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from fastapi import FastAPI, HTTPException |
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from pydantic import BaseModel |
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from llama_cpp import Llama |
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from multiprocessing import Process, Queue |
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import uvicorn |
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from dotenv import load_dotenv |
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from difflib import SequenceMatcher |
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load_dotenv() |
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app = FastAPI() |
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models = [ |
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{"repo_id": "Ffftdtd5dtft/gpt2-xl-Q2_K-GGUF", "filename": "gpt2-xl-q2_k.gguf"}, |
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{"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-8B-Instruct-Q2_K-GGUF", "filename": "meta-llama-3.1-8b-instruct-q2_k.gguf"}, |
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{"repo_id": "Ffftdtd5dtft/gemma-2-9b-it-Q2_K-GGUF", "filename": "gemma-2-9b-it-q2_k.gguf"}, |
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{"repo_id": "Ffftdtd5dtft/gemma-2-27b-Q2_K-GGUF", "filename": "gemma-2-27b-q2_k.gguf"}, |
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] |
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llms = [] |
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for model in models: |
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llm = Llama.from_pretrained(repo_id=model['repo_id'], filename=model['filename']) |
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llms.append(llm) |
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class ChatRequest(BaseModel): |
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message: str |
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top_k: int = 50 |
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top_p: float = 0.95 |
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temperature: float = 0.7 |
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def generate_chat_response(request, queue): |
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try: |
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user_input = request.message |
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responses = [] |
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for llm in llms: |
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response = llm.create_chat_completion( |
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messages=[{"role": "user", "content": user_input}], |
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top_k=request.top_k, |
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top_p=request.top_p, |
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temperature=request.temperature |
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) |
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reply = response['choices'][0]['message']['content'] |
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responses.append(reply) |
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best_response = select_best_response(responses, request) |
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queue.put(best_response) |
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except Exception as e: |
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queue.put(f"Error: {str(e)}") |
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def select_best_response(responses, request): |
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coherent_responses = filter_by_coherence(responses, request) |
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best_response = filter_by_similarity(coherent_responses) |
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return best_response |
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def filter_by_coherence(responses, request): |
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return responses |
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def filter_by_similarity(responses): |
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responses.sort(key=len, reverse=True) |
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best_response = responses[0] |
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for i in range(1, len(responses)): |
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ratio = SequenceMatcher(None, best_response, responses[i]).ratio() |
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if ratio < 0.9: |
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best_response = responses[i] |
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break |
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return best_response |
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@app.post("/generate_chat") |
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async def generate_chat(request: ChatRequest): |
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queue = Queue() |
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p = Process(target=generate_chat_response, args=(request, queue)) |
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p.start() |
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p.join() |
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response = queue.get() |
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if "Error" in response: |
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raise HTTPException(status_code=500, detail=response) |
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return {"response": response} |
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if __name__ == "__main__": |
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uvicorn.run(app, host="0.0.0.0", port=8001) |
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