Upload agent.py

agent.py

@@ -0,0 +1,347 @@
+import cmath
+import os
+from typing import Dict, List, Sequence, TypedDict, cast
+
+from dotenv import load_dotenv
+from langchain.tools.retriever import create_retriever_tool
+from langchain_community.document_loaders import ArxivLoader, WikipediaLoader
+from langchain_community.vectorstores import SupabaseVectorStore
+from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
+from langchain_core.tools import tool
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_groq import ChatGroq
+from langchain_huggingface import (
+    ChatHuggingFace,
+    HuggingFaceEmbeddings,
+    HuggingFaceEndpoint,
+)
+from langchain_tavily import TavilySearch
+from langgraph.graph import END, START, MessagesState, StateGraph
+from langgraph.prebuilt import ToolNode, tools_condition
+from pydantic import BaseModel
+from supabase.client import Client, create_client
+
+# Load environment variables from .env file
+load_dotenv()
+
+
+class WebSearchInput(BaseModel):
+    query: str
+
+
+class WikipediaSearchInput(BaseModel):
+    query: str
+
+
+class ArxivSearchInput(BaseModel):
+    query: str
+
+
+@tool
+def search_web(query: str) -> str:
+    """Search the web using Tavily and return relevant results."""
+
+    """Search Tavily for a query and return maximum 3 results.
+    
+    Args:
+        query: The search query."""
+    search_docs = TavilySearch(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 search_wikipedia(query: str) -> str:
+    """Search Wikipedia using LangChain's loader and return the first document summary."""
+    try:
+        loader = WikipediaLoader(query=query, lang="en", load_max_docs=2)
+        docs = loader.load()
+        if not docs:
+            return {"error": f"No Wikipedia articles found for query: {query}"}
+        formatted_docs = "\n\n---\n\n".join(
+            [f"Wikipedia Article: {query}\n\n{doc.page_content}" for doc in docs]
+        )
+        return {"wiki_results": formatted_docs}
+    except Exception as e:
+        return {"error": f"Error searching Wikipedia: {str(e)}"}
+
+
+@tool
+def arxiv_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 {"arxiv_results": formatted_search_docs}
+
+
+@tool
+def power(a: float, b: float) -> float:
+    """
+    Get the power of two numbers.
+    Args:
+        a (float): the first number
+        b (float): the second number
+    """
+    return a**b
+
+
+@tool
+def square_root(a: float) -> float | complex:
+    """
+    Get the square root of a number.
+    Args:
+        a (float): the number to get the square root of
+    """
+    if a >= 0:
+        return a**0.5
+    return cmath.sqrt(a)
+
+
+@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: float, b: float) -> float:
+    """
+    Divides two numbers.
+    Args:
+        a (float): the first float number
+        b (float): the second float number
+    """
+    if b == 0:
+        raise ValueError("Cannot divided 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
+
+
+# System prompt
+system_prompt = SystemMessage(
+    content="""You are a helpful assistant tasked with answering questions using a set of tools. 
+Now, I will ask you a question. Report your thoughts, and finish your answer with the following template: 
+FINAL ANSWER: [YOUR FINAL ANSWER]. 
+YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, Apply the rules above for each element (number or string), ensure there is exactly one space after each comma.
