Contents

• pgContext
  • Live demos
  • Vector search
  • Metadata filtering
  • Roadmap
  • Quickstart
  • Build from Source
  • Package Registries
    • Homebrew
    • PGXN
  • Installing with an AI Agent
  • Minimal Packaged Example
  • How It Works
  • Feature Status
  • Comparison and Fit
  • Focused V1 Scope
  • Documentation
  • PostgreSQL Authority, Community, and Security
  • License

pgContext

A full AI search engine, built into Postgres.

Hybrid dense + full-text retrieval, filter-aware ANN, and exact, MVCC-visible re-scoring: a dedicated vector engine's feature set, as a PostgreSQL 17 and 18 extension.

Built by Evokoa  ·  Fully-managed hosting at Polygres

pgContext is an Apache-2.0 PostgreSQL 17 and 18 extension that turns Postgres into a full AI search engine: dense vector search, metadata-filtered approximate search, and hybrid (dense + full-text) retrieval, all inside the database you already run.

Most retrieval stacks add a second service, copy your application data into it, and create a separate authorization, backup, and recovery boundary to keep in sync. pgContext keeps retrieval next to the data it searches: your ordinary PostgreSQL tables stay the source of truth for vectors, metadata, MVCC, ACL/RLS, backup, and replication. HNSW and other acceleration state are derived, rebuildable indexes (never a second copy that can drift), and every approximate result is re-scored exactly against the live row before it is returned, so a fast answer is still a correct, permission-safe answer.

At a glance: exact and persisted HNSW search · L2, inner-product, cosine, and L1 metrics · filters over registered columns and JSONB paths · MVCC/ACL/RLS and exact-score rechecks · collections, scroll, count, facets, and grouping · dense + full-text hybrid retrieval with reciprocal-rank fusion.

[!TIP] Looking for a managed version? We have launched a managed version of pgContext on polygres.com for full high performance AI Retrieval on Postgres.

Live demos

See pgContext’s retrieval in action (hosted on Polygres):

• Wikipedia hybrid search: query a Wikipedia-scale dataset with live semantic + keyword hybrid retrieval.
• Memory demo: an interactive hybrid-retrieval playground with adjustable fusion weights across retrieval channels.

Vector search

_{Standard GloVe-100-angular benchmark (1.18M vectors, cosine), both engines in one PostgreSQL 17 container with the same parallel build budget, scored against the dataset's own ground-truth neighbors. Full report →}

pgContext serves persisted, page-native HNSW straight from durable PostgreSQL index pages. An HNSW plan never substitutes fixture candidates or silently exact-scans the whole collection. On the standard GloVe-100-angular benchmark (1,183,514 vectors, cosine), with pgContext and pgvector in the same PostgreSQL 17 container and built with the same parallel budget:

• Faster at the same recall. pgContext matches pgvector’s recall at every search setting while answering each query 3.8-5.3× faster (for example, 0.910 recall@10 at 2.4 ms versus 13.0 ms at ef_search 512), and the advantage widens as you raise the recall target.
• Higher recall for the same latency. Read the other way, that speed is quality: given roughly 2.5 ms per query, pgContext reaches 0.91 recall@10 where pgvector reaches 0.75, because it can afford far more search effort in the same time.
• Every answer is re-checked exactly. ANN candidates are resolved back to the live row and re-scored exactly, under PostgreSQL MVCC visibility, ACL/RLS, and SQL predicates: a fast answer is still a correct, permission-safe answer.
• Hybrid retrieval is built in. Dense vector search fused with PostgreSQL full-text ranking (reciprocal-rank fusion) ships in the extension, not as application glue.

All figures above are Apple M4 Pro (NEON). See the pgContext vs pgvector report for the full latency and recall curves, the three-system comparison that adds Qdrant (a strong, mature peer that leads at very high recall through per-query segment parallelism), and docs/benchmarks/pgvector.md for every lane.

Metadata filtering

Filtering is where vector search usually breaks: bolt a WHERE clause onto an approximate search and recall quietly collapses as the filter gets selective. pgContext treats filtering as part of the search, not an afterthought.

Write Qdrant-style filters over your registered PostgreSQL columns and JSONB paths: must / should / must_not, equality, any / except, numeric and datetime ranges, is_null / is_empty:

SELECT source_key, score
FROM pgcontext.search(
    'docs', '[ ... ]'::pgcontext.vector,
    '{
       "must":     [{"key": "tenant_id", "match": "acme"},
                    {"key": "price", "range": {"gte": 10, "lt": 20}}],
       "should":   [{"key": "metadata.topic", "match": {"value": "billing"}}],
       "must_not": [{"key": "archived", "match": true}]
     }'::jsonb,
    10
);

What sets it apart:

• No filter index to build or maintain. Registering a column or JSONB path makes it filterable immediately; pgContext plans and runs the filtered search for you, no extra index required. Add an ordinary index (say, a btree on the column) later only when you want to speed a specific filter up; it’s an optimization, not a prerequisite.
• Filter-aware ANN, not post-filtering. Below a selectivity crossover pgContext scores exactly; above it, it pushes a single reusable mask through the persisted HNSW graph while excluded nodes still act as connectors, so recall holds up even when the filter is highly selective.
• One grammar for search, count, and facets. The same filter drives filtered search, counts, and facet aggregation: a cohesive retrieval API, not hand-assembled SQL per query.
• Safe and governed by PostgreSQL. Filters compile to a typed AST with bound parameters over registered fields only (no SQL injection, no mutating unregistered columns), and every result is re-checked against MVCC visibility and RLS/ACL before it is returned.

