Update agent.py

agent.py

@@ -1,183 +1,65 @@
-"""LangGraph Agent"""
 import os
 from dotenv import load_dotenv
-from langgraph.graph import START, StateGraph, MessagesState
-from langchain_openai import ChatOpenAI  # <-- ADD THIS IMPORT
+from langchain.graph import START, StateGraph, MessagesState
+from langchain_openai import ChatOpenAI
 from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_community.tools.tavily_search import TavilySearchResults
-from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
 from langchain_community.vectorstores import SupabaseVectorStore
 from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
 from langchain_core.tools import tool
-from supabase.client import Client, create_client
 
 load_dotenv()
 
-@tool
-def multiply(a: int, b: int) -> int:
-    """Multiply two numbers.
-    Args:
-        a: first int
-        b: second int
-    """
-    return a * b
+# ... [Your tool definitions here] ...
 
-@tool
-def add(a: int, b: int) -> int:
-    """Add two numbers.
-    Args:
-        a: first int
-        b: second int
-    """
-    return a + b
+tools = [multiply, add, subtract, divide, modulus, wiki_search, web_search, arvix_search]
 
-@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) -> float:
-    """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}
-
-tools = [
-    multiply,
-    add,
-    subtract,
-    divide,
-    modulus,
-    wiki_search,
-    web_search,
-    arvix_search,
-]
-
-# load the system prompt from the file
 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(
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+from supabase.client import create_client
+supabase = create_client(
     os.environ.get("SUPABASE_URL"),
     os.environ.get("SUPABASE_SERVICE_KEY"))
 vector_store = SupabaseVectorStore(
     client=supabase,
-    embedding= embeddings,
+    embedding=embeddings,
     table_name="documents",
-    query_name="match_documents_langchain",
+    query_name="match_documents_langchain"
 )
 
 def build_graph(provider: str = "openai"):
-    """Build the graph with OpenAI, Gemini, or HuggingFace backend."""
     if provider == "openai":
         llm = ChatOpenAI(
-            model="gpt-3.5-turbo",  # or "gpt-4o" if available and you want to use it
+            model="gpt-3.5-turbo",  # or "gpt-4o"
             temperature=0,
             openai_api_key=os.environ.get("OPENAI_API_KEY"),
         )
-    elif provider == "google":
-        from langchain_google_genai import ChatGoogleGenerativeAI
-        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
-    elif provider == "huggingface":
-        from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
-        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 'openai', 'google', or 'huggingface'.")
+        raise ValueError("Invalid provider.")
 
     llm_with_tools = llm.bind_tools(tools)
 
-    from langchain_core.messages import AIMessage
-
     def retriever(state: MessagesState):
-            query = state["messages"][-1].content
-            results = vector_store.similarity_search(query, k=1)
-            if not results:
-                return {"messages": [AIMessage(content="FINAL ANSWER: No relevant answer found.")]}
-            similar_doc = results[0]
-            content = similar_doc.page_content
-            if "FINAL ANSWER:" in content:
-                answer = content.split("FINAL ANSWER:")[-1].strip()
-                return {"messages": [AIMessage(content=f"FINAL ANSWER: {answer}")]}
-            else:
-                return {"messages": [AIMessage(content=content.strip())]}
-        
-        
-            builder = StateGraph(MessagesState)
-            builder.add_node("retriever", retriever)
-            builder.add_node("assistant", assistant)
-            builder.add_edge(START, "retriever")
-            builder.add_edge("retriever", "assistant")
-            builder.set_finish_point("assistant")
-        
-            return builder.compile()
-
+        query = state["messages"][-1].content
+        results = vector_store.similarity_search(query, k=1)
+        if not results:
+            return {"messages": [AIMessage(content="FINAL ANSWER: No relevant answer found.")]}
+        similar_doc = results[0]
+        content = similar_doc.page_content
+        if "FINAL ANSWER:" in content:
+            answer = content.split("FINAL ANSWER:")[-1].strip()
+            return {"messages": [AIMessage(content=f"FINAL ANSWER: {answer}")]}
+        else:
+            return {"messages": [AIMessage(content=content.strip())]}
+
+    def assistant(state: MessagesState):
+        user_message = state["messages"][-1]
+        # Make sure you send both system and user message
+        result = llm_with_tools.invoke([sys_msg, user_message])
+        return {"messages": [result]}
 
     builder = StateGraph(MessagesState)
     builder.add_node("retriever", retriever)