Create backend.py

backend.py

@@ -0,0 +1,47 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import List
+from ai_agent import get_response_from_ai_agent
+
+class RequestState(BaseModel):
+    model_name: str
+    model_provider: str
+    system_prompt: str
+    messages: List[str]
+    allow_search: bool
+
+ALLOWED_MODEL_NAMES = ["llama-3.3-70b-versatile", "mixtral-8x7b-32768"]
+
+app = FastAPI(title="LangGraph AI Agent")
+
+@app.post("/chat")
+async def chat_endpoint(request: RequestState):
+    """
+    API Endpoint to interact with the Chatbot using LangGraph and search tools.
+    """
+    try:
+        if request.model_provider != "Groq":
+            raise HTTPException(status_code=400, detail="Only Groq provider is supported")
+            
+        if request.model_name not in ALLOWED_MODEL_NAMES:
+            raise HTTPException(status_code=400, detail="Invalid model name. Please select a valid Groq model")
+        
+        if not request.messages:
+            raise HTTPException(status_code=400, detail="No message provided")
+        
+        response = get_response_from_ai_agent(
+            llm_id=request.model_name,
+            query=request.messages[0],
+            allow_search=request.allow_search,
+            system_prompt=request.system_prompt or "You are a helpful AI assistant."
+        )
+        
+        return {"response": response}
+        
+    except Exception as e:
+        print(f"Debug - Error in chat_endpoint: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="127.0.0.1", port=9999)