---
license: apache-2.0
---

Prepare Qdrant:

```
mkdir qdrant_storage
mkdir qdrant_snapshots
```

Start Qdrant:

```
docker run -d -p 6333:6333 -p 6334:6334 \
    -v $(pwd)/qdrant_storage:/qdrant/storage:z \
    -v $(pwd)/qdrant_snapshots:/qdrant/snapshots:z \
    qdrant/qdrant
```

Create collection:

```
curl -X PUT 'http://localhost:6333/collections/ktx.finance' \
  -H 'Content-Type: application/json' \
  --data-raw '{
    "vectors": {
      "size": 384,
      "distance": "Cosine",
      "on_disk": true
    }
  }'
```

Query collection:

```
curl 'http://localhost:6333/collections/ktx.finance'
```

Optional: delete collection

```
curl -X DELETE 'http://localhost:6333/collections/ktx.finance'
```

Get embedding model:

```
curl -LO https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF/resolve/main/all-MiniLM-L6-v2-ggml-model-f16.gguf
```

Get the embedding app:

```
curl -LO https://raw.githubusercontent.com/YuanTony/chemistry-assistant/main/rag-embeddings/create_embeddings.wasm
```

Create and save the generated embeddings:

```
wasmedge --dir .:. --nn-preload default:GGML:AUTO:all-MiniLM-L6-v2-ggml-model-f16.gguf create_embeddings.wasm default ktx.finance 384 ktx_docs_20240322.txt
```

Check the results:

```
curl 'http://localhost:6333/collections/ktx.finance'
```

Create snapshot:

```
curl -X POST 'http://localhost:6333/collections/ktx.finance/snapshots'
```

Access the snapshots:

```
ls qdrant_snapshots/ktx.finance/
```