agent.py

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

from langchain_core.tools import tool
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.vectorstores import Chroma
from langchain.tools.retriever import create_retriever_tool
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
from langchain_community.embeddings import SentenceTransformerEmbeddings

from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.memory import MemorySaver

# ---- Tool Definitions ----
@tool
def multiply(a: int, b: int) -> int:
    """Multiply two integers and return the product."""
    return a * b

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

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

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

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

@tool
def optimized_web_search(query: str) -> str:
    """Perform an optimized web search using TavilySearchResults and return concatenated document snippets."""
    try:
        time.sleep(random.uniform(1, 2))
        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:
    """Perform an optimized Wikipedia search and return concatenated document snippets."""
    try:
        time.sleep(random.uniform(0.5, 1))
        docs = WikipediaLoader(query=query, load_max_docs=1).load()
        return "\n\n---\n\n".join(
            f"<Doc src='{d.metadata['source']}'>{d.page_content[:800]}</Doc>"
            for d in docs
        )
    except Exception as e:
        return f"Wikipedia search failed: {e}"

# ---- LLM Integrations ----
load_dotenv()

from langchain_groq import ChatGroq
from langchain_nvidia_ai_endpoints import ChatNVIDIA
from google import genai

import requests

def baidu_ernie_generate(prompt, api_key=None):
    url = "https://api.baidu.com/ernie/v1/generate"
    headers = {"Authorization": f"Bearer {api_key}"}
    data = {"model": "ernie-4.5", "prompt": prompt}
    try:
        resp = requests.post(url, headers=headers, json=data, timeout=30)
        return resp.json().get("result", "")
    except Exception as e:
        return f"ERNIE API error: {e}"

def deepseek_generate(prompt, api_key=None):
    url = "https://api.deepseek.com/v1/chat/completions"
    headers = {"Authorization": f"Bearer {api_key}"}
    data = {"model": "deepseek-chat", "messages": [{"role": "user", "content": prompt}]}
    try:
        resp = requests.post(url, headers=headers, json=data, timeout=30)
        choices = resp.json().get("choices", [{}])
        if choices and "message" in choices[0]:
            return choices[0]["message"].get("content", "")
        return ""
    except Exception as e:
        return f"DeepSeek API error: {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:
    def __init__(self):
        self.tools = [
            multiply, add, subtract, divide, modulus,
            optimized_web_search, optimized_wiki_search
        ]
        self.graph = self._build_graph()

    def _build_graph(self):
        groq_llm = ChatGroq(model="llama3-70b-8192", temperature=0, api_key=os.getenv("GROQ_API_KEY"))
        nvidia_llm = ChatNVIDIA(model="meta/llama3-70b-instruct", temperature=0, api_key=os.getenv("NVIDIA_API_KEY"))

        def router(st: EnhancedAgentState) -> EnhancedAgentState:
            q = st["query"].lower()
            if "groq" in q:      t = "groq"
            elif "nvidia" in q:  t = "nvidia"
            elif "gemini" in q or "google" in q: t = "gemini"
            elif "deepseek" in q: t = "deepseek"
            elif "ernie" in q or "baidu" in q: t = "baidu"
            else: t = "groq"  # default
            return {**st, "agent_type": t}

        def groq_node(st: EnhancedAgentState) -> EnhancedAgentState:
            t0 = time.time()
            sys = SystemMessage(content="Answer as an expert.")
            res = groq_llm.invoke([sys, HumanMessage(content=st["query"])])
            return {**st, "final_answer": res.content, "perf": {"time": time.time() - t0, "prov": "Groq"}}

        def nvidia_node(st: EnhancedAgentState) -> EnhancedAgentState:
            t0 = time.time()
            sys = SystemMessage(content="Answer as an expert.")
            res = nvidia_llm.invoke([sys, HumanMessage(content=st["query"])])
            return {**st, "final_answer": res.content, "perf": {"time": time.time() - t0, "prov": "NVIDIA"}}

        def gemini_node(st: EnhancedAgentState) -> EnhancedAgentState:
            t0 = time.time()
            genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
            model = genai.GenerativeModel("gemini-1.5-pro-latest")
            res = model.generate_content(st["query"])
            return {**st, "final_answer": res.text, "perf": {"time": time.time() - t0, "prov": "Gemini"}}

        def deepseek_node(st: EnhancedAgentState) -> EnhancedAgentState:
            t0 = time.time()
            resp = deepseek_generate(st["query"], api_key=os.getenv("DEEPSEEK_API_KEY"))
            return {**st, "final_answer": resp, "perf": {"time": time.time() - t0, "prov": "DeepSeek"}}

        def baidu_node(st: EnhancedAgentState) -> EnhancedAgentState:
            t0 = time.time()
            resp = baidu_ernie_generate(st["query"], api_key=os.getenv("BAIDU_API_KEY"))
            return {**st, "final_answer": resp, "perf": {"time": time.time() - t0, "prov": "ERNIE"}}

        def pick(st: EnhancedAgentState) -> str:
            return st["agent_type"]

        g = StateGraph(EnhancedAgentState)
        g.add_node("router", router)
        g.add_node("groq", groq_node)
        g.add_node("nvidia", nvidia_node)
        g.add_node("gemini", gemini_node)
        g.add_node("deepseek", deepseek_node)
        g.add_node("baidu", baidu_node)
        g.set_entry_point("router")
        g.add_conditional_edges("router", pick, {
            "groq": "groq",
            "nvidia": "nvidia",
            "gemini": "gemini",
            "deepseek": "deepseek",
            "baidu": "baidu"
        })
        for n in ["groq", "nvidia", "gemini", "deepseek", "baidu"]:
            g.add_edge(n, 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)
        raw_answer = out["final_answer"]
        parts = raw_answer.split('\n\n', 1)
        answer_part = parts[1].strip() if len(parts) > 1 else raw_answer.strip()
        return answer_part

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