SQL API Contract

SQL API Contract

pgContext uses one contract registry in context-pg to classify SQL-visible objects as stable, experimental, or internal. A compatibility check compares installed pgcontext functions against that registry so new SQL functions cannot be added without a compatibility decision.

Compatibility Policy

The full support, version, upgrade, and deprecation policy lives in support_policy.md. The summary below applies to stable SQL objects listed in this reference.

Stable SQL functions, types, operators, casts, status values, and SQLSTATE categories follow semantic extension-version compatibility after the first production release. Patch releases may add optional fields, functions, enum values, and diagnostics, but must not remove or repurpose stable SQL objects.

Breaking changes require a new extension version, upgrade notes, and a named migration path. A change is breaking when it removes a stable SQL object, changes a stable function signature or return column, changes the meaning of a stable status value, changes a documented SQLSTATE for the same failure class, or makes previously valid stable input fail without an explicit migration.

Deprecated stable APIs remain callable for at least one minor release after the replacement is documented. Deprecation notices belong in the user guide, release notes, and upgrade guide; clients should not need to infer deprecation from free-form PostgreSQL messages.

Diagnostic rows and telemetry use stable column names, typed status values, and numeric counters. Human-readable messages may become more specific, so clients must branch on SQLSTATEs and documented status fields instead of parsing text. Experimental and internal objects are excluded from this compatibility promise.

Stable User APIs

Collection and registration:

pgcontext.create_collection(collection_name text)
pgcontext.create_collection(collection_name text, table_name text)
pgcontext.create_collection_alias(alias_name text, target_collection_name text)
pgcontext.collection_aliases()
pgcontext.collection_info(collection_name text)
pgcontext.collection_limits(collection_name text)
pgcontext.configure_collection_limits(collection_name text, strict_mode boolean, max_dimensions integer, max_vectors integer, max_points bigint, max_filter_nodes integer, max_search_limit integer, max_candidate_budget integer, query_timeout_ms integer, max_index_memory_bytes bigint)
pgcontext.drop_collection(collection_name text)
pgcontext.drop_collection_alias(alias_name text)
pgcontext.register_vector(collection_name text, vector_name text, vector_column text, dimensions integer, metric text)
pgcontext.collection_vectors(collection_name text)
pgcontext.configure_vector(collection_name text, vector_name text, hnsw_options jsonb, quantization_options jsonb, status text)
pgcontext.register_filter_column(collection_name text, filter_key text, column_name text)
pgcontext.register_jsonb_path(collection_name text, filter_key text, column_name text, path text[])
pgcontext.upsert_points(collection_name text, source_keys text[])
pgcontext.delete_points(collection_name text, source_keys text[])
pgcontext.bulk_upsert_points(collection_name text, source_keys text[], batch_size integer)
pgcontext.bulk_delete_points(collection_name text, source_keys text[], batch_size integer)
pgcontext.backfill_points(collection_name text, batch_size integer)
pgcontext.set_payload(collection_name text, source_keys text[], payload jsonb)
pgcontext.delete_payload(collection_name text, source_keys text[], payload_keys text[])
pgcontext.clear_payload(collection_name text, source_keys text[])

Search and query:

pgcontext.search(collection text, vector vector, limit integer)
pgcontext.search(collection text, vector_name text, vector vector, limit integer)
pgcontext.search(collection text, vector vector, filter text, limit integer)
pgcontext.search(collection text, vector_name text, vector vector, filter text, limit integer)
pgcontext.search(collection text, vector vector, candidate_point_ids bigint[], limit integer)
pgcontext.search(collection text, vector_name text, vector vector, candidate_point_ids bigint[], limit integer)
pgcontext.search(collection text, vector vector, filter text, candidate_point_ids bigint[], limit integer)
pgcontext.search(collection text, vector_name text, vector vector, filter text, candidate_point_ids bigint[], limit integer)
pgcontext.search(query vector, point_ids bigint[], vectors vector[], metric text, limit integer)
pgcontext.recommend(collection text, positive_point_ids bigint[], negative_point_ids bigint[], limit integer)
pgcontext.recommend(collection text, positive_vectors vector[], negative_vectors vector[], limit integer)
pgcontext.discover(collection text, context_point_ids bigint[], limit integer)
pgcontext.explore(collection text, context_point_ids bigint[], limit integer)
pgcontext.query(collection text, vector vector, text_query text, lexical_source text, limit integer)
pgcontext.query_nearest(vector vector, limit integer)
pgcontext.query_nearest(vector_name text, vector vector, filter jsonb, limit integer)
pgcontext.query_sparse_nearest(vector_name text, vector sparsevec, filter jsonb, limit integer)
pgcontext.query_sparse_nearest(vector_name text, vector sparsevec, limit integer)
pgcontext.query_lexical(source text, query jsonb, filter jsonb, limit integer)
pgcontext.query_multi_model(collection text, branches jsonb, filter jsonb, limit integer, rrf_k integer, unique_candidate_budget integer, require_all_profiles boolean) returns a bounded, version-safe JSON report for rank-only weighted-RRF fusion across immutable profiles. See Multi-model retrieval.
pgcontext.query_late_interaction(query_vectors vector[], candidates_per_query integer, limit integer)
pgcontext.query_recommend(positive_point_ids bigint[], negative_point_ids bigint[], limit integer)
pgcontext.query_discover(context_point_ids bigint[], limit integer)
pgcontext.query_lookup(point_ids bigint[])
pgcontext.query_prefetch(branches jsonb[])
pgcontext.query_prefetch(branches jsonb[], fusion text, rank_constant integer)
pgcontext.query_weight(branch jsonb, weight double precision)
pgcontext.query_score_threshold(branch jsonb, min_score double precision, max_score double precision)
pgcontext.query_formula(branch jsonb, formula text)
pgcontext.query_rerank(branch jsonb, limit integer)
pgcontext.query_external_rerank(branch jsonb, model_revision bigint, limit integer)
pgcontext.query_topology_expand(branch jsonb, max_depth integer, limit integer)
pgcontext.execute_query(collection text, plan jsonb)
pgcontext.explain(collection text, lexical_source text)
pgcontext.scroll(collection text, cursor text, limit integer)
pgcontext.count(collection text)
pgcontext.count(collection text, filter text)
pgcontext.facet(collection text, field text, filter text, limit integer)
pgcontext.grouped_search(collection text, vector vector, group_by text, group_limit integer, limit integer)
pgcontext.grouped_search(collection text, vector_name text, vector vector, group_by text, group_limit integer, limit integer)

Every composite constructor parses and validates the complete child plan before returning JSON. Formula constructors also compile the bounded expression, so an unknown child kind or non-executable formula fails at construction instead of surviving until execute_query.

pgcontext.search is the stable single-vector retrieval surface. Use it for exact or ANN-style nearest-neighbor retrieval over one dense vector branch, including filter-first search, candidate recheck, and grouped exact search by a registered payload field. pgcontext.query is the multi-stage retrieval pipeline: its convenience overloads build the same typed executor plan used by execute_query, including registered dense + full-text and dense + sparse RRF fusion. Additional ANN sparse branch planners and broader multi-branch planning remain deferred instead of being hidden behind search.

