File size: 1,469 Bytes
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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/
```
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