Update agent.py

agent.py

@@ -1,213 +1,104 @@
 # agent.py
 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 typing import TypedDict, Annotated, Sequence, Dict, Any, List
+from langchain_core.messages import BaseMessage, HumanMessage
 from langchain_core.tools import tool
-from langchain.tools.retriever import create_retriever_tool
-from supabase.client import Client, create_client
+from langchain_openai import ChatOpenAI
+from langgraph.graph import END, StateGraph
+from langgraph.prebuilt import ToolNode
+from langchain_community.tools import DuckDuckGoSearchResults
+from langchain_community.utilities import WikipediaAPIWrapper
+from langchain.agents import create_tool_calling_agent, AgentExecutor
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+import operator
+from langchain_experimental.utilities import PythonREPL
 
 load_dotenv()
 
-@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
+class AgentState(TypedDict):
+    messages: Annotated[Sequence[BaseMessage], operator.add]
+    sender: str
 
 @tool
-def subtract(a: int, b: int) -> int:
-    """Subtract two numbers.
-    
-    Args:
-        a: first int
-        b: second int
-    """
-    return a - b
+def wikipedia_search(query: str) -> str:
+    """Search Wikipedia for information."""
+    return WikipediaAPIWrapper().run(query)
 
 @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
+def web_search(query: str, num_results: int = 3) -> list:
+    """Search the web for current information."""
+    return DuckDuckGoSearchResults(num_results=num_results).run(query)
 
 @tool
-def modulus(a: int, b: int) -> int:
-    """Get the modulus of two numbers.
-    
-    Args:
-        a: first int
-        b: second int
-    """
-    return a % b
+def calculate(expression: str) -> str:
+    """Evaluate mathematical expressions."""
+    python_repl = PythonREPL()
+    return python_repl.run(expression)
 
-@tool
-def wiki_search(query: str) -> str:
-    """Search Wikipedia for a query and return maximum 2 results.
+class BasicAgent:
+    """A complete langgraph agent implementation."""
     
-    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.
+    def __init__(self, model_name: str = "gpt-3.5-turbo"):
+        self.tools = [wikipedia_search, web_search, calculate]
+        self.llm = ChatOpenAI(model=model_name, temperature=0.7)
+        self.agent_executor = self._build_agent_executor()
+        self.workflow = self._build_workflow()  # Initialize workflow here
     
-    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
+    def _build_agent_executor(self) -> AgentExecutor:
+        """Build the agent executor with tools."""
+        prompt = ChatPromptTemplate.from_messages([
+            ("system", "You are a helpful AI assistant. Use tools when needed."),
+            MessagesPlaceholder(variable_name="messages"),
+            MessagesPlaceholder(variable_name="agent_scratchpad"),
         ])
-    return {"web_results": formatted_search_docs}
-
-@tool
-def arvix_search(query: str) -> str:
-    """Search Arxiv for a query and return maximum 3 result.
+        agent = create_tool_calling_agent(self.llm, self.tools, prompt)
+        return AgentExecutor(agent=agent, tools=self.tools, verbose=True)
     
-    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}
-
-
-
-# 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(
-    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,
-]
-
-# 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
-    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,
-            ),
+    def _build_workflow(self) -> StateGraph:
+        """Build and compile the agent workflow."""
+        workflow = StateGraph(AgentState)
+        
+        workflow.add_node("agent", self._run_agent)
+        workflow.add_node("tools", ToolNode(self.tools))
+        
+        workflow.set_entry_point("agent")
+        workflow.add_conditional_edges(
+            "agent",
+            self._should_continue,
+            {"continue": "tools", "end": END}
         )
-    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"])]}
+        workflow.add_edge("tools", "agent")
+        
+        return workflow.compile()
     
-    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
+    def _run_agent(self, state: AgentState) -> Dict[str, Any]:
+        """Execute the agent."""
+        response = self.agent_executor.invoke({"messages": state["messages"]})
+        return {"messages": [response["output"]]}
+    
+    def _should_continue(self, state: AgentState) -> str:
+        """Determine if the workflow should continue."""
+        last_message = state["messages"][-1]
+        return "continue" if last_message.additional_kwargs.get("tool_calls") else "end"
+    
+    def __call__(self, question: str) -> str:
+        """Process a user question and return a response."""
+        # Initialize state with the user's question
+        state = AgentState(messages=[HumanMessage(content=question)], sender="user")
+        
+        # Execute the workflow
+        for output in self.workflow.stream(state):
+            for key, value in output.items():
+                if key == "messages":
+                    for message in value:
+                        if isinstance(message, BaseMessage):
+                            return message.content
+        
+        return "Sorry, I couldn't generate a response."
+
+# Example usage
 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()
+    response = agent("What's the capital of France?")
+    print(response)