Dense vector compatibility:

SQL type vector, including vector(n) catalog metadata for dimensions 1..=16000; assignments with a different dimension fail with SQLSTATE 22023
pgcontext.l2_distance(left vector, right vector)
pgcontext.inner_product(left vector, right vector)
pgcontext.negative_inner_product(left vector, right vector)
pgcontext.cosine_distance(left vector, right vector)
pgcontext.l1_distance(left vector, right vector)
pgcontext.vector_dims(vector vector)
Operators <->, <#>, <=>, <+>, <, <=, =, <>, >=, and >
B-tree operator class pgcontext.vector_ops
Aggregates pgcontext.sum(vector) and pgcontext.avg(vector)
An assignment cast from real[] to vector; explicit-only casts from integer[] and double precision[] reject elements that are not exactly representable as real; and an assignment cast from vector to real[]

Provider-native integer and packed-binary source contracts are experimental:

SQL types int8vec(n) and uint8vec(n) accept 1..=16000 signed or unsigned 8-bit coordinates, expose PostgreSQL binary send/receive hooks, and enforce typmods on assignment. bitvec exposes the same binary-protocol contract for packed provider-native profiles.
Constructors pgcontext.int8vec(text) and pgcontext.uint8vec(text) and dimension helpers pgcontext.int8vec_dims(int8vec) and pgcontext.uint8vec_dims(uint8vec).
Exact L2, raw inner-product, negative-inner-product, cosine, and L1 functions and the <->, <#>, <=>, and <+> operators. Raw inner product is exposed as int8vec_inner_product and uint8vec_inner_product; negative inner product remains the KNN ordering support function. Integer accumulation remains exact in bigint before the final double precision result conversion.
Deterministic comparison operators and default B-tree opclasses pgcontext.int8vec_ops and pgcontext.uint8vec_ops.
pgcontext.sum(int8vec|uint8vec) returns bigint[] and pgcontext.avg(int8vec|uint8vec) returns double precision[]; both use a checked bigint[] transition state.
Explicit checked casts from smallint[] and integer[], casts back to smallint[], lossless assignment casts to dense vector, and explicit dense-to-integer casts that reject fractional or out-of-range coordinates. There is no implicit signed/unsigned semantic cast.
pgcontext.int8vec_from_profile(collection, profile_name, values smallint[]) and pgcontext.uint8vec_from_profile(...) resolve the authoritative representation and dimension from a registered profile before constructing a value.
pgcontext.bitvec_from_provider_bytes(collection text, profile_name text, payload bytea) resolves logical dimensions and both byte/bit orders from the registered profile, requires the exact byte length, and rejects nonzero padding bits. Callers cannot override the stored layout.
pgcontext.vector_prefix(vector vector, dimensions integer) returns the leading dimensions coordinates of a dense vector. It is immutable and parallel-safe. pgContext’s own prefix probe binds the width as a parameter, so an expression index over a literal width will not be matched by it. It never rewrites the stored value: adaptive-dimension candidate generation projects a prefix at query time while the row keeps its full vector. Raises invalid_parameter_value outside 1..=vector_dims(vector). See Adaptive-dimension retrieval.
pgcontext.register_embedding_profile(collection text, profile_name text, source_column text, hnsw_index text, profile jsonb, lifecycle text DEFAULT 'active') stores an immutable provider contract. hnsw_index must be schema-qualified and must be a live, simple pgcontext_hnsw index over source_column with the exact representation, typmod, metric, and pgContext opclass declared by the profile. The JSON object must contain exactly representation, dimensions, normalization, metric, provider, model, revision, input_template, output_template, bit_order, byte_order, scale, zero_point, and configuration_hash, and may additionally carry matryoshka_prefixes or the paired source_version_column and embedding_version_column. Version bindings must name distinct bigint columns and are required by query_multi_model. Binary profiles require Hamming or Jaccard plus both orders; integer profiles may declare a positive scale and in-range zero point. The configuration hash is 16 lowercase hexadecimal digits and cannot be zero. matryoshka_prefixes is an optional array of 1..=8 strictly ascending positive prefix dimensions, each strictly below dimensions. It declares that the model certifies its leading coordinates as usable lower-dimension embeddings, and is accepted only for a dense or half representation under the l2, inner_product, or cosine metric — the representations and metrics whose coordinates stay independently interpretable at a cut point. pgContext never truncates or rewrites the stored vector; a declared prefix only makes candidate generation cheaper, and final ranking always uses the full authoritative dimensions.
pgcontext.set_embedding_profile_lifecycle(collection text, profile_name text, lifecycle text) moves a registered profile to a new lifecycle state. The transition is validated before any catalog write: shadow promotes to active, active begins a cutover into draining, draining completes into retired or rolls back to active, and a failed profile must re-backfill through shadow before it can serve again. retired is terminal and no state may be re-declared as itself. Requires collection ownership.
pgcontext.embedding_profile_coverage(collection text) reports, per profile, its lifecycle, whether it serves queries, its source column, current covered points, stale points whose embedding version differs from the source version, and active mapped points. All three counts join the authoritative source and therefore include only rows visible under the invoker’s current ACL and RLS policy.
pgcontext.embedding_profiles() lists source-column and HNSW bindings for collections owned by the session role. pgcontext.embedding_profile_explain(collection text, profile_name text) reports the normalized profile, durable source/index names, live binding validity, source authority, exact score representation, compatible HNSW opclass, and authoritative final-score rule. Both functions are security-definer functions with a pinned search path and SESSION_USER ownership filtering.

