Vector extensions Innovation Release
This documentation covers the current Innovation Release of
EDB Postgres AI. See also:
- Hybrid Manager dual release strategy
- Documentation for the current Long-term support release
AI pipelines integrates with two high-performance PostgreSQL extensions for vector search: VectorChord for dense semantic (embedding-based) search, and VectorChord-BM25 for sparse keyword (BM25) search. Both are configured as part of the pipeline's embedding (knowledge base) step and complement the standard pgvector capabilities built into EDB Postgres AI.
| Extension | Search type | Best for |
|---|---|---|
| VectorChord | Dense vector (ANN) | Semantic similarity at billion-scale; lower latency than HNSW/IVFFlat at high dimensions. |
| VectorChord-BM25 | Sparse keyword (BM25) | Exact keyword relevance ranking; complements dense search in hybrid retrieval. |
Together, these extensions form the foundation for hybrid search pipelines — combining semantic meaning (VectorChord) with keyword precision (VectorChord-BM25) to produce higher-quality retrieval results.
For full extension documentation including installation and release notes, see: