Update agent.py

agent.py

@@ -1,225 +1,151 @@
-"""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
-
-class BasicAgent:
-    def __init__(self, provider="groq"):
-        self.graph = build_graph(provider=provider)
-    
-    def run(self, question: str) -> str:
-        messages = [HumanMessage(content=question)]
-        response = self.graph.invoke({"messages": messages})
-        # Return the last message from the assistant node (the answer)
-        return response["messages"][-1].content
+from supabase.client import create_client
+from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_community.vectorstores import SupabaseVectorStore
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
+from langchain_groq import ChatGroq
+from langchain_google_genai import ChatGoogleGenerativeAI
 
+load_dotenv()
 
+# Load system prompt from file
+with open("system_prompt.txt", "r", encoding="utf-8") as f:
+    system_prompt_text = f.read()
 
-load_dotenv()
+sys_msg = SystemMessage(content=system_prompt_text)
 
+# Define simple math tools as example
 @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
-    """
+def divide(a: int, b: int) -> float:
     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}
+    docs = WikipediaLoader(query=query, load_max_docs=2).load()
+    return "\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 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}
+    docs = TavilySearchResults(max_results=3).invoke(query=query)
+    return "\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 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}
-
+def arxiv_search(query: str) -> str:
+    docs = ArxivLoader(query=query, load_max_docs=3).load()
+    return "\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 docs
+    )
 
+tools = [multiply, add, subtract, divide, modulus, wiki_search, web_search, arxiv_search]
 
-# load the system prompt from the file
-with open("system_prompt.txt", "r", encoding="utf-8") as f:
-    system_prompt = f.read()
+# Setup Supabase vector store retriever
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
 
-# System message
-sys_msg = SystemMessage(content=system_prompt)
+supabase = create_client(
+    os.environ.get("SUPABASE_URL"),
+    os.environ.get("SUPABASE_SERVICE_KEY")
+)
 
-# 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,
+    embedding=embeddings,
     table_name="documents",
     query_name="match_documents_langchain",
 )
-create_retriever_tool = create_retriever_tool(
+
+retriever_tool = create_retriever_tool(
     retriever=vector_store.as_retriever(),
     name="Question Search",
-    description="A tool to retrieve similar questions from a vector store.",
+    description="Retrieve similar questions from vector store.",
 )
 
+# Add retriever tool if you want
+tools.append(retriever_tool)
 
-
-tools = [
-    multiply,
-    add,
-    subtract,
-    divide,
-    modulus,
-    wiki_search,
-    web_search,
-    arvix_search,
-]
-
-# Build graph function
-def build_graph(provider: str = "groq"):
-    """Build the graph"""
-    # Load environment variables from .env file
+def build_graph(provider="groq"):
     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",
                 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):
-        """Retriever node"""
-        similar_question = vector_store.similarity_search(state["messages"][0].content)
+    def retriever_node(state: MessagesState):
+        similar = 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}",
+            content=f"Here is a similar question and answer for reference:\n\n{similar[0].page_content}"
         )
         return {"messages": [sys_msg] + state["messages"] + [example_msg]}
 
+    def assistant_node(state: MessagesState):
+        # This will prompt the model with system prompt + question + context, expecting reasoning + FINAL ANSWER
+        return {"messages": [llm_with_tools.invoke(state["messages"])]}
+
     builder = StateGraph(MessagesState)
-    builder.add_node("retriever", retriever)
-    builder.add_node("assistant", assistant)
+    builder.add_node("retriever", retriever_node)
+    builder.add_node("assistant", assistant_node)
     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_conditional_edges("assistant", tools_condition)
     builder.add_edge("tools", "assistant")
 
-    # Compile graph
     return builder.compile()
 
-# test
+class BasicAgent:
+    def __init__(self, provider="groq"):
+        self.graph = build_graph(provider=provider)
+    def run(self, question: str) -> str:
+        messages = [HumanMessage(content=question)]
+        response = self.graph.invoke({"messages": messages})
+        # Return the last message (should start with FINAL ANSWER)
+        return response["messages"][-1].content
+
 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()
+    agent = BasicAgent(provider="groq")
+    q = "When was a picture of St Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
+    print(agent.run(q))
+