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Copy file name to clipboardExpand all lines: examples/text_embedding_qdrant/README.md
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@@ -19,7 +19,6 @@ We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/c
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### Query
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We use Qdrant client to query the index, and reuse the embedding operation in the indexing flow.
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## Pre-requisites
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-[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.
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docker run -d -p 6334:6334 -p 6333:6333 qdrant/qdrant
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```
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-[Create a collection](https://qdrant.tech/documentation/concepts/vectors/#named-vectors) to export the embeddings to.
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```bash
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curl -X PUT \
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'http://localhost:6333/collections/cocoindex' \
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--header 'Content-Type: application/json' \
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--data-raw '{
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"vectors": {
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"text_embedding": {
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"size": 384,
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"distance": "Cosine"
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}
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}
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}'
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```
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You can view the collections and data with the Qdrant dashboard at <http://localhost:6333/dashboard>.
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## Run
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- Install dependencies:
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cocoindex setup main.py
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```
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It will automatically create a collection in Qdrant.
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You can view the collections and data with the Qdrant dashboard at <http://localhost:6333/dashboard>.
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