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main.py

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+import os
+from fastapi import FastAPI
+from pydantic import BaseModel
+from tavily import TavilyClient
+from datetime import date
+from langchain_ollama.llms import OllamaLLM
+
+app = FastAPI()
+
+class PromptRequest(BaseModel):
+    prompt: str
+    temperature: float = 0.5
+
+@app.get("/")
+def health():
+    return {"ok": True}
+
+today_date = date.today()
+
+def search_tool(query:str):
+    api_key = os.environ.get("TAVILY_API_KEY")
+    client = TavilyClient(api_key)
+    response = client.search(query=query, include_answer="advanced", search_depth="advanced")
+    return response
+
+@app.post("/gemma4:e2b")
+async def generate_response(request: PromptRequest):
+    llm = OllamaLLM(
+        model="gemma4:e2b",
+        temperature=request.temperature,
+        base_url="http://localhost:11436"
+    )
+    tool_prompt = f"System Role: You are an autonomous AI Agent with real-time internet access. Current Date: {today_date} TOOL_DEFINITION: - Name: Search_tool - Activation Command: Search [Your Query Here]"
+    response = llm.invoke(f'{tool_prompt}, User-Query:-{request.prompt}')
+
+    if "Search " in response:
+        new_query = response.removeprefix("Search ")
+        search_response = search_tool(query=new_query)
+        new_response = llm.invoke(f"Extra_information:- {search_response} User-Query:- {request.prompt}")
+        return {"response": new_response}
+    else:
+        return {"response": response}
+
+@app.post("/qwen3.5:2b")
+async def qwen_generate_response(request:PromptRequest):
+    llm = OllamaLLM(
+        model="qwen3.5:2b",
+        temperature=request.temperature,
+        base_url="http://localhost:11435"
+    )
+    tool_prompt = f"System Role: You are an autonomous AI Agent with real-time internet access. Current Date: {today_date} TOOL_DEFINITION: - Name: Search_tool - Activation Command: Search [Your Query Here]"
+    response = llm.invoke(f'{tool_prompt}, User-Query:-{request.prompt}')
+
+    if "Search " in response:
+        new_query = response.removeprefix("Search ")
+        search_response = search_tool(query=new_query)
+        new_response = llm.invoke(f"Extra_information:- {search_response} User-Query:- {request.prompt}")
+        return {"response": new_response}
+    else:
+        return {"response": response}