在docker内部署deepseek rag环境

docker run -d \
  --name ollama \
  -e OLLAMA_MODELS=/root/models \
  -v learn/langchain/ollama/data:/root/.ollama \
  -v /learn/langchain/ollama/models:/root/models \
  -p 11434:11434 \
  ollama/ollama:latest

docker exec -it ollama ollama run deepseek-r1:1.5b

% docker exec -it ollama ollama list
NAME                       ID              SIZE      MODIFIED
deepseek-r1:1.5b           a42b25d8c10a    1.1 GB    12 minutes ago

% curl 'http://127.0.0.1:11434/'
Ollama is running

verifying sha256 digest
writing manifest
success
>>> 你叫什么名字
<think>
</think>
您好！我是由中国的深度求索（Deep^@Seek）

docker exec -it ollama ollama pull nomic-embed-text:latest

% docker exec -it ollama ollama run nomic-embed-text:latest 
verifying sha256 digest
writing manifest
success
Error: "nomic-embed-text:latest" does not support generate

% docker exec -it ollama ollama list
NAME                       ID              SIZE      MODIFIED
nomic-embed-text:latest    0a109f422b47    274 MB    46 seconds ago
deepseek-r1:1.5b           a42b25d8c10a    1.1 GB    12 minutes ago

% curl http://localhost:11434/api/embeddings -d '{
  "model": "nomic-embed-text:latest",
  "prompt": "The sky is blue because of Rayleigh scattering"
}'

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docker run -d -p 6333:6333 -p 6334:6334 \
-v /learn/langchain/qdrant/storage:/qdrant/storage \
qdrant/qdrant:latest

           _                 _
  __ _  __| |_ __ __ _ _ __ | |_
 / _` |/ _` | '__/ _` | '_ \| __|
| (_| | (_| | | | (_| | | | | |_
 \__, |\__,_|_|  \__,_|_| |_|\__|
    |_|
Version: 1.13.6, build: 4db98ecd
Access web UI at http://localhost:6333/dashboard

PUT collections/star_charts
{
  "vectors": {
    "size": 4,
    "distance": "Dot"
  }
}

{
  "result": true,
  "status": "ok",
  "time": 11.794904818
}

PUT collections/star_charts/points
{
  "points": [
    {
      "id": 1,
      "vector": [0.05, 0.61, 0.76, 0.74],
      "payload": {
        "colony": "Mars"
      }
    },
    {
      "id": 2,
      "vector": [0.19, 0.81, 0.75, 0.11],
      "payload": {
        "colony": "Jupiter"
      }
    },
    {
      "id": 3,
      "vector": [0.36, 0.55, 0.47, 0.94],
      "payload": {
        "colony": "Venus"
      }
    },
    {
      "id": 4,
      "vector": [0.18, 0.01, 0.85, 0.80],
      "payload": {
        "colony": "Moon"
      }
    },
    {
      "id": 5,
      "vector": [0.24, 0.18, 0.22, 0.44],
      "payload": {
        "colony": "Pluto"
      }
    }
  ]
}

{
  "result": {
    "operation_id": 0,
    "status": "acknowledged"
  },
  "status": "ok",
  "time": 0.052241224
}

POST collections/star_charts/points/search
{
  "vector": [0.2, 0.1, 0.9, 0.7],
  "limit": 3,
  "with_payload": true
}

{
  "result": [
    {
      "id": 4,
      "version": 0,
      "score": 1.362,
      "payload": {
        "colony": "Moon"
      }
    },
    {
      "id": 1,
      "version": 0,
      "score": 1.273,
      "payload": {
        "colony": "Mars"
      }
    },
    {
      "id": 3,
      "version": 0,
      "score": 1.208,
      "payload": {
        "colony": "Venus"
      }
    }
  ],
  "status": "ok",
  "time": 0.156498077
}

PUT /collections/midjourney/snapshots/recover
{
  "location": "http://snapshots.qdrant.io/midlib.snapshot"
}

{
  "result": true,
  "status": "ok",
  "time": 40.917713856
}

 curl -X PUT http://localhost:6333/collections/langchaingo-ollama-rag \
  -H 'Content-Type: application/json' \
  --data-raw '{
    "vectors": {
      "size": 768,
      "distance": "Dot"
    }
  }'

{"result":true,"status":"ok","time":130.206360552}