Comments (2)
@kkraune suggested looking into the possibility of using vector-search app as the example in the deploy to cloud documentation.
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This integration https://github.com/vespa-engine/pyvespa/blob/master/vespa/test_integration_vespa_cloud_vector_search.py does exactly this. Resolving.
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Related Issues (20)
- OSError when using multiple async feed_batch() instead of feed_data_point() HOT 2
- Access to user query in reranking phase when using approximate neighbor search HOT 1
- Use rank profile inputs HOT 4
- What about increasing the minimum required Python version? HOT 1
- Add field to schema HOT 2
- No document can be fed because ostensibly there isn't enough disk space HOT 7
- Allow to add configuration to content HOT 2
- Dependency Dashboard
- Types for RankProfile.inputs HOT 1
- VespaSync::delete_all_docs only makes a single delete call HOT 1
- Better feed implementation/interface HOT 1
- Deprecation of PyVespa functionality HOT 1
- Add support for mode streaming with pyvespa HOT 1
- Add support for node resource specification and count
- Remove dependency on pandas
- Struct inconsistencie HOT 3
- Deploy with docker compose and similar setup vespa to other vector dbs HOT 5
- Manually deployed vespa with docker compose Failed to establish a new connection: [Errno 111] HOT 15
- clear explanation where does the docker container stores files HOT 1
- Type hint of RankProfile inputs HOT 3
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