mirror of
https://github.com/open-webui/llama-cpp-runner
synced 2025-05-10 06:41:26 +00:00
117 lines
3.6 KiB
Docker
117 lines
3.6 KiB
Docker
FROM python:3.11-slim
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WORKDIR /app
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# Install only essential packages and clean up in one layer to reduce image size
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RUN apt-get update && apt-get install -y --no-install-recommends \
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curl \
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wget \
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git \
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build-essential \
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&& apt-get clean \
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&& rm -rf /var/lib/apt/lists/*
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# Copy only necessary files
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COPY pyproject.toml README.md LICENSE /app/
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COPY src/ /app/src/
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# Install the package in development mode and required dependencies
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RUN pip install --no-cache-dir -e . && pip install --no-cache-dir requests fastapi uvicorn
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# Create volume mount points
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VOLUME /models
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VOLUME /cache
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# Create proxy server script directly in the Dockerfile
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RUN echo 'import os\n\
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import uvicorn\n\
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from fastapi import FastAPI, Request\n\
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from fastapi.responses import StreamingResponse, JSONResponse\n\
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from llama_cpp_runner.main import LlamaCpp\n\
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\n\
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app = FastAPI(title="LlamaCpp Proxy")\n\
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\n\
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# Initialize the LlamaCpp class\n\
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models_dir = os.environ.get("MODELS_DIR", "/models")\n\
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cache_dir = os.environ.get("CACHE_DIR", "/cache")\n\
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verbose = os.environ.get("VERBOSE", "true").lower() == "true"\n\
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timeout = int(os.environ.get("TIMEOUT_MINUTES", "30"))\n\
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\n\
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print(f"Models directory: {models_dir}")\n\
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print(f"Cache directory: {cache_dir}")\n\
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\n\
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# Create the LlamaCpp instance\n\
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llama_runner = LlamaCpp(\n\
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models_dir=models_dir,\n\
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cache_dir=cache_dir, \n\
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verbose=verbose, \n\
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timeout_minutes=timeout\n\
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)\n\
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\n\
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@app.get("/")\n\
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def read_root():\n\
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"""Get server status and list of available models."""\n\
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return {"status": "running", "models": llama_runner.list_models()}\n\
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\n\
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@app.post("/v1/chat/completions")\n\
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async def chat_completions(request: Request):\n\
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"""Forward chat completion requests to the LlamaCpp server."""\n\
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try:\n\
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body = await request.json()\n\
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\n\
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if "model" not in body:\n\
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return JSONResponse(\n\
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status_code=400,\n\
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content={"error": "Model not specified in request"}\n\
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)\n\
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\n\
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try:\n\
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result = llama_runner.chat_completion(body)\n\
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\n\
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# Handle streaming responses\n\
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if body.get("stream", False):\n\
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async def generate():\n\
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for line in result:\n\
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if line:\n\
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yield f"data: {line}\\n\\n"\n\
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yield "data: [DONE]\\n\\n"\n\
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\n\
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return StreamingResponse(generate(), media_type="text/event-stream")\n\
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else:\n\
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return result\n\
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except Exception as e:\n\
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return JSONResponse(\n\
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status_code=500,\n\
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content={"error": str(e)}\n\
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)\n\
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except Exception as e:\n\
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return JSONResponse(\n\
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status_code=400,\n\
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content={"error": f"Invalid request: {str(e)}"}\n\
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)\n\
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\n\
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@app.get("/models")\n\
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def list_models():\n\
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"""List all available models."""\n\
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return {"models": llama_runner.list_models()}\n\
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\n\
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if __name__ == "__main__":\n\
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print("Starting LlamaCpp Proxy Server on port 3636")\n\
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models = llama_runner.list_models()\n\
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print(f"Available models: {models}")\n\
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if not models:\n\
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print("WARNING: No models found in the models directory.")\n\
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uvicorn.run(app, host="0.0.0.0", port=3636)' > /app/proxy_server.py
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# Expose the proxy server port
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EXPOSE 3636
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# Set environment variables
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ENV PYTHONUNBUFFERED=1
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ENV MODELS_DIR=/models
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ENV CACHE_DIR=/cache
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ENV VERBOSE=true
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ENV TIMEOUT_MINUTES=30
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# Command to run when the container starts
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CMD ["python", "/app/proxy_server.py"] |