See Filters for the full grammar and field semantics.

Roadmap

pgContext 0.3.0 extends the V1 foundation with a bounded advanced-retrieval pipeline. It now includes:

• Broader vector indexing. Non-dense and integer HNSW, Stable quantized serving with exact rerank, segmented maintenance, and Experimental native IVFFlat.
• Composable retrieval. Stable typed composite execution, PostgreSQL-native lexical retrieval, and multi-model weighted reciprocal-rank fusion alongside dense, sparse, filtered, fuzzy, quantized, recommendation, lookup, and late-interaction branches.
• Semantic infrastructure. Experimental detached reranking and automatic source-linked chunking, with optional revision-pinned real-model smokes but no bundled model weights.
• Owned serving infrastructure. Internally maintained late-interaction tokens, immutable mapped generations, supervised jobs, automatic bounded telemetry, a lazy HNSW cursor, and the internal bounded virtual beam.
• pgvector migration. A certified PostgreSQL 17 and 18 main-extension binding, conflict-safe name facade, preflight/adoption tooling, and resumable HNSW/IVFFlat ownership conversion without requiring a new application vector column.
• Graph-augmented retrieval. We plan to bring graph capabilities from our sister extension pgGraph into pgContext, so vector results can expand and re-rank along the relationships in your data (the pattern behind GraphRAG), without leaving Postgres or copying data between systems.

Still to come: broader 1M/10M IVFFlat and HNSW certification, production model adapters and embedding jobs, topology-backed mixed retrieval, additional PostgreSQL majors, and longer platform/security campaigns. See the known limitations, product roadmap, and full roadmap.

Quickstart

Like pgvector, pgContext is one CREATE EXTENSION away once PostgreSQL can see it:

CREATE EXTENSION pgcontext;

Pick whichever install path fits your setup: Docker (zero build), Homebrew, PGXN, or source. The fastest cross-platform option is the pre-built Docker image; it is multi-arch (linux/amd64 and linux/arm64) and runs on Linux or through Docker Desktop’s Linux-container support on macOS and Windows. Choose the matching pgMAJOR-vVERSION tag; unqualified version tags continue to select PostgreSQL 17.

docker pull ghcr.io/evokoa/pgcontext:pg17-v0.3.0
docker run -d --rm \
  --name pgcontext \
  -e POSTGRES_PASSWORD=postgres \
  -e POSTGRES_DB=pgcontext \
  -p 5432:5432 \
  ghcr.io/evokoa/pgcontext:pg17-v0.3.0

Wait for PostgreSQL to accept connections, then verify the extension is loaded (this uses psql inside the container, so you do not need a local PostgreSQL client):

for i in $(seq 1 30); do
  docker exec pgcontext pg_isready -U postgres -d pgcontext && break
  sleep 1
done
docker exec pgcontext psql -U postgres -d pgcontext \
  -c "SELECT extname, extversion FROM pg_extension WHERE extname = 'pgcontext';"

If you have psql installed locally you can also connect directly:

psql -h localhost -U postgres -d pgcontext

Build from Source

For a checkout or downloaded archive:

make install PG_CONFIG=/path/to/postgresql-17/bin/pg_config
psql -d postgres -c 'CREATE EXTENSION pgcontext;'

You need Rust 1.96.0, cargo-pgrx 0.19.1, PostgreSQL 17 or 18, its server development headers, and a matching pg_config. See the complete installation guide for Linux/macOS/Windows shell support, verification, uninstall, cleanup, and troubleshooting.

Package Registries

Homebrew

The Evokoa Homebrew tap installs pgContext 0.3.0 for Homebrew postgresql@17. The formula builds from source and installs PostgreSQL extension files; it does not add a pgcontext command to your shell.

brew update
brew install Evokoa/tap/pgcontext
brew services start postgresql@17

Enable pgContext in each database that should use it (replace postgres with the target database name):

psql -X -v ON_ERROR_STOP=1 -d postgres \
  -c 'CREATE EXTENSION IF NOT EXISTS pgcontext;'

PGXN

pgContext 0.3.0 is available from PGXN. With PostgreSQL 17 or 18, its matching server development headers, Rust 1.96.0, and cargo-pgrx 0.19.1 installed:

pgxn install pgContext

Installing with an AI Agent

If you are an AI coding agent (or driving one) setting pgContext up in a fresh environment, follow AGENTS.md: it gives a deterministic, non-interactive install-and-verify recipe (Docker or source), the exact version pins, a copy-paste smoke test, and a short explanation of what the extension does so you can wire it into an application correctly.