Operations, diagnostics, and telemetry:

pgcontext.index_status(index_name text)
pgcontext.index_diagnostics(index_name text)
pgcontext.estimate_index_memory(index_name text)
pgcontext.index_advisor(collection text)
pgcontext.optimization_status(collection text)
pgcontext.vacuum_advice(index_name text)
pgcontext.hnsw_serving_stats() — this backend’s packed-generation serving counters: pack_builds, pack_reuses, last_pack_bytes, last_pack_millis, total_pack_millis, shared_attaches, shared_publishes, shared_publish_skips, mapped_attaches, mapped_publishes, mapped_publish_skips, page_native_fallbacks, delta_segment_records, delta_segment_scans. Local pack/reuse counters describe the calling backend only; shared_* counters describe this backend’s activity against the cross-backend shared registry (see pgcontext.hnsw_shared_serving), while mapped_* counters describe immutable file-generation serving (see pgcontext.hnsw_mmap_serving); page_native_fallbacks counts queries served from unpacked directory reads because no pack was available and pgcontext.hnsw_pack_on_first_use was off; delta_segment_records and delta_segment_scans describe the persisted segmented-write delta region (see pgcontext.hnsw_delta_segment_limit) — rows absorbed without a graph splice and scans that merged the region with base-graph results.
pgcontext.hnsw_build_stats() — phase timing of this backend’s most recent HNSW bulk build: last_build_tuples, graph_millis (heap scan plus in-memory graph construction), write_millis (snapshot extraction, page writes, Generic-WAL emission). All zeros before the first build.
pgcontext.hnsw_segment_stats(index regclass) — segmented publication, compaction, and codec diagnostics. Codec columns report codec, the build-bound codec-configuration identity as codec_revision (an exact unsigned decimal string), fixed codec_code_width, current candidate_budget, mandatory exact_source_rerank, and codec_serving_capability. A null revision means the index is full precision; a quantized index binds its configuration revision even when it is empty.
pgcontext.recall_check(exact_point_ids bigint[], candidate_point_ids bigint[], min_recall double precision)
pgcontext.telemetry()
pgcontext.record_query_stat(collection text, cohort text, query_kind text, result_count bigint, candidate_count bigint, latency_ms double precision)
pgcontext.record_query_stat(collection text, cohort text, query_kind text, result_count bigint, candidates_considered bigint, rows_rechecked bigint, rows_pruned bigint, recall_threshold double precision, recall_achieved double precision, latency_ms double precision, lifecycle_state pgcontext."QueryLifecycleState")
pgcontext.query_cohort_stats()
pgcontext.query_execution_stats() — membership-filtered automatic rollups by actual strategy, completion, latency bucket, lifecycle state, and bounded executor work counters. Adaptive exact leaves also expose nullable adaptive_prefix_dimensions and adaptive_termination grouping columns; total_expansions includes every prefix-widening step.
pgcontext.query_telemetry_queue_stats() — pg_monitor-restricted health counters for the bounded asynchronous delivery queue Stable status values use the SQL enum labels below. String inputs that update catalog state, such as pgcontext.update_embedding_migration(..., status text), may accept lowercase command strings, but result rows expose the typed enum labels.
pgcontext."EmbeddingMigrationStatus": Planned, Running, Completed, Failed
pgcontext."IndexAdvisorRecommendation": NoAction, CreateBtreeIndex, CreateGinIndex, AnalyzeTable, AvoidCandidateMaterialization, TuneHnswSettings
pgcontext."IndexDiagnosticStatus": Ready, IndexNotReady, IndexCorrupt, UnsupportedAccessMethod
pgcontext."IndexLifecycleStatus": Ready, Building, Invalid
pgcontext."IndexMemoryEstimateStatus": Projected, UnsupportedAccessMethod, UnavailableStatistics
pgcontext."OptimizationStatus": Indexed, ExactOnly, MissingArtifacts, StaleCatalog
pgcontext."QueryCohortStatus": Observed
pgcontext."QueryExplainStatus": Ready, Fallback, Policy
pgcontext."QueryLatencyBucket": Lt1Ms, Lt10Ms, Lt100Ms, Lt1S, Gte1S, Unspecified
pgcontext."QueryLifecycleState": Unspecified, Exact, Indexed, Fallback, IndexNotReady, IndexCorrupt, ArtifactMissing
pgcontext."RecallCheckStatus": Passing, Failing, EmptyExact
pgcontext."TelemetryStatus": Active, Empty, MissingArtifacts, StaleCatalog
pgcontext."VacuumAdviceStatus": Healthy, VacuumRecommended, AnalyzeRecommended, UnsupportedAccessMethod

Experimental build-job metadata uses the pgcontext."BuildJobStatus" labels Planned, Running, CancelRequested, Cancelled, Completed, Failed, and Abandoned. These rows track backend-local progress and operator intent for future artifact builds; they do not make CREATE INDEX resumable and they do not make a partially built artifact query-safe.

GUCs

These HNSW tuning GUCs are SQL-visible for the experimental HNSW serving path. They are outside the first stable SQL compatibility promise until HNSW serving graduates from the experimental parity row.

pgcontext.hnsw_m
pgcontext.hnsw_ef_construction
pgcontext.hnsw_ef_search
pgcontext.hnsw_candidate_budget
pgcontext.hnsw_iterative_expansion_limit
pgcontext.hnsw_recall_threshold
pgcontext.pgvector_compat_warnings (coexist-mode advisory notice; see pgvector_coexist.md)