+Your answer should only start with "FINAL ANSWER: ", then follows with the answer. """
+)
+
+supabase_url = os.environ.get("SUPABASE_URL")
+supabase_service_key = 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(supabase_url, 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.",
+)
+
+# Initialize tools
+tools = [
+    search_wikipedia,
+    search_web,
+    arxiv_search,
+    power,
+    square_root,
+    multiply,
+    divide,
+    subtract,
+    add,
+    modulus,
+]
+
+
+def build_agent_graph(provider: str = "groq"):
+    """Build the graph"""
+
+    # Initialize LLM class
+    try:
+        gemini_api_key = os.getenv("GEMINI_API_KEY")
+        if provider == "groq":
+            # Groq https://console.groq.com/docs/models
+            chat_model = ChatGroq(
+                model="qwen-qwq-32b", temperature=0
+            )  # optional : qwen-qwq-32b gemma2-9b-it
+        elif provider == "gemini":
+            chat_model = ChatGoogleGenerativeAI(
+                model="gemini-2.5-pro",
+                temperature=1.0,
+                max_retries=2,
+                google_api_key=gemini_api_key,
+            )
+        elif provider == "huggingface":
+            llm = HuggingFaceEndpoint(
+                url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
+                temperature=0,
+            )
+            chat_model = ChatHuggingFace(llm=llm, verbose=True)
+        else:
+            raise ValueError("Invalid provider.")
+    except Exception as e:
+        raise Exception(f"Failed to initialize LLM: {str(e)}")
+
+    llm_with_tools = chat_model.bind_tools(tools)
+
+    # Create nodes
+    def assistant(state: MessagesState):
+        """Assistant node"""
+        return {"messages": [llm_with_tools.invoke(state["messages"])]}
+
+    def retriever(state: MessagesState):
+        query = state["messages"][-1].content
+        results = vector_store.similarity_search(query, k=1)
+
+        if not results:
+            print(f"[retriever] No similar documents found for query: {query}")
+            return {
+                "messages": [
+                    AIMessage(content="I couldn't find any similar content in memory.")
+                ]
+            }
+
+        similar_doc = results[0]
+        content = similar_doc.page_content
+
+        if "Final answer :" in content:
+            answer = content.split("Final answer :")[-1].strip()
+        else:
+            answer = content.strip()
+
+        return {"messages": [AIMessage(content=answer)]}
+
+    # Build graph
+    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")
+
+    builder.set_entry_point("retriever")
+    builder.set_finish_point("retriever")
+
+    return builder.compile()
+
+
+# Manual test function
+def test_agent():
+    """Run a manual test of the agent"""
+    print("\n" + "=" * 50)
+    print("Starting Agent Test")
+    print("=" * 50)
+
+    # Check environment variables
+    if not os.getenv("HUGGINGFACEHUB_API_TOKEN"):
+        print("\nError: HUGGINGFACEHUB_API_TOKEN not set")
+        return
+    if not os.getenv("GEMINI_API_KEY"):
+        print("\nError: GEMINI_API_KEY not set")
+        return
+    if not os.getenv("TAVILY_API_KEY"):
+        print("\nWarning: TAVILY_API_KEY not set - web search will be unavailable")
+
+    if not os.getenv("SUPABASE_URL"):
+        print("\nWarning: SUPABASE_URL not set - web search will be unavailable")
+
+    print("\nInitializing agent...")
+    try:
+        graph = build_agent_graph(provider="groq")
+        print("Agent initialized successfully")
+    except Exception as e:
+        print(f"Failed to initialize agent: {str(e)}")
+        return
+
+    # Test a single question
+    question = "Examine the video at https://www.youtube.com/watch?v=1htKBjuUWec.\n\nWhat does Teal'c say in response to the question \"Isn't that hot?\""
+    print("\nTesting question:", question)
+    print("-" * 50)
+
+    try:
+        # Create messages state
+        messages = [HumanMessage(content=question)]
+
+        # Run agent
+        print("\nWaiting for response...")
+        result = graph.invoke({"messages": messages})
+
+        # Get answer
+        if result and "messages" in result and result["messages"]:
+
+            answer = result["messages"][-1].content
+            print("\nResponse received:")
+            print("-" * 20)
+            print(answer)
+            print("-" * 20)
+        else:
+            print("\nError: No response from agent")
+
+    except Exception as e:
+        print(f"\nError processing question: {str(e)}")
+
+    print("\n" + "=" * 50)
+    print("Test Complete")
+    print("=" * 50 + "\n")
+
+
+# Run test if script is run directly
+if __name__ == "__main__":
+    test_agent()