Capability|Reference|Status|pgContext release contract|Owning reference
Dense vector SQL type, casts, operators, aggregates|pgvector|stable|Dense `vector`, including `vector(n)` typmod metadata and assignment enforcement, is the stable first-release vector surface.|docs/user_guide/api_reference.md
Exact vector search over arrays and registered tables|pgvector,Qdrant|stable|Exact search is the correctness baseline for SQL search, recall checks, ANN, and hybrid retrieval.|docs/user_guide/vector_search.md
Collections over PostgreSQL source tables|Qdrant|stable|Collections reference authoritative PostgreSQL source tables and pgContext catalog metadata.|docs/user_guide/collections.md
Point upsert and delete mappings|Qdrant|stable|Point APIs map source keys to stable pgContext point IDs without owning source rows.|docs/user_guide/collections.md
Filter JSON over ordinary columns and JSONB paths|Qdrant|stable|Registered fields render through typed SQL predicates and SPI parameters.|docs/user_guide/filters.md
Scroll, count, and facet|Qdrant|stable|Stable APIs operate over active table-backed point mappings with shared filter semantics.|docs/user_guide/api_reference.md
Dense plus full-text hybrid query|Qdrant|stable|`pgcontext.query` supports dense vector plus PostgreSQL full-text retrieval with reciprocal rank fusion.|docs/user_guide/hybrid_retrieval.md
Telemetry and operational status functions|Qdrant|stable|Diagnostics expose typed statuses and local counters without vectors, payloads, filters, or literal query text.|docs/user_guide/operations.md
Model versions and embedding migrations|Qdrant|stable|Model-version metadata and migration progress records are stable catalog APIs.|docs/user_guide/collections.md
HNSW access method|pgvector,Qdrant|experimental|`pgcontext_hnsw` serves dense L2, inner-product, cosine, and L1 kNN through metric-bound persisted PostgreSQL pages, retains only published topology during concurrent inserts, exposes cancellation and `hnsw_last_scan_work` counters, has no silent exact fallback, resets on rescan, and does not support mark/restore. Table-driven mutation, VACUUM, REINDEX, exact-oracle, and crash/restart coverage runs for every dense metric; Hamming kNN remains unavailable.|docs/user_guide/indexes.md
Filtered ANN serving|Qdrant|experimental|Filtered table search binds a validated HNSW index and, in one statement snapshot, materializes registered column/JSONB candidates once. Selective masks cross over to exact scoring; broader masks drive one page-backed HNSW traversal using the stored metric. Masked-out nodes remain connectors and sparse masks can expand through ACORN-like second-hop neighbors. Final source joins preserve logical PointId, MVCC/deletion state, ACL/RLS, predicate, and exact-distance rechecks. Selective, broad, empty, stale-stat, deleted, typed JSONB, tenant-isolation, and cancellation cases are covered.|docs/user_guide/vector_search.md
SQL halfvec|pgvector|experimental|`halfvec` text I/O, dimensions, typmods, exact distance helpers, distance operators, explicit rounding numeric-array casts, aggregates, btree ordering opclass, and L2 `pgcontext_hnsw` opclass backed by dense vector storage are SQL-visible.|docs/user_guide/pgvector_migration.md
SQL sparsevec|pgvector,Qdrant|experimental|`sparsevec` text I/O, dimensions, typmods, structured array construction, dense `real[]`/`vector` casts, canonical accessors, exact L2, inner-product, cosine, and L1 helpers/operators, aggregates, btree ordering opclass, L2 `pgcontext_hnsw` opclass backed by dense vector storage, sparse collection metadata, exact array top-k, and named table search are SQL-visible; non-L2 sparse ANN branches remain planned.|docs/user_guide/vector_search.md
SQL bit vectors|pgvector|experimental|`bitvec` text I/O, dimensions, typmods, Hamming/Jaccard distance, distance operators, `boolean[]` casts, PostgreSQL `bit`/`bit varying` casts, pgvector-compatible built-in `bit` Hamming/Jaccard functions and operators, bitwise OR/AND aggregates, btree ordering opclass, and explicit Hamming `pgcontext_hnsw` indexing through `pgcontext.bitvec_hnsw_hamming_ops` are SQL-visible; Jaccard ANN indexing remains planned and default `pgcontext_hnsw` attempts fail with SQLSTATE `42704`.|docs/user_guide/pgvector_migration.md
SQL quantization APIs|pgvector,Qdrant|experimental|Binary, scalar/SQ8-style, product quantize/reconstruct helpers, and exact quantized-candidate rerank are SQL-visible; quantized HNSW index serving remains planned.|docs/user_guide/indexes.md
Named dense vector registration and search|Qdrant|stable|Named dense vector registration, dimensions, metrics, and search-by-name selection are part of the first stable table-backed search surface.|docs/user_guide/collections.md
Per-vector dense index and quantization metadata|Qdrant|experimental|The experimental `collection_vectors` and `configure_vector` functions expose validated metadata containers; HNSW/quantization option semantics and full planner use remain planned.|docs/user_guide/collections.md
Named sparse vectors per collection|Qdrant|experimental|Sparse vector registration, storage/index/status metadata, exact named sparse table search, and exact dense+sparse RRF query fusion are SQL-visible; sparse ANN/index serving remains planned.|docs/user_guide/collections.md
Multi-vector and late-interaction query|Qdrant|experimental|Exact late-interaction MaxSim rerank over explicit arrays, exact table-backed `vector[]` search, typed ANN-planner diagnostics, and HNSW token candidate generation with O(1) declared token-dimension validation, source-table exact MaxSim rerank, deleted-point checks, token-table prerequisite failures, planner preflight, and hydrated budget checks are SQL-visible; full memory/latency release gates remain open.|docs/user_guide/vector_search.md
Recommendation search|Qdrant|stable|Positive/negative point-ID and raw-vector recommendation search uses exact rerank with ACL/RLS and deleted-point checks.|docs/user_guide/vector_search.md
Discovery or explore search|Qdrant|stable|Exact diversity-oriented discover/explore search ranks active rows farthest from visible context examples.|docs/user_guide/vector_search.md
Query constructors|Qdrant|stable|Validated SQL constructors produce JSON plans for nearest, recommend, discover, lookup, prefetch, weighting, thresholds, formulas, and final rerank.|docs/user_guide/vector_search.md
Grouped search|Qdrant|stable|Grouped exact search caps results per registered payload field with deterministic ordering and PostgreSQL ACL/RLS checks.|docs/user_guide/collections.md
Payload mutation helpers|Qdrant|stable|Registered payload fields can be set, deleted, or cleared through documented source-table mutation policy.|docs/user_guide/collections.md
Bulk point backfill APIs|Qdrant|stable|Bulk point upsert, delete, and source-table backfill APIs report bounded per-batch progress diagnostics.|docs/user_guide/collections.md
IVFFlat|pgvector|intentionally different|pgContext does not support IVFFlat in the first production surface; use exact search or HNSW paths, or keep pgvector IVFFlat indexes alongside pgContext during migration.|docs/user_guide/indexes.md
PostgreSQL-native ACL, RLS, transactions, and backups|Postgres improvement|intentionally different|pgContext relies on PostgreSQL source tables, privileges, RLS, transactions, backup, and WAL instead of replacing them.|docs/user_guide/security.md
Rebuildable acceleration artifacts|Postgres improvement|intentionally different|Indexes and segment files are cache artifacts; PostgreSQL tables remain authoritative.|docs/user_guide/storage.md