Experimental APIs

Automatic document chunking:

pgcontext.create_document_chunk_projection(table_name text) creates the caller-owned projection table with the frozen chunk/citation contract.
pgcontext.register_chunking_profile(profile_name text, parser text, target_tokens integer, max_tokens integer, min_tokens integer, overlap_tokens integer, max_document_bytes bigint, include_structure_context boolean) registers an immutable parser/tokenizer profile. overlap_tokens must be no greater than 64 and must remain below target_tokens.
pgcontext.register_document_source(collection text, source_name text, text_column text, source_version_column text, projection_table text, profile_name text) binds authoritative source and projection relations.
pgcontext.install_document_chunk_trigger(collection text, source_name text) installs the transactional enqueue/invalidation trigger.
pgcontext.enqueue_document_chunking(collection text, source_name text, source_keys text[]) explicitly enqueues at most 256 visible source rows.
pgcontext.prepare_chunking_profile_alias(alias_name text, profile_name text) selects a non-serving shadow profile; replacing a shadow lazily supersedes its unfinished jobs at the next fenced lifecycle boundary.
pgcontext.enqueue_document_chunking_profile(collection text, source_name text, profile_name text, source_keys text[]) builds the prepared shadow for selected keys without changing current reads.
pgcontext.promote_chunking_profile_alias(alias_name text, profile_name text) atomically makes the prepared shadow current and retains the prior profile as a draining fallback.
pgcontext.rollback_chunking_profile_alias(alias_name text) atomically restores the most recently retained predecessor.
pgcontext.drain_chunking_profile_alias(alias_name text, profile_name text) explicitly removes one predecessor from fallback eligibility. At most eight predecessors may remain retained per alias.
pgcontext.claim_document_chunk_jobs(limit integer, lease_millis integer, worker_id text) returns fenced jobs and bounded worker envelopes.
pgcontext.stage_document_chunks(job_id bigint, lease_token bigint, response jsonb) and pgcontext.publish_document_chunk_generation(job_id bigint, lease_token bigint) validate and atomically publish complete output.
pgcontext.heartbeat_document_chunk_job, pgcontext.checkpoint_document_chunk_job, pgcontext.fail_document_chunk_job, pgcontext.cancel_document_chunk_job, and pgcontext.retry_document_chunk_job manage bounded leased work.
pgcontext.invalidate_document_chunks(collection text, source_name text, source_keys text[]) retires current aliases without deleting authoritative source rows.
pgcontext.rebuild_document_chunk_job(job_id bigint) is an owner-authorized recovery operation for a ready or retired derived generation whose user-owned projection failed its stored digest. It removes the current alias, clears that generation’s projection/embedding output, and requeues the same source identity for canonical publication.
pgcontext.rollback_document_chunk_generation(collection text, source_name text, source_key text, generation_id bigint) restores a prior complete published generation only when its source identity still matches the current invoker-visible row.
pgcontext.document_chunking_progress(collection text, source_name text) returns content-free lifecycle counters.
pgcontext.current_document_chunks(collection text, source_name text, source_keys text[]) rehydrates the source and returns only current complete chunks whose version and digest still match under current ACL/RLS.

The PG17 and PG18 lifecycle suites pass. Stable promotion remains blocked by the frozen PG17 one-million-row source-cardinality lane’s bounded publication workload throughput floor. See Automatic document chunking for the worker protocol, bounds, citation contract, and logical-restore behavior.

Exact-first readiness:

pgcontext.inspect_exact_first_source(source_table text, options jsonb) returns bounded type-support diagnostics without mutation.
pgcontext.register_exact_first(collection text, source_table text, specification jsonb, apply_policy text) registers an idempotent dense exact path and requires at least one complete public dense adapter.
pgcontext.exact_first_search(collection text, binding text, vector vector, limit integer) executes a complete invoker-authoritative exact scan with native real scores and deterministic source-key ties.
pgcontext.exact_first_advisor(collection text, objectives jsonb) stores an immutable exact-only, HNSW, or IVFFlat recommendation and reviewed DDL.
pgcontext.apply_exact_first_plan(collection text, plan_revision bigint, policy text) leaves the plan frozen, enqueues it, or applies it in the foreground under an explicit policy.
pgcontext.claim_exact_first_build, heartbeat_exact_first_build, publish_exact_first_build, fail_exact_first_build, cancel_exact_first_build, and retry_exact_first_build form the fenced top-level concurrent-index controller protocol.
pgcontext.exact_first_readiness(collection text) and pgcontext.exact_first_progress(collection text) return bounded, content-free state and progress.

These APIs are Experimental until the unchanged P14 ten-million-row contract passes on PostgreSQL 17 and 18. See Exact-first readiness.

Provider-neutral semantic reranking:

pgcontext.register_semantic_rerank_source(collection text, source_name text, text_column text, source_version_column text) registers the authoritative text and version binding.
pgcontext.prepare_semantic_rerank(collection text, source_name text, query text, candidates jsonb, model text, model_revision bigint, ttl_millis bigint, failure_policy text, filter jsonb, allow_partial boolean) returns one bounded, authorized provider-neutral envelope.
pgcontext.finalize_semantic_rerank(request_id bigint, response jsonb, failure_reason text) validates untrusted scores and authoritatively rechecks every final row.
pgcontext.cleanup_semantic_rerank_requests(limit integer) removes bounded expired or finalized request state owned by the session role.

The PostgreSQL 17 and 18 local certification lanes pass. These APIs remain Experimental until retained hosted worker build and smoke evidence covers Darwin and Linux on arm64 and x86_64. See Semantic reranking.

Profile-backed migration tracking:

pgcontext.create_embedding_migration(collection text, source_profile text, target_profile text, total_points bigint)
pgcontext.update_embedding_migration(migration_id bigint, processed_points bigint, status text)
pgcontext.embedding_migrations()

The former register_model_version, model_versions, and _model_versions surfaces were removed. There is no compatibility overload; migrations now reference immutable profiles.

pgvector coexist-mode tooling (see pgvector_coexist.md for semantics and caveats):