Minimal Packaged Example

CREATE EXTENSION pgcontext;

CREATE TABLE docs (
    id text PRIMARY KEY,
    embedding pgcontext.vector(3) NOT NULL,
    category text NOT NULL,
    metadata jsonb NOT NULL
);

INSERT INTO docs VALUES
    ('postgres', '[1,0,0]', 'database', '{"language":"sql"}'),
    ('rust', '[0.8,0.2,0]', 'systems', '{"language":"rust"}'),
    ('vectors', '[0.7,0.1,0.2]', 'database', '{"language":"sql"}');

SELECT * FROM pgcontext.create_collection('docs', 'public.docs');
SELECT pgcontext.register_vector('docs', 'embedding', 'embedding', 3, 'cosine');
SELECT pgcontext.register_filter_column('docs', 'category', 'category');
SELECT pgcontext.upsert_points('docs', ARRAY['postgres', 'rust', 'vectors']);

SELECT source_key, score
FROM pgcontext.search(
    'docs', '[1,0,0]'::pgcontext.vector,
    '{"must":[{"key":"category","match":"database"}]}'::jsonb,
    3
);

CREATE INDEX docs_embedding_hnsw
ON docs USING pgcontext_hnsw (
    embedding pgcontext.vector_hnsw_cosine_ops
);

SELECT id, embedding OPERATOR(pgcontext.<=>) '[1,0,0]'::pgcontext.vector AS distance
FROM docs
ORDER BY embedding OPERATOR(pgcontext.<=>) '[1,0,0]'::pgcontext.vector
LIMIT 3;

The runnable, output-checked form is playground/demo.sql.

How It Works

pgContext registers application-owned tables and filterable fields in extension catalogs. Exact search is the correctness oracle. The pgcontext_hnsw access method stores metric-bound graph records on PostgreSQL index pages and returns a bounded candidate set. Every candidate is resolved back to the live source row, checked against PostgreSQL visibility and filters, and scored exactly before it is returned. Acceleration state is rebuildable; application data never moves out of PostgreSQL.

Feature Status

Capability	V1 status
Dense vectors, exact metrics, casts, and aggregates	Stable
Registered-table exact search and metadata filtering	Stable
Collections, scroll, count, facets, and grouping	Stable
Dense plus PostgreSQL full-text hybrid retrieval	Stable
Dense L2, inner-product, cosine, and L1 HNSW	Implemented; performance-qualified on PostgreSQL 17
Metadata-filtered ANN	Implemented with iterative and adaptive masked paths
halfvec, sparsevec, and bitvec wrappers/opclasses	Partial, experimental
PostgreSQL 17 and 18	Supported; release images build and verify on amd64 and arm64

Comparison and Fit

	pgContext V1	pgvector	Separate vector service
Authoritative data	Ordinary PostgreSQL tables	PostgreSQL columns	Usually copied externally
Exact dense search	Yes	Yes	Usually
HNSW	Dense, page-native	Mature	Common
IVFFlat	pgcontext_ivfflat, page-native, experimental	Mature	Product-dependent
Lexical / full-text	Registered tsvector sources, GIN/GiST, optional pg_trgm	Not provided; use PostgreSQL directly	Product-specific
Metadata filtering	Registered PostgreSQL fields/JSONB	SQL predicates	Product-specific filters
Drop-in pgvector compatibility	No	Native	No

For a detailed capability-by-capability view, including where pgvector or Qdrant is the better fit, see pgvector migration, the parity matrix, and the full three-way comparison.

Focused V1 Scope

V1 builds and verifies PostgreSQL 17 and 18 release images on amd64 and arm64, with exact retrieval, page-native dense HNSW, filtered ANN, and backend-local packed generations. Additional vector types are available for evaluation and controlled rollout. The roadmap grows that foundation with 10M-vector performance and correctness certification, a TurboQuant evaluation, deeper lifecycle certification, broader non-dense ANN, and more packaging options. See Known Issues and the roadmap for precise adoption guidance.

Documentation

• Overview
• Quickstart
• Installation
• Playground
• Indexes
• Filters
• Operations
• SQL API
• Known issues
• Roadmap

PostgreSQL Authority, Community, and Security

PostgreSQL owns durability, transactions, visibility, roles, RLS, backups, and replication. Treat pgContext HNSW and other acceleration artifacts as rebuildable indexes, not an independent database.

Contributions and independent workload testing are welcome. Read CONTRIBUTING.md and the Code of Conduct. Use GitHub issues for public bugs and support questions. Do not disclose vulnerabilities publicly; follow SECURITY.md or email team@evokoa.com.

pgContext is built by Evokoa, the team behind pgGraph.

License

Apache-2.0. See LICENSE and NOTICE.