sajad-dev
94355af62b
- Introduced LLM configuration in rag_config.yaml and corresponding LLMConfig class in config.py. - Updated load_rag_config function to parse LLM settings from the configuration file. - Added new API route for RAG in urls.py to facilitate access to the chat model. - Modified QdrantVectorStore to use query_points method for improved functionality.
44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
"""
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ویوهای RAG — چت با استریم
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"""
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from django.http import StreamingHttpResponse
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from rest_framework import status
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from rest_framework.request import Request
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from rest_framework.response import Response
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from rest_framework.views import APIView
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from .chat import chat_rag_stream
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class ChatView(APIView):
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"""
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چت RAG با استریم.
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POST با {"message": "متن سوال"} یا query param message
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"""
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def post(self, request: Request):
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message = request.data.get("message") or request.query_params.get("message")
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if not message or not isinstance(message, str):
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return Response(
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{"code": 400, "msg": "پارامتر message الزامی است."},
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status=status.HTTP_400_BAD_REQUEST,
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)
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message = str(message).strip()
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if not message:
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return Response(
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{"code": 400, "msg": "پیام نباید خالی باشد."},
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status=status.HTTP_400_BAD_REQUEST,
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)
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def generate():
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try:
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for chunk in chat_rag_stream(message):
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yield chunk
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except Exception as e:
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yield f"\n[خطا: {e}]"
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return StreamingHttpResponse(
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generate(),
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content_type="text/plain; charset=utf-8",
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)
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