Comments (1)
vecs
automatically indexes your metadata when you run Collection.create_index(...)
. Internally that metadata is stored as a jsonb column.
If you query based on equality e.g.
docs.query(
...
filters={"user_id": {"$eq": 99}},
)
then the filter will be able to use the index and the performance difference will be negligible when compared to a dedicated column.
In the future we may offer the ability to create dedicated columns to allow cleaner interop with other parts of postgres e.g. e.g. referential integrity with foreign keys, but performance is not likely to be a motivator in real-world workloads
from vecs.
Related Issues (20)
- Upsert returns 500/502/504 if given too many rows/characters ~2000 trips it HOT 1
- version 0.3 breaking upsert vectors HOT 1
- Feature Request: $in operator HOT 1
- Support pg_embedding HOT 2
- Openai Example on documentation doesn't seem to work well. HOT 9
- What is the relationship between SupabaseVectorStore integrated with langchain and this library HOT 1
- Support HNSW indexes
- Clarify Request for Support Policy HOT 2
- Empty Description on PyPI HOT 1
- Option to load private models from Higgingface HOT 2
- Add `ef_construction` and `m` as optional parameters when building HSNW index HOT 3
- Enable schema in client's parameters HOT 6
- Support for Metadata Projection HOT 1
- Explain Query Method HOT 1
- Deleting with filters not working (Package mismatch with source code?) HOT 1
- can't add to an existing table HOT 6
- Remove limit of 1 entry per filter HOT 3
- Adding another column in collections to store raw text HOT 2
- Feature Request: Adding a function to clear the table HOT 1
- str object is not callable HOT 5
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from vecs.