agent.py

import os
from dotenv import load_dotenv
from typing import List, Dict, Any, Optional
import tempfile
import pandas as pd
import numpy as np

"""Langraph"""
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import ToolNode, tools_condition
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_groq import ChatGroq
from langchain_huggingface import (
    ChatHuggingFace,
    HuggingFaceEndpoint,
)
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langchain_community.utilities import SerpAPIWrapper

load_dotenv()

### =============== SEARCH TOOLS =============== ###

@tool
def serpapi_search(query: str) -> str:
    """Search the web using SerpAPI.
    Args:
        query: The search query."""
    try:
        # Get API key from environment variable
        api_key = os.getenv("SERPAPI_API_KEY")
        if not api_key:
            return {"search_results": "Error: SERPAPI_API_KEY not found in environment variables."}
        
        # Initialize SerpAPIWrapper with the API key
        search = SerpAPIWrapper(serpapi_api_key=api_key)
        
        # Perform the search
        results = search.run(query)
        
        if not results or results.strip() == "":
            return {"search_results": "No search results found."}
        
        return {"search_results": results}
    except Exception as e:
        return {"search_results": f"Error performing search: {str(e)}"}

### =============== DOCUMENT PROCESSING TOOLS =============== ###
# File handling still requires external tools

@tool
def save_and_read_file(content: str, filename: Optional[str] = None) -> str:
    """
    Save content to a file and return the path.
    Args:
        content (str): the content to save to the file
        filename (str, optional): the name of the file. If not provided, a random name file will be created.
    """
    temp_dir = tempfile.gettempdir()
    if filename is None:
        temp_file = tempfile.NamedTemporaryFile(delete=False, dir=temp_dir)
        filepath = temp_file.name
    else:
        filepath = os.path.join(temp_dir, filename)

    with open(filepath, "w") as f:
        f.write(content)

    return f"File saved to {filepath}. You can read this file to process its contents."


# load the system prompt from the file
with open("system_prompt.txt", "r", encoding="utf-8") as f:
    system_prompt = f.read()
print(system_prompt)

# System message
sys_msg = SystemMessage(content=system_prompt)

tools = [
    serpapi_search,
    save_and_read_file,
]


# Build graph function
def build_graph(provider: str = "openai"):
    """Build the graph"""
    # Load environment variables from .env file
    if provider == "openai":
        llm = ChatOpenAI(model="gpt-4o", temperature=0)
    elif provider == "groq":
        llm = ChatGroq(model="qwen-qwq-32b", temperature=0)
    elif provider == "huggingface":
        llm = ChatHuggingFace(
            llm=HuggingFaceEndpoint(
                repo_id="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
                task="text-generation",
                max_new_tokens=1024,
                do_sample=False,
                repetition_penalty=1.03,
                temperature=0,
            ),
            verbose=True,
        )
    else:
        raise ValueError("Invalid provider. Choose 'openai', 'groq', or 'huggingface'.")
    
    # Bind tools to LLM
    llm_with_tools = llm.bind_tools(tools)

    # Node
    def assistant(state: MessagesState):
        """Assistant node"""
        # Add system message at the beginning of messages
        messages = [sys_msg] + state["messages"]
        response = llm_with_tools.invoke(messages)
        # Return the response as is
        return {"messages": state["messages"] + [response]}

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

    # Compile graph
    return builder.compile()


# test
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="openai")
    messages = [HumanMessage(content=question)]
    messages = graph.invoke({"messages": messages})
    for m in messages["messages"]:
        m.pretty_print()