ai - wsl 下运行rag embedding

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refer to:

1. 检查wsl  nvidia-smi

bigbanana@DESKTOP-4OEP3L0:/workspace/cairn_new$ nvidia-smi
Mon Sep  7 06:55:23 2026
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 595.54                 Driver Version: 595.79         CUDA Version: 13.2     |
+-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA GeForce RTX 3080        On  |   00000000:04:00.0  On |                  N/A |
| 50%   53C    P8             17W /  320W |    2906MiB /  20480MiB |      7%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+

2. 所需要的第三方包，并且重启docker

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt update
sudo apt-get install -y nvidia-container-toolkit
source ~/env_8078
sudo apt-get install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo service docker stop
sudo service docker start

4.docker-compose.yaml: 
  # RAG embedding 服务：本地 BGE-M3（中文友好，1024 维）。text-embeddings-inference(TEI)
  cairn-embed:
    # image: ghcr.io/huggingface/text-embeddings-inference:cpu-1.5
    image: ghcr.io/huggingface/text-embeddings-inference:1.5
    container_name: cairn-embed
    volumes:
      # 手动下载的 BGE-M3 裸文件目录(宿主路径 ./embed_model)；TEI 按本地路径加载，先下好文件再启动
      - ./embed_model:/model
    environment:
      HF_HUB_OFFLINE: "1"
    command:
      - --model-id
      - /model
      - --port
      - "8080"
    healthcheck:
      test: ["CMD-SHELL", "bash -c ':> /dev/tcp/127.0.0.1/8080' || exit 1"]
      interval: 10s
      timeout: 5s
      retries: 10
      start_period: 30s
      restart: unless-stopped

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