Comments (2)
olirice
commented on May 24, 2024
2
thanks @limcheekin
given that pg_embedding is 1 month old and pre-1.0 it isn't something we want to support in vecs for now
Better performance on large dataset
pgvector merged HNSW support this morning which allows for an apples-to-apples comparison (identical parameterization) with pg_embeddings' on-disk hnsw index
Once that's included in a release, Supabase and RDS are expected to upgrade pretty fast which removes the main reason to consider adding pg_embedding
from vecs.
limcheekin
commented on May 24, 2024
Thanks for the update on "pgvector merged HNSW support this morning" and sharing the chart on performance comparison.
from vecs.
Related Issues (20)
- 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
- FATAL: Max client connections reached HOT 1
- `$in` operator doesn't seem to work HOT 1
- Check for a covering index using pg_catalog params, not ix name/string matching
- Why the ID field can only be the "str" type? HOT 1
- SCRAM exchange: Wrong password HOT 1
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