura)
+try:
+    supabase_url = os.environ['SUPABASE_URL']
+    supabase_key = os.environ['SUPABASE_KEY']
+    supabase: Client = create_client(supabase_url, supabase_key)
+except KeyError as e:
+    raise ValueError(f"""
     Missing Supabase credentials. Please set:
     1. SUPABASE_URL and SUPABASE_KEY as Secrets in HF Space settings
     OR
-    2. As environment variables if running locally
+    2. As environment variables in a .env file for local development
+    Missing: {e}
     """)
 
-supabase: Client = create_client(supabase_url, supabase_key)
-
-@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
-
-@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'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
-            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'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
-            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'<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}
-
+# Herramientas (tools) permanecen igual...
+# [Mantén todas tus herramientas (@tool) como están]
 
-
-# load the system prompt from the file
+# Cargar prompt del sistema
 with open("system_prompt.txt", "r", encoding="utf-8") as f:
     system_prompt = f.read()
 
-# 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("https://ujbrcpxgwgsxyjqjhtam.supabase.co"), 
-    os.environ.get("eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6InVqYnJjcHhnd2dzeHlqcWpodGFtIiwicm9sZSI6ImFub24iLCJpYXQiOjE3NTA5MjU1NzIsImV4cCI6MjA2NjUwMTU3Mn0.i78C9tbh4jmVbhKfG_Xng1IMzATkf9fs31RtFYs22E8"))
-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.",
-)
-
-
-
-tools = [
-    multiply,
-    add,
-    subtract,
-    divide,
-    modulus,
-    wiki_search,
-    web_search,
-    arvix_search,
-]
+# Configuración del vector store corregida
+try:
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+    vector_store = SupabaseVectorStore(
+        client=supabase,
+        embedding=embeddings,
+        table_name="documents",
+        query_name="match_documents_langchain",
+    )
+    retriever_tool = create_retriever_tool(
+        retriever=vector_store.as_retriever(),
+        name="Question Search",
+        description="Search for similar questions in our knowledge base",
+    )
+    tools.append(retriever_tool)  # Añade esta herramienta a tu lista existente
+except Exception as e:
+    print(f"Warning: Could not initialize vector store: {e}")
+    retriever_tool = None
 
-# Build graph function
+# Función build_graph corregida
 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
-    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",
-                temperature=0,
-            ),
+    """Build the graph with error handling"""
+    try:
+        if provider == "google":
+            llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash", temperature=0)
+        elif provider == "groq":
+            llm = ChatGroq(model="mixtral-8x7b-32768", temperature=0)
+        elif provider == "huggingface":
+            llm = ChatHuggingFace(
+                llm=HuggingFaceEndpoint(
+                    endpoint_url="https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2",
+                    temperature=0,
+                )
+            )
+        else:
+            raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'")
+            
+        llm_with_tools = llm.bind_tools(tools)
+        
+        # Nodos del grafo
+        def assistant(state: MessagesState):
+            return {"messages": [llm_with_tools.invoke(state["messages"])]}
+            
+        def retriever(state: MessagesState):
+            if not retriever_tool:
+                return {"messages": state["messages"]}
+            similar_questions = vector_store.similarity_search(state["messages"][-1].content, k=1)
+            if similar_questions:
+                example_msg = HumanMessage(
+                    content=f"Similar question found:\n\n{similar_questions[0].page_content}"
+                )
+                return {"messages": state["messages"] + [example_msg]}
+            return {"messages": state["messages"]}
+            
+        # Construcción del grafo
+        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,
         )
-    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):
-        """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]}
-
-    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__":
-    question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
-    # 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()
+        builder.add_edge("tools", "assistant")
+        
+        return builder.compile()
+        
+    except Exception as e:
+        print(f"Error building graph: {e}")
+        raise
+
+# [El resto del código permanece igual]