agent_graph.py

import os
from dotenv import load_dotenv
from langchain.tools import tool
from langgraph.graph import StateGraph, END, START, MessagesState
from langgraph.prebuilt import tools_condition, ToolNode
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.tools import tool
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
from langchain_community.document_loaders import WikipediaLoader

load_dotenv()

# ---- TOOL DEFINITIONS ----

@tool
def multiply(a: int, b: int) -> int:
    """Multiply two integers."""
    return a * b

@tool
def divide(a: int, b: int) -> float:
    """Divide two integers."""
    return a / b

@tool
def subtract(a: int, b: int) -> int:
    """Subtract b from a."""
    return a - b

@tool
def add(a: int, b: int) -> int:
    """Add two integers."""
    return a + b

@tool
def exponential(base: int, exponent: int) -> int:
    """Raise base to exponent."""
    return base ** exponent

@tool
def tavily_search(query: str) -> str:
    """Search the web for a given query using the Tavily API (returns detailed snippets)."""
    import requests
    response = requests.post(
        "https://api.tavily.com/search",
        headers={"Content-Type": "application/json"},
        json={
            "api_key": os.getenv("TAVILY_API_KEY"),
            "query": query,
            "search_depth": "advanced",
            "max_results": 3,
        },
    )
    data = response.json()
    return "\n\n".join([r.get("content", "") for r in data.get("results", [])])

@tool
def wiki_lookup(query: str) -> str:
    """Search Wikipedia for a given query and return article content."""
    docs = WikipediaLoader(query=query, load_max_docs=1).load()
    return docs[0].page_content if docs else "No Wikipedia page found."

# ---- TOOL LIST ----

tools = [
    multiply,
    add,
    subtract,
    divide,
    exponential,
    tavily_search,
    wiki_lookup,
]

# ---- BUILD GRAPH ----

def build_graph(provider: str = "google"):
    """Build LangGraph agent with tools and selected LLM."""
    
    if provider == "google":
        llm = ChatGoogleGenerativeAI(
            model="gemini-2.0-flash", temperature=0
        )
    elif provider == "HF_model":
        llm = ChatHuggingFace(
            llm=HuggingFaceEndpoint(
                repo_id="mistralai/Mistral-7B-Instruct-v0.1",
                temperature=0,
            )
        )
    else:
        raise ValueError("Invalid provider. Choose 'google' or 'HF_model'.")

    llm_with_tools = llm.bind_tools(tools)

    sys_msg = """You are a general AI assistant. I will ask you a question. 
    
    Your final answer must strictly follow this format: 
    FINAL ANSWER: [YOUR FINAL ANSWER]. 

    Only write the answer in that exact format. Do not explain anything. Do not include any other text.
    
    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 above rules depending of whether the element to be put in the list is a number or a string."""

    def assistant(state: MessagesState):
        messages = [
            SystemMessage(content=sys_msg),
            *state["messages"]
        ]
        return {"messages": [llm_with_tools.invoke(messages)]}


    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")

    return builder.compile()