Vector extensions ================= 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. .. csv-table:: :header: Extension,Search type,Best for :widths: 12,12,25 :align: left :class: longtable :ref:`Enable VectorChord (optional)` ,Dense vector (ANN),Semantic similarity at billion-scale; lower latency than HNSW/IVFFlat at high dimensions. :ref:`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: - :ref:`Enable VectorChord (optional) ` - :ref:`VectorChord-BM25 ` .. toctree:: :maxdepth: 3 knowledge-bases--vectorchord--vectorchord-bm25 knowledge-bases--vectorchord--vectorchord-extension