Update agent.py

agent.py

@@ -1,120 +1,85 @@
-"""LangGraph Agent"""
+"""LangGraph Agent (No Supabase)"""
 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 langgraph.prebuilt import tools_condition, ToolNode
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_groq import ChatGroq
-from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
 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_community.document_loaders import WikipediaLoader, ArxivLoader
 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()
 
 @tool
 def multiply(a: int, b: int) -> int:
-    """Multiply two integers and return the product."""
+    """Multiply two integers and return the result."""
     return a * b
 
 @tool
 def add(a: int, b: int) -> int:
-    """Add two integers and return the sum."""
+    """Add two integers and return the result."""
     return a + b
 
 @tool
 def subtract(a: int, b: int) -> int:
-    """Subtract second integer from first and return the difference."""
+    """Subtract b from a and return the result."""
     return a - b
 
 @tool
 def divide(a: int, b: int) -> float:
-    """Divide first integer by second and return the quotient. Raises error if divisor is zero."""
+    """Divide a by b and return the result."""
     if b == 0:
         raise ValueError("Cannot divide by zero.")
     return a / b
 
 @tool
 def modulus(a: int, b: int) -> int:
-    """Return the modulus (remainder) of first integer divided by second."""
+    """Return the modulus of a and b."""
     return a % b
 
 @tool
 def wiki_search(query: str) -> dict:
-    """Search Wikipedia for a query and return formatted top 2 results."""
+    """Search Wikipedia for a query and return up to 2 results."""
     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}
+    results = "\n\n---\n\n".join(
+        f"<Document>\n{doc.page_content}\n</Document>" for doc in search_docs
+    )
+    return {"wiki_results": results}
 
 @tool
 def web_search(query: str) -> dict:
-    """Search the web via Tavily and return formatted top 3 results."""
+    """Search the web via Tavily and return up to 3 results."""
     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}
+    results = "\n\n---\n\n".join(
+        f"<Document>\n{doc.page_content}\n</Document>" for doc in search_docs
+    )
+    return {"web_results": results}
 
 @tool
 def arvix_search(query: str) -> dict:
-    """Search Arxiv for a query and return formatted top 3 results (truncated content)."""
+    """Search Arxiv and return up to 3 truncated results."""
     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}
-
-# Load the system prompt from file
+    results = "\n\n---\n\n".join(
+        f"<Document>\n{doc.page_content[:500]}\n</Document>" for doc in search_docs
+    )
+    return {"arvix_results": results}
+
+# Load system prompt
 with open("system_prompt.txt", "r", encoding="utf-8") as f:
     system_prompt = f.read()
 
 sys_msg = SystemMessage(content=system_prompt)
 
-# Build 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.",
-)
-
 tools = [
-    multiply,
-    add,
-    subtract,
-    divide,
-    modulus,
-    wiki_search,
-    web_search,
-    arvix_search,
+    multiply, add, subtract, divide, modulus,
+    wiki_search, web_search, arvix_search
 ]
 
 def build_graph(provider: str = "groq"):
-    """Build the LangGraph agent graph with the specified provider."""
+    """Build the LangGraph agent with selected LLM provider."""
     if provider == "google":
         llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
     elif provider == "groq":
@@ -124,39 +89,30 @@ def build_graph(provider: str = "groq"):
             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'.")
+        raise ValueError("Invalid provider: choose 'google', 'groq' or 'huggingface'.")
+
     llm_with_tools = llm.bind_tools(tools)
 
     def assistant(state: MessagesState):
-        """Assistant node to process messages with LLM and tools."""
         return {"messages": [llm_with_tools.invoke(state["messages"])]}
 
-    def retriever(state: MessagesState):
-        """Retriever node to find similar questions from vector store."""
-        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_edge(START, "assistant")
     builder.add_conditional_edges("assistant", tools_condition)
     builder.add_edge("tools", "assistant")
 
     return builder.compile()
 
 if __name__ == "__main__":
-    question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
-    graph = build_graph(provider="groq")
+    from langchain_core.messages import HumanMessage
+    question = "What is the capital of France and its population?"
+    graph = build_graph()
     messages = [HumanMessage(content=question)]
-    messages = graph.invoke({"messages": messages})
-    for m in messages["messages"]:
-        m.pretty_print()
+    result = graph.invoke({"messages": messages})
+    for msg in result["messages"]:
+        print(msg.content)