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May 31, 2025
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docs(qdrant): no longer needs to manually create Qdrant collection #582

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15 changes: 0 additions & 15 deletions examples/image_search/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -14,28 +14,13 @@ We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/c
- Qdrant for Vector Storage
- FastApi for backend


## Setup
- Make sure Postgres and Qdrant are running
```
docker run -d -p 6334:6334 -p 6333:6333 qdrant/qdrant
export COCOINDEX_DATABASE_URL="postgres://cocoindex:cocoindex@localhost/cocoindex"
```

- Create Qdrant Collection
```
curl -X PUT 'http://localhost:6333/collections/image_search' \
-H 'Content-Type: application/json' \
-d '{
"vectors": {
"embedding": {
"size": 768,
"distance": "Cosine"
}
}
}'
```

## Run
- Install dependencies:
```
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22 changes: 3 additions & 19 deletions examples/text_embedding_qdrant/README.md
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Expand Up @@ -19,7 +19,6 @@ We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/c
### Query
We use Qdrant client to query the index, and reuse the embedding operation in the indexing flow.


## Pre-requisites

- [Install Postgres](https://cocoindex.io/docs/getting_started/installation#-install-postgres) if you don't have one. Although the target store is Qdrant, CocoIndex uses Postgress to track the data lineage for incremental processing.
Expand All @@ -30,24 +29,6 @@ We use Qdrant client to query the index, and reuse the embedding operation in th
docker run -d -p 6334:6334 -p 6333:6333 qdrant/qdrant
```

- [Create a collection](https://qdrant.tech/documentation/concepts/vectors/#named-vectors) to export the embeddings to.

```bash
curl -X PUT \
'http://localhost:6333/collections/cocoindex' \
--header 'Content-Type: application/json' \
--data-raw '{
"vectors": {
"text_embedding": {
"size": 384,
"distance": "Cosine"
}
}
}'
```

You can view the collections and data with the Qdrant dashboard at <http://localhost:6333/dashboard>.

## Run

- Install dependencies:
Expand All @@ -62,6 +43,9 @@ We use Qdrant client to query the index, and reuse the embedding operation in th
cocoindex setup main.py
```

It will automatically create a collection in Qdrant.
You can view the collections and data with the Qdrant dashboard at <http://localhost:6333/dashboard>.

- Update index:

```bash
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