Create agent.py

agent.py

@@ -0,0 +1,204 @@
+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():
+    return HybridLangGraphMultiLLMSystem().graph