pgcontext.migration_report() returns one row per pgvector-typed column: (schema_name text, table_name text, column_name text, type_name text, dimensions int, pgvector_indexes text[], pgcontext_indexes text[], conversion_supported bool, blockers text[], suggested_command text). Read-only. The blockers are a fail-closed inventory for ownership conversion; array, generated, partitioned, dependent-view, defaulted, and complex-index shapes are reported rather than guessed.
pgcontext.adopt_pgvector(target regclass DEFAULT NULL, dry_run bool DEFAULT true, drop_old bool DEFAULT false) returns (index_name text, action text, command text, executed bool) rows; migrates supported pgvector HNSW indexes to pgcontext_hnsw through the main-extension binding. IVFFlat adoption is intentionally directed to the ownership-conversion workflow so it is rebuilt as pgcontext_ivfflat, never silently remapped to HNSW. Dry-run is the default. Only extension-owned, usable, plain single-column indexes are accepted. HNSW build options and tablespace are preserved. If drop_old is requested, the replacement must first pass an exact-oracle recall gate; a failure aborts the transaction without dropping the source index.
pgcontext.compare_indexes(table_name text, column_name text, queries int DEFAULT 20) returns one row per ANN index on the column: (index_name text, access_method text, operator text, p50_ms float8, p95_ms float8, recall_at_10 float8), measured with sampled stored vectors against an exact same-operator oracle; indexes the planner never chose report NULL measurements. Read-only.
pgcontext.pgvector_compatibility_inventory() publishes the executable type, operator, function, access-method, and setting translation matrix.
pgcontext.enable_pgvector_binding() installs owner-only, idempotent casts, support functions, and HNSW opclasses for pgvector 0.8.x in public. They are standalone objects owned by the exact pgcontext extension-owner role, so pg_dump emits them and their dependencies. disable_pgvector_binding() removes them; live dependent indexes block removal with SQLSTATE 2BP01.
pgcontext.enable_pgvector_name_facade() installs unqualified hnsw and ivfflat access-method names plus pgvector-spelled opclasses for canonical pgContext types only when neither name is owned. These are also standalone, dump-visible objects owned by the exact superuser extension-owner role. The inverse function fails closed with SQLSTATE 2BP01 while facade indexes exist.
pgcontext.start_pgvector_ownership_conversion(target regclass, column_name text, mode text DEFAULT 'fast', metric text DEFAULT 'cosine', application_uses_column_lists bool DEFAULT false, application_dependencies_reviewed bool DEFAULT false) starts a persisted conversion for a table-owner role. mode is fast or restricted_online. Online mode immediately adds a canonical shadow column and synchronization trigger, so its explicit column-list attestation is mandatory. Every mode requires the application-dependency attestation because PostgreSQL cannot discover relation references hidden in application SQL or string-bodied SQL/PLpgSQL functions. The caller must have CREATE on the target schema (and any preserved nondefault index tablespace) whenever the conversion builds replacement indexes.
pgcontext.run_pgvector_ownership_conversion(conversion_id bigint, batch_size int DEFAULT 1000, sessions_drained bool DEFAULT false) performs one bounded step. Fast mode requires the session-drain attestation and completes atomically. Online mode backfills at most batch_size mismatches; once backfill is complete, next_command contains a CREATE INDEX CONCURRENTLY command that must be run as its own top-level statement. Call run_pgvector_ownership_conversion again to certify that index and advance the job to ready.
pgcontext.cutover_pgvector_ownership_conversion(conversion_id bigint, sessions_drained bool DEFAULT false) performs the locked online name swap after exact row validation and requires all application sessions to have been drained/reprepared. pgcontext.finalize_pgvector_ownership_conversion then irreversibly drops the synchronized pgvector rollback column, while pgcontext.rollback_pgvector_ownership_conversion restores the original column and indexes before finalization.
pgcontext.pgvector_ownership_conversions() lists only jobs owned by a role of which SESSION_USER is a member. The private job catalog is not dumped; in-flight relation/type OIDs are intentionally never resumed after restore.

Ownership conversion is deliberately restricted to permanent ordinary heap tables and directly pgvector-owned vector, halfvec, or supported sparsevec columns. It refuses unsupported defaults, generated/dependent expressions, column ACLs, views, catalog-discoverable function dependencies, constraints, RLS policies, user triggers, publications, extended statistics, partitions/inheritance, replica identity, composite dependencies, column comments/nondefault storage/statistics, and complex or counterfeit indexes. Source HNSW indexes with per-index options are refused because the current pgcontext_hnsw AM cannot represent them; IVFFlat lists is preserved when rebuilding on pgcontext_ivfflat. Invalid indexes and indexes with comments are refused rather than silently normalizing or losing metadata. Fast conversion uses a binary metadata type change and rebuilds certified source ANN indexes on the matching native access method; for a dimensioned source it preserves the dimension invariant with a validated CHECK constraint so the heap is not rewritten. Restricted-online conversion supports at most one source ANN index and requires its metric to match the requested replacement.

Index maintenance:

pgcontext.compact(index regclass) returns one row, (live_rows bigint, base_records_read bigint, delta_records_drained bigint). Rebuilds a pgcontext_hnsw index’s graph from the index’s own pages — never the heap — and reopens an empty delta segment, restoring the fast insert path once pgcontext.hnsw_delta_segment_limit writes have accumulated. Results are unchanged by a compaction.

It publishes the rebuilt graph with a single atomic metapage update, so concurrent readers see either the old graph or the new one, never a partial result. Two limits: the superseded pages stay in place, so compaction restores write throughput without shrinking the relation on disk (use REINDEX for that), and it can only drop deleted rows that a preceding VACUUM has tombstoned.

It takes ShareUpdateExclusiveLock on the parent table for the rest of the transaction, which is the lock VACUUM holds, so the two wait for each other rather than interleaving. Ordinary INSERT/UPDATE/DELETE are not blocked. If it nonetheless observes a concurrent index mutation it raises serialization_failure and changes nothing.

Normal inserts rotate a full active delta into an immutable graph segment and compact only one adjacent pair when the 16-entry directory is full. Call this full compaction explicitly to collapse all segments or reclaim logical mutation history; use REINDEX when physical file shrinkage is required. See index configuration.

pgcontext_hnsw and experimental pgcontext_ivfflat expose metric-bound dense-vector operator classes:

pgcontext.vector_hnsw_ops for L2 (the default)
pgcontext.vector_hnsw_ip_ops for inner-product ordering
pgcontext.vector_hnsw_cosine_ops for cosine distance
pgcontext.vector_hnsw_l1_ops for L1 distance
pgcontext.vector_ivfflat_ops for L2 (the default)
pgcontext.vector_ivfflat_ip_ops for inner-product ordering
pgcontext.vector_ivfflat_cosine_ops for cosine distance
pgcontext.vector_ivfflat_l1_ops for L1 distance

Equivalent IVFFlat classes exist for halfvec, int8vec, and uint8vec; bitvec_ivfflat_hamming_ops and bitvec_ivfflat_jaccard_ops are explicit. See Indexes for reloptions and exact-source semantics. These classes and the IVFFlat v4 format are not yet covered by the stable compatibility promise.

pgcontext.ivfflat_index_info(index regclass) -> jsonb verifies the complete published IVF generation and reports list/page/codec/build-worker diagnostics.
pgcontext.ivfflat_last_scan_work() returns requested probes, visited lists and postings, delta records, candidates, exact reranks, widening rounds, completion reason, codec, and generation for the backend’s latest native IVF scan.
pgcontext.compact_ivfflat(index regclass) -> jsonb synchronously retrains, folds foreground deltas, atomically publishes a verified generation, and reports source/base rows, the prior delta, codec, and reclaimed pages.
pgcontext.enqueue_ivfflat_compaction(collection text, index regclass) submits the same work to the durable supervised build lifecycle. Registered indexes also enqueue debt automatically at 10,000 foreground records.

