agent.py

"""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_anthropic import ChatAnthropic
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, AIMessage
from langchain_core.tools import tool
from langchain.tools.retriever import create_retriever_tool
from supabase.client import Client, create_client
import re

# === Load environment ===
load_dotenv()

# === Tools ===
@tool
def multiply(a: int, b: int) -> int:
    """Multiplies two integers and returns the result."""
    return a * b

@tool
def add(a: int, b: int) -> int:
    """Adds two integers and returns the sum."""
    return a + b

@tool
def subtract(a: int, b: int) -> int:
    """Subtracts the second integer from the first and returns the result."""
    return a - b

@tool
def divide(a: int, b: int) -> float:
    """Divides the first integer by the second and returns the result as a float."""
    if b == 0:
        raise ValueError("Cannot divide by zero.")
    return a / b

@tool
def modulus(a: int, b: int) -> int:
    """Returns the remainder of dividing the first integer by the second."""
    return a % b

@tool
def wiki_search(query: str) -> str:
    """Searches Wikipedia for a query and returns the top 2 results as a formatted string."""
    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
    return "\n\n---\n\n".join([doc.page_content for doc in search_docs])

@tool
def web_search(query: str) -> str:
    """Uses Tavily to search the web for a query and returns the top 3 result snippets."""
    search_docs = TavilySearchResults(max_results=3).invoke(query=query)
    return "\n\n---\n\n".join([doc.page_content for doc in search_docs])

@tool
def arvix_search(query: str) -> str:
    """Searches Arxiv for academic papers related to the query and returns the top 3 abstracts."""
    search_docs = ArxivLoader(query=query, load_max_docs=3).load()
    return "\n\n---\n\n".join([doc.page_content[:1000] for doc in search_docs])

# === System Prompt ===
with open("system_prompt.txt", "r", encoding="utf-8") as f:
    system_prompt = f.read()
sys_msg = SystemMessage(content=system_prompt)

# === Embedding & Vector DB ===
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
supabase: Client = create_client(os.getenv("SUPABASE_URL"), os.getenv("SUPABASE_SERVICE_KEY"))
vector_store = SupabaseVectorStore(
    client=supabase,
    embedding=embeddings,
    table_name="Vector_Test",
    query_name="match_documents_langchain",
)

# === Tools ===
tools = [multiply, add, subtract, divide, modulus, wiki_search, web_search, arvix_search]

# === LangGraph Agent Definition ===
def build_graph(provider: str = "claude"):
    if provider == "claude":
        llm = ChatAnthropic(
            model="claude-3-opus-20240229",
            temperature=0,
            anthropic_api_key=os.getenv("CLAUDE_API_KEY")
        )
    elif provider == "groq":
        llm = ChatGroq(model="qwen-qwq-32b", temperature=0)
    elif provider == "google":
        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
    elif provider == "huggingface":
        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")

    llm_with_tools = llm.bind_tools(tools)

    def retriever(state: MessagesState):
        query = state["messages"][-1].content
        similar = vector_store.similarity_search(query)
        return {"messages": [sys_msg, state["messages"][-1], HumanMessage(content=f"Reference: {similar[0].page_content}")]}

    def assistant(state: MessagesState):
        response = llm_with_tools.invoke(state["messages"])
        return {"messages": state["messages"] + [response]}

    def formatter(state: MessagesState):
    last = state["messages"][-1].content.strip()

    cleaned = re.sub(r"<.*?>", "", last)
    cleaned = re.sub(r"(Final\s*Answer:|Answer:)", "", cleaned, flags=re.IGNORECASE)
    cleaned = cleaned.strip().split("\n")[0].strip()
    return {"messages": [AIMessage(content=cleaned)]}

    builder = StateGraph(MessagesState)
    builder.add_node("retriever", retriever)
    builder.add_node("assistant", assistant)
    builder.add_node("tools", ToolNode(tools))
    builder.add_node("formatter", formatter)
    builder.add_edge(START, "retriever")
    builder.add_edge("retriever", "assistant")
    builder.add_conditional_edges("assistant", tools_condition)
    builder.add_edge("tools", "assistant")
    builder.add_edge("assistant", "formatter")

    return builder.compile()

# === Test ===
if __name__ == "__main__":
    graph = build_graph("claude")
    result = graph.invoke({"messages": [HumanMessage(content="What is the capital of France?")]})
    for m in result["messages"]:
        m.pretty_print()