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50 lines
1.7 KiB
Markdown
50 lines
1.7 KiB
Markdown
---
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title: CocoIndex
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---
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# CocoIndex
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[CocoIndex](https://cocoindex.io) is a high performance ETL framework to transform data for AI, with real-time incremental processing.
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Qdrant is available as a native built-in vector database to store and retrieve embeddings.
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Install CocoIndex:
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```bash
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pip install -U cocoindex
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```
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Install Postgres with [Docker Compose](https://docs.docker.com/compose/install/):
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```bash
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docker compose -f <(curl -L https://raw.githubusercontent.com/cocoindex-io/cocoindex/refs/heads/main/dev/postgres.yaml) up -d
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```
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CocoIndex is a stateful ETL framework and only processes data that has changed. It uses Postgres as a metadata store to track the state of the data.
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```python
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import cocoindex
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doc_embeddings.export(
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"doc_embeddings",
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cocoindex.storages.Qdrant(
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collection_name="cocoindex",
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grpc_url="https://xyz-example.cloud-region.cloud-provider.cloud.qdrant.io:6334/",
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api_key="<your-api-key-here>",
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),
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primary_key_fields=["id_field"],
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setup_by_user=True,
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)
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```
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The spec takes the following fields:
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- `collection_name` (type: str, required): The name of the collection to export the data to.
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- `grpc_url` (type: str, optional): The gRPC URL of the Qdrant instance. Defaults to http://localhost:6334/.
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- `api_key` (type: str, optional). API key to authenticate requests with.
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Before exporting, you must create a collection with a vector name that matches the vector field name in CocoIndex, and set `setup_by_user=True` during export.
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## Further Reading
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- [CocoIndex Documentation](https://cocoindex.io/docs/ops/storages#qdrant)
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- [Example Code to build text embeddings with Qdrant](https://github.com/cocoindex-io/cocoindex/tree/main/examples/text_embedding_qdrant)
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