Quantization helpers are SQL-visible for inspecting and testing encoded representations:

pgcontext.binary_quantize(vector) returns a bitvec sign code.
pgcontext.scalar_quantize(vector, min real, max real, levels integer) returns scalar/SQ8-style byte codes.

Backend-local build metadata is experimental and owner-scoped:

pgcontext.start_build_job(collection text, artifact_kind text, artifact_name text, target_name text, total_units bigint) creates a running job row for one PostgreSQL backend. If a previous running or cancel_requested row for the same target belongs to a backend that no longer appears in pg_stat_activity, it is first recorded as abandoned so a replacement build can start without manual catalog repair.
pgcontext.build_jobs(collection text) lists visible build jobs for a collection.
pgcontext.update_build_job(build_job_id bigint, processed_units bigint, status text, error_message text default null) records progress or a terminal status from the backend that owns the running job.
pgcontext.request_build_cancel(build_job_id bigint) records cooperative cancellation intent.
pgcontext.retry_build_job(build_job_id bigint) claims a failed, cancelled, or abandoned job for the current backend while preserving recorded progress.
pgcontext.run_build_job(build_job_id bigint, units_per_step bigint default 1) executes experimental segment and mmap jobs synchronously in the current backend, advances bounded progress, honors already-visible cancellation, and records a terminal status.
pgcontext.encode_artifact_segment(kind text, payload bytea) encodes an experimental rebuildable artifact byte stream with a versioned segment header and checksum. The currently exposed kind is hnsw_graph.
pgcontext.validate_artifact_segment(segment bytea) validates an encoded segment through the mmap-safe loader and returns kind, payload length, and checksum metadata without marking the artifact query-safe.
pgcontext.validate_hnsw_graph_artifact(segment bytea) validates both the outer segment header and the portable HNSW graph payload format, returning record count, dimensions, and base-neighbor count. Malformed graph payloads raise data-corruption SQLSTATE instead of being accepted as serving input.
pgcontext.publish_artifact_segment(build_job_id bigint, segment bytea) validates encoded segment bytes for a completed visible segment or mmap build job and records manifest metadata only.
pgcontext.publish_artifact_segment_file(build_job_id bigint, segment bytea) validates encoded segment bytes, atomically materializes them under a generated PostgreSQL data-directory-relative pgcontext_artifacts/... path, reloads the file through the validator, and records the generated relative path with file_materialized lifecycle state.
pgcontext.artifact_segments(collection text) lists visible validated artifact manifests for a collection, including generated relative paths for file-materialized artifacts.
pgcontext.artifact_segment_memory(collection text) reports per-artifact payload bytes, segment header bytes, total mapped bytes, lifecycle state, and whether the artifact has a materialized file path.
pgcontext.artifact_segment_serving_readiness(collection text, max_mapped_bytes bigint) reloads visible artifact files and reports whether each artifact is safe for the mmap serving path under the supplied memory budget. It never serves vectors; it gates on mmap kind, file-materialized lifecycle, root-confined paths, segment checksum/catalog metadata agreement, and mapped bytes within budget.
pgcontext.artifact_segment_mmap_payload(collection text, artifact_name text, max_mapped_bytes bigint) reloads one visible mmap artifact through the same readiness gate and returns its validated payload bytes only when the file is serving-ready under the supplied memory budget. Missing, corrupt, drifted, path-escaped, metadata-only, non-mmap, or over-budget artifacts fail closed with a prerequisite-state error instead of returning bytes. This is an experimental loader primitive, not a stable vector search API or durable HNSW artifact payload contract.
pgcontext.search_mmap_hnsw_artifact(collection text, artifact_name text, vector vector, max_mapped_bytes bigint, candidate_limit integer, limit integer) is an experimental mmap serving slice: it opens a serving-ready HNSW graph artifact through a validated read-only OS mapping, traverses persisted graph links, merges source points added after the generation high-water mark, and returns source-table rechecked rows scored from the authoritative registered vector column. Corrupt artifact payloads raise data-corruption SQLSTATE; not-ready artifacts fail closed with prerequisite-state errors.
pgcontext.artifact_segment_diagnostics(collection text) reloads each visible file-materialized artifact through the segment loader, rejects catalog paths outside pgcontext_artifacts/..., and compares file metadata with the catalog. The constrained status text is one of ready, metadata_only, artifact_missing, checksum_mismatch, artifact_corrupt, metadata_mismatch, or path_rejected. The repair_advice column is deterministic: ready means no action, metadata-only, retired, or pathless manifests are not cleanup candidates, path_rejected means fix or remove the invalid catalog path before cleanup, and missing, corrupt, checksum-drifted, or metadata-drifted artifacts should be retired or rebuilt after investigation. The cleanup_eligible column is true only for root-confined materialized artifact paths that are eligible for retire_artifact_segment cleanup.
pgcontext.retire_artifact_segment(artifact_id bigint) marks the visible manifest retired and then attempts to remove its generated, root-confined materialized artifact file. Missing files are tolerated; catalog paths outside pgcontext_artifacts/... are rejected without touching the filesystem.
pgcontext.cleanup_artifact_segments(collection text, dry_run boolean) reports or retires manifest-known materialized files that are missing, corrupt, checksum-drifted, or metadata-drifted. It also reports or removes regular orphan .pgctxseg files under the generated per-collection artifact directory when no visible manifest references them. Orphan rows use artifact_id = 0, status = 'orphaned_file', and lifecycle_state = 'orphaned_file'. Symlinks, directories, and non-segment files are skipped.

