veryfinal.py

import os
import time
import random
import operator
from typing import List, Dict, Any, TypedDict, Annotated
from dotenv import load_dotenv

from langchain_core.tools import tool
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader
from langgraph.graph import StateGraph, END
from langgraph.checkpoint.memory import MemorySaver
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
from langchain_groq import ChatGroq

load_dotenv()  # expects GROQ_API_KEY in your .env

@tool
def multiply(a: int, b: int) -> int: return a * b

@tool
def add(a: int, b: int) -> int: return a + b

@tool
def subtract(a: int, b: int) -> int: return a - b

@tool
def divide(a: int, b: int) -> float:
    if b == 0:
        raise ValueError("Cannot divide by zero.")
    return a / b

@tool
def modulus(a: int, b: int) -> int: return a % b

@tool
def optimized_web_search(query: str) -> str:
    try:
        time.sleep(random.uniform(0.7, 1.5))
        docs = TavilySearchResults(max_results=2).invoke(query=query)
        return "\n\n---\n\n".join(
            f"<Doc url='{d.get('url','')}'>{d.get('content','')[:500]}</Doc>"
            for d in docs
        )
    except Exception as e:
        return f"Web search failed: {e}"

@tool
def optimized_wiki_search(query: str) -> str:
    try:
        time.sleep(random.uniform(0.3, 1))
        docs = WikipediaLoader(query=query, load_max_docs=1).load()
        return "\n\n---\n\n".join(
            f"<Doc src='{d.metadata.get('source','Wikipedia')}'>{d.page_content[:800]}</Doc>"
            for d in docs
        )
    except Exception as e:
        return f"Wikipedia search failed: {e}"

class EnhancedAgentState(TypedDict):
    messages: Annotated[List[HumanMessage | AIMessage], operator.add]
    query: str
    agent_type: str
    final_answer: str
    perf: Dict[str, Any]
    agno_resp: str

class HybridLangGraphMultiLLMSystem:
    """
    Router that picks between Groq-hosted Llama-3 8B, Llama-3 70B (default),
    and Groq-hosted DeepSeek-Chat according to the query content.
    """
    def __init__(self):
        self.tools = [
            multiply, add, subtract, divide, modulus,
            optimized_web_search, optimized_wiki_search
        ]
        self.graph = self._build_graph()

    def _llm(self, model_name: str):
        return ChatGroq(
            model=model_name,
            temperature=0,
            api_key=os.getenv("GROQ_API_KEY")
        )

    def _build_graph(self):
        llama8_llm  = self._llm("llama3-8b-8192")
        llama70_llm = self._llm("llama3-70b-8192")
        deepseek_llm = self._llm("deepseek-chat")

        def router(st: EnhancedAgentState) -> EnhancedAgentState:
            q = st["query"].lower()
            if "llama-8" in q:
                t = "llama8"
            elif "deepseek" in q:
                t = "deepseek"
            else:
                t = "llama70"
            return {**st, "agent_type": t}

        def llama8_node(st: EnhancedAgentState) -> EnhancedAgentState:
            t0 = time.time()
            sys = SystemMessage(content="You are a helpful AI assistant.")
            res = llama8_llm.invoke([sys, HumanMessage(content=st["query"])])
            return {**st,
                    "final_answer": res.content,
                    "perf": {"time": time.time() - t0, "prov": "Groq-Llama3-8B"}}

        def llama70_node(st: EnhancedAgentState) -> EnhancedAgentState:
            t0 = time.time()
            sys = SystemMessage(content="You are a helpful AI assistant.")
            res = llama70_llm.invoke([sys, HumanMessage(content=st["query"])])
            return {**st,
                    "final_answer": res.content,
                    "perf": {"time": time.time() - t0, "prov": "Groq-Llama3-70B"}}

        def deepseek_node(st: EnhancedAgentState) -> EnhancedAgentState:
            t0 = time.time()
            sys = SystemMessage(content="You are a helpful AI assistant.")
            res = deepseek_llm.invoke([sys, HumanMessage(content=st["query"])])
            return {**st,
                    "final_answer": res.content,
                    "perf": {"time": time.time() - t0, "prov": "Groq-DeepSeek"}}

        g = StateGraph(EnhancedAgentState)
        g.add_node("router", router)
        g.add_node("llama8", llama8_node)
        g.add_node("llama70", llama70_node)
        g.add_node("deepseek", deepseek_node)
        g.set_entry_point("router")
        g.add_conditional_edges("router", lambda s: s["agent_type"],
                                {"llama8": "llama8", "llama70": "llama70", "deepseek": "deepseek"})
        g.add_edge("llama8", END)
        g.add_edge("llama70", END)
        g.add_edge("deepseek", END)
        return g.compile(checkpointer=MemorySaver())

    def process_query(self, q: str) -> str:
        state = {
            "messages": [HumanMessage(content=q)],
            "query": q,
            "agent_type": "",
            "final_answer": "",
            "perf": {},
            "agno_resp": ""
        }
        cfg = {"configurable": {"thread_id": f"hyb_{hash(q)}"}}
        out = self.graph.invoke(state, cfg)
        return out.get("final_answer", "").strip()

def build_graph(provider: str | None = None):
    return HybridLangGraphMultiLLMSystem().graph

if __name__ == "__main__":
    qa_system = HybridLangGraphMultiLLMSystem()
    # Test each model
    print(qa_system.process_query("llama-8: What is the capital of France?"))
    print(qa_system.process_query("llama-70: Tell me about quantum mechanics."))
    print(qa_system.process_query("deepseek: What is the Riemann Hypothesis?"))