frontapi.py

from fastapi import FastAPI, HTTPException, Request
from fastapi.responses import JSONResponse
from pydantic import BaseModel
import uvicorn
import asyncio
import json

app = FastAPI()

class ChatCompletionRequest(BaseModel):
    model: str
    messages: list
    temperature: float = 1.0
    top_p: float = 1.0
    n: int = 1
    stream: bool = False
    stop: list = None
    max_tokens: int = None
    presence_penalty: float = 0.0
    frequency_penalty: float = 0.0
    logit_bias: dict = None
    user: str = None

@app.post("/v1/chat/completions")
async def chat_completions(request: ChatCompletionRequest):
    try:
        
        # Here we would process the request, analyze the prompt,
        # inject extra context, and send it to the actual LLM.
        # For now, we'll just return a mock response.

        messages = request.messages
        prompt = messages[-1]['content'] if messages else ""


        


        response = {
            "id": "chatcmpl-123",
            "object": "chat.completion",
            "created": 1677652288,
            "model": request.model,
            "choices": [{
                "index": 0,
                "message": {
                    "role": "assistant",
                    "content": "This is a mock response from the shared context LLM API."
                },
                "finish_reason": "stop"
            }],
            "usage": {
                "prompt_tokens": 9,
                "completion_tokens": 12,
                "total_tokens": 21
            }
        }

        # Here you would save the original prompt and final output for Pinecone, etc.

        return JSONResponse(content=response)

    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/v1/completions")
async def completions(request: Request):
    # This endpoint is for text completions (non-chat model)
    # Implement similar logic as chat_completions
    body = await request.json()
    # Process the request...
    return JSONResponse(content={"text": "Mock completion response"})

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)