The metadata is stored in extension catalogs and is useful for explicit operator workflows. It is not a shared Rust worker queue, and it is not a stable serving contract for HNSW, segment, or mmap artifacts. File materialization is an experimental publication primitive; it does not make artifacts query-safe. - pgcontext.scalar_reconstruct(codes bytea, min real, max real, levels integer) reconstructs scalar byte codes to vector. - pgcontext.product_quantize(vector, subvector_dimensions integer, codebooks jsonb) returns product-quantization byte codes using JSONB centroid codebooks. - pgcontext.product_reconstruct(codes bytea, subvector_dimensions integer, codebooks jsonb) reconstructs product byte codes to vector. - pgcontext.rerank_quantized_candidates(query vector, point_ids bigint[], original_vectors vector[], metric text, limit integer) treats point_ids as approximate candidates only, requires one original dense vector per candidate, and returns exact rerank scores in final SQL order.

Experimental vector variants are SQL-visible so users can test parsing, validation, and exact scoring outside the stable compatibility promise:

SQL types halfvec, sparsevec, bitvec, int8vec, and uint8vec
Pgvector-style halfvec(n), sparsevec(n), and bitvec(n) typmods with dimension enforcement on assignment
Text constructors pgcontext.halfvec(text), pgcontext.sparsevec(text), and pgcontext.bitvec(text)
Dimension helpers pgcontext.halfvec_dims(halfvec), pgcontext.sparsevec_dims(sparsevec), and pgcontext.bitvec_dims(bitvec)
Exact half-vector distance functions for L2, inner product, negative inner product, cosine, and L1
Explicit-only, rounding half-vector casts from real[], integer[], and double precision[] to halfvec, and an assignment cast from halfvec to real[]
Half-vector aggregates pgcontext.sum(halfvec) and pgcontext.avg(halfvec)
Sparse-vector array constructor pgcontext.sparsevec_from_arrays(integer[], real[], integer) plus pgcontext.sparsevec_indices(sparsevec) and pgcontext.sparsevec_values(sparsevec) accessors
Sparse-vector casts between real[], dense vector, and sparsevec
Sparse-vector aggregates pgcontext.sum(sparsevec) and pgcontext.avg(sparsevec)
Exact sparse-vector distance functions for L2, inner product, negative inner product, cosine, and L1
Bit-vector Hamming and Jaccard distance functions
Bit-vector casts from boolean[] to bitvec and from bitvec to boolean[]
Bit-vector casts from PostgreSQL bit and bit varying to bitvec, and from bitvec to PostgreSQL bit and bit varying
Pgvector-compatible built-in bit Hamming and Jaccard distance functions, plus <~> and <%> operator overloads
Bit-vector aggregates pgcontext.bit_or(bitvec) and pgcontext.bit_and(bitvec)
Distance operator overloads for halfvec (<->, <#>, <=>, <+>), sparsevec (<->, <#>, <=>, <+>), and bitvec (<~>, <%>)
Default btree ordering opclasses pgcontext.halfvec_ops, pgcontext.sparsevec_ops, and pgcontext.bitvec_ops for deterministic equality, comparison, and ordinary PostgreSQL btree indexes

The non-dense HNSW operator classes are first-class SQL contracts:

halfvec_hnsw_ops, halfvec_hnsw_ip_ops, halfvec_hnsw_cosine_ops, and halfvec_hnsw_l1_ops
sparsevec_hnsw_ops, sparsevec_hnsw_ip_ops, sparsevec_hnsw_cosine_ops, and sparsevec_hnsw_l1_ops
bitvec_hnsw_hamming_ops and bitvec_hnsw_jaccard_ops
int8vec_hnsw_ops, int8vec_hnsw_ip_ops, int8vec_hnsw_cosine_ops, and int8vec_hnsw_l1_ops
uint8vec_hnsw_ops, uint8vec_hnsw_ip_ops, uint8vec_hnsw_cosine_ops, and uint8vec_hnsw_l1_ops

Each class stores a dense graph payload, traverses with the matching metric, and returns the exact operator distance type. The variant types and their non-index SQL helpers remain experimental as a broader compatibility surface.

Experimental sparse collection metadata validates table-backed sparsevec source columns and stores per-vector sparse storage/index/status metadata:

