mirror of
https://github.com/pgvector/pgvector.git
synced 2026-07-07 13:10:56 +08:00
Added support for inline filtering with HNSW [skip ci]
This commit is contained in:
@@ -467,6 +467,12 @@ If filtering by many different values, consider [partitioning](https://www.postg
|
||||
CREATE TABLE items (embedding vector(3), category_id int) PARTITION BY LIST(category_id);
|
||||
```
|
||||
|
||||
Or a composite HNSW index (added in 0.9.0)
|
||||
|
||||
```sql
|
||||
CREATE INDEX ON items USING hnsw (embedding vector_l2_ops, category_id);
|
||||
```
|
||||
|
||||
## Iterative Index Scans
|
||||
|
||||
With approximate indexes, queries with filtering can return less results since filtering is applied *after* the index is scanned. Starting with 0.8.0, you can enable iterative index scans, which will automatically scan more of the index until enough results are found (or it reaches `hnsw.max_scan_tuples` or `ivfflat.max_probes`).
|
||||
@@ -1282,6 +1288,7 @@ Thanks to:
|
||||
- [k-means++: The Advantage of Careful Seeding](https://theory.stanford.edu/~sergei/papers/kMeansPP-soda.pdf)
|
||||
- [Concept Decompositions for Large Sparse Text Data using Clustering](https://www.cs.utexas.edu/users/inderjit/public_papers/concept_mlj.pdf)
|
||||
- [Efficient and Robust Approximate Nearest Neighbor Search using Hierarchical Navigable Small World Graphs](https://arxiv.org/ftp/arxiv/papers/1603/1603.09320.pdf)
|
||||
- [HQANN: Efficient and Robust Similarity Search for Hybrid Queries with Structured and Unstructured Constraints](https://arxiv.org/pdf/2207.07940.pdf)
|
||||
|
||||
## History
|
||||
|
||||
|
||||
Reference in New Issue
Block a user