pgcontext.register_sparse_vector(collection_name text, vector_name text, vector_column text, dimensions integer, metric text)
pgcontext.collection_sparse_vectors(collection_name text)
pgcontext.configure_sparse_vector(collection_name text, vector_name text, storage_options jsonb, index_options jsonb, status text)
pgcontext.attach_sparse_hnsw_index(collection_name text, vector_name text, index_name text) validates and binds a schema-qualified, metric-matched sparse HNSW index.
pgcontext.search_sparse(query sparsevec, point_ids bigint[], vectors sparsevec[], metric text, limit integer) scores explicit sparse candidate arrays with l2, inner_product, cosine, or l1 and returns exact top-k rows with deterministic tie breaks.
pgcontext.search_sparse(collection text, vector_name text, query sparsevec, limit integer) serves a validated attached sparse HNSW index with exact source rerank, or falls back to exhaustive exact table-backed scoring when no valid binding exists.
pgcontext.search_sparse(collection text, vector_name text, query sparsevec, filter text, limit integer) applies registered-field filter JSON through an HNSW candidate mask and the final authoritative source recheck.
pgcontext.explain_sparse(collection text, vector_name text, query sparsevec, limit integer) reports the actual exact/HNSW strategy and scored/candidate/recheck counters.
pgcontext.query(collection text, vector vector, sparse_vector_name text, sparse_query sparsevec, limit integer) fuses dense exact search with exact sparse search through reciprocal rank fusion. Sparse ANN branches remain outside the stable compatibility promise.
pgcontext.rerank_late_interaction(query_vectors vector[], point_ids bigint[], candidate_vectors vector[], candidate_offsets integer[], limit integer) partitions candidate token vectors by point, scores each point with exact MaxSim inner product, enforces a comparison budget, and returns final rerank order with deterministic tie breaks.
pgcontext.register_lexical_source(collection text, source_name text, text_columns text[], text_configuration text, field_weights text[], json_paths text[], ranker text, normalization integer, rank_weights real[]) registers an ordered weighted lexical document over 1..=16 source-table text or JSON-path columns, resolving the text-search configuration, column type, and collation identity from pg_catalog under collection ownership and source-relation SELECT.
pgcontext.register_lexical_document_source(collection text, source_name text, document_column text, text_configuration text, ranker text, normalization integer, rank_weights real[]) registers a stored, generated, or trigger-maintained tsvector column as the lexical document. pgContext never writes that column.
pgcontext.register_lexical_tsquery(collection text, source_name text, tsquery_name text, tsquery_column text) binds a per-row tsquery column that the registered_tsquery query form references by name.
pgcontext.create_lexical_index(collection text, source_name text, method text) creates and attaches the canonical GIN or GiST index for a registered lexical source and returns the index name. The index expression is the same canonical document expression the query paths render.
pgcontext.attach_lexical_index(collection text, source_name text, index_name text) attaches an existing valid, live, non-partial GIN or GiST index on the registered source relation and records its full definition for drift detection. It must contain exactly one key and no included columns, use PostgreSQL’s tsvector_ops, and match the registered stored column or the planner-proven canonical raw-field expression.
pgcontext.detach_lexical_index(collection text, source_name text) removes the index binding and restores the complete exact fallback.
pgcontext.drop_lexical_source(collection text, source_name text) removes a registration and its field bindings.
pgcontext.lexical_sources(collection text) lists registered lexical sources visible to the caller.
pgcontext.register_fuzzy_source(collection text, source_name text, text_column text) registers a PostgreSQL text column as a pg_trgm trigram source, resolving the extension through its catalog entry rather than search_path.
pgcontext.query_fuzzy(source text, query text, mode text, threshold double precision, filter jsonb, limit integer) builds an experimental registered trigram-fuzzy query leaf.
pgcontext.create_fuzzy_index(collection text, source_name text, method text) creates and attaches the canonical gin_trgm_ops or gist_trgm_ops index.
pgcontext.attach_fuzzy_index(collection text, source_name text, index_name text), pgcontext.detach_fuzzy_index(collection text, source_name text), and pgcontext.drop_fuzzy_source(collection text, source_name text) manage fuzzy bindings. An attached index must key the registered column directly and use the operator class owned by the resolved pg_trgm extension; a same-table index over another column is rejected.
pgcontext.fuzzy_sources(collection text) lists registered fuzzy sources visible to the caller.
pgcontext.refresh_lexical_catalog(collection text) re-derives lexical and fuzzy catalog OIDs from their stable names after a dump/restore or source table rewrite and returns the number of refreshed source rows.
pgcontext.lexical_headline(collection text, source_name text, point_ids bigint[], query jsonb, options text) returns bounded ts_headline fragments for already-retrieved points. Point count, option bytes, and source-document bytes are admitted before PostgreSQL builds any markup; total output bytes are a hard cap enforced while reading the response. The returned text carries PostgreSQL’s own markup and must be sanitized by the caller for its output context.
pgcontext.register_late_interaction(collection text, source_table text, token_source text) binds a collection’s source-table vector[] column, materializes one private pgContext token row per array element under invoker ACL/RLS, installs a same-transaction source-DML capture trigger, and builds a collection-scoped inner-product HNSW index. The ordinary-table source must expose id as a NOT NULL, immediate, single-column unique key. An empty source is registered as building until a repair can infer dimensions and publish the index.
pgcontext.repair_late_interaction(collection text, batch_size integer) atomically replaces the derived token rows with keyset pagination and a per-batch materialization byte budget, refreshes restored table bindings, reinstalls the capture trigger, and rebuilds the HNSW index. A failed statement or savepoint rolls the previous token generation and index back intact.
pgcontext.search_late_interaction(collection text, query_vectors vector[], vector_column text, limit integer) exact-scores active collection points from a table-backed vector[] source column with MaxSim and returns final SQL order with ACL/deleted-point checks.
pgcontext.explain_late_interaction(collection text, query_vectors vector[], vector_column text) reports the exact table scan, MaxSim comparison count, comparison budget, and typed ANN-planner readiness diagnostics for a table-backed late-interaction query without materializing candidate vectors.
pgcontext.search_late_interaction_ann(collection text, query_vectors vector[], candidates_per_query integer, limit integer) serves approximate candidates from the collection’s registered, pgContext-owned token relation and collection-scoped inner-product HNSW generation. The candidate prefix expands geometrically, within collection and comparison budgets, when deleted or RLS-hidden candidates are removed by the invoker-side source recheck. Final ordering always uses exact MaxSim over the current source row; token vectors and source-table identifiers are not caller parameters.
pgcontext.explain_late_interaction_ann(collection text, query_vectors vector[], candidates_per_query integer) validates the bound source column and exact owned HNSW predicate, expression, dimension, and opclass before reporting the ANN planner strategy. It does not expose raw token vectors or exact global token counts.
pgcontext.search_late_interaction_ann(collection text, query_vectors vector[], vector_column text, token_table text, token_source_key_column text, token_vector_column text, candidates_per_query integer, limit integer) is the legacy experimental overload for user-maintained companion tables. It uses a companion token table with a pgcontext_hnsw index to collect approximate candidate source keys from a NOT NULL source-key column and a dimensioned vector(n) NOT NULL token column, deduplicates them, and exact-reranks the authoritative source-table vector[] values with MaxSim. The function validates the declared token dimension in O(1), including for an empty token table, and rejects mixed query dimensions or query/token mismatch with SQLSTATE 22023. It validates source and token-table ACLs, rejects strict collection max_candidate_budget violations for the projected token-candidate work, rejects planner-projected comparison budget violations before collecting token candidates, and enforces the actual hydrated exact-rerank budget while scoring source-table vectors.
pgcontext.explain_late_interaction_ann(collection text, query_vectors vector[], vector_column text, token_table text, token_source_key_column text, token_vector_column text, candidates_per_query integer) is the legacy companion-table explain overload. It validates the companion token table, NOT NULL source-key and token-vector columns, declared vector(n) dimensions, HNSW index, ACLs, strict collection candidate budget, and planner budget before reporting the ann_candidate_serving planner strategy.

Product codebooks are JSON arrays shaped as [[[centroid_value, ...], ...], ...], one codebook array per subvector. These functions, quantized candidate rerank, sparse exact array search, and late-interaction rerank surfaces are experimental; HNSW token candidate serving for late interaction is also experimental and must not expose approximate scores as final SQL scores.

Current Maturity Boundary

Named sparse ANN, SQL bit ANN indexing, quantized index scan serving, external artifact import/export APIs, and full multi-vector serving are not stable today. Missing product behavior, longer-duration certification, and broader workload coverage are tracked in the public roadmap.

PGXN

PostgreSQL Extension Network