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VexDB-THU/VexDB-Lite

A cross-platform vector database, which can be integrated into existing databases as a plugin.

78MITC++

View on GitHub Website

Stars: 1,675
Forks: 300
Watchers: 146
Open issues: 0
Contributors: 3

Star history, last 90 days+147 over 3 snapshots

2026-07-31 · 1,5282026-08-06 · 1,675

Star history, last 90 days
Date	Stars
2026-07-31	1528
2026-08-04	1675
2026-08-06	1675

Today

— stars today

This week

— stars this week

This month

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Momentum

35.0

growth rate 0.00%/day

From the README

VexDB

English | 中文

VexDB-Lite is a vector similarity search engine for PostgreSQL, DuckDB, and SQLite. The backends share the same graph index algorithm, SIMD distance dispatch, and PQ/RaBitQ quantization kernels.

See vexdb_duckdb/README.md for the DuckDB extension docs.
This root README is a project-level overview and build guide.

Latest release: v0.0.17

PostgreSQL 16–19 packages for Linux x86_64 and AArch64
DuckDB v1.5.2 packages for Linux x86_64 and AArch64
SQLite packages for Linux, macOS, iOS, Android, and WASM
SHA256SUMS.txt covering all 30 release archives

1. Components

1.1 PostgreSQL: `vexdb_lite`

Current functionality:

floatvector(N) type
Distance functions and operators:
- l2_distance (<->)
- cosine_distance (<=>)
- inner_product (use <~> for negative inner product / MIPS)
Scalar helpers: vector_dims(), vector_norm(), l2_normalize(), vexdb_index_info()
CREATE INDEX ... USING vexdb_graph
Index options: m, ef_construction, parallel_workers (parallel build), quantizer (pq / rabitq), pq_m, and memory_mode
PQ and RaBitQ code-only storage; memory_mode='compact' makes quantizer activation mandatory
Optimizer rewrite into an ANN Index Scan
Shared-memory vector buffer cache and parallel index build
Runtime settings: vexdb.ef_search, vexdb.vec_architecture

1.2 DuckDB: `vexdb_lite`

Current functionality:

GRAPH_INDEX on FLOAT[N] vector columns
Distance functions and operators:
- l2_distance (<->)
- cosine_distance (<=>)
- inner_product (use <~> for negative inner product / MIPS)
Note: <#> is unavailable in DuckDB (# clashes with its comment syntax); use <~> for negative inner product — same meaning as in PG.
Scalar helpers: vector_dims(), l2_normalize(), vexdb_version(), vexdb_index_info()
CREATE INDEX ... USING GRAPH_INDEX (vec [, metadata...]) with metadata filtering
Index options: m, ef_construction, parallel_workers (parallel build), quantizer (pq / rabitq) and pq_m
Both PQ and RaBitQ support memory_mode='compact'; compact RaBitQ still traverses the graph using quantized codes
Optimizer rewrite into VEXDB_INDEX_SCAN
Vector buffer cache and parallel index build
Runtime settings: vexdb_ef_search, vexdb_brute_force_threshold, vexdb_pq_search_mode, vexdb_pq_refine_k_factor

1.3 SQLite: `vexdb_lite`

See vexdb_sqlite/README.md for the full API and build guide. Current functionality:

GRAPH_INDEX virtual tables with shadow-table persistence
L2, cosine, and inner-product search with JSON or float32 BLOB vectors
Incremental insert, update, delete, transaction rollback, and reopen recovery
Metadata filtering, LIMIT pushdown, and parallel graph construction
PQ and RaBitQ in full or compact memory mode
Loadable desktop extension plus static registration for iOS, Android, and WASM

2. Capability Matrix

2.1 PG Extension Comparison (PGVector vs VexDB-Lite vs VexDB)

Category	Feature	Description	PGVector	VexDB-Lite (open-source)	VexDB (commercial)
Graph Index	graph_index	A fully self-developed high-performance graph index that merges the advantages of various graph indexes and works seamlessly across all scenarios.	❌	✅	✅
Distance	Distance function dispatch	Inlined distance functions, compile-time optimized	❌	✅	✅
Cache	vector buffer	General vector cache, all scenarios	❌	✅	✅
Cache	bulk buffer	Full in-memory vector cache for max throughput	❌	❌	✅
Cache	Async I/O cache	Accelerated disk-to-cache reads under memory pressure	❌	❌	✅
Data types	floatvector	Standard float32 vector type	✅	✅	✅
Data types	halfvector	Float16 vector type	✅	🟡	✅
Data types	int8vector	Int8 vector type	❌	🟡	✅
Quantization	PQ quantization	Maximum compression, QPS close to raw vectors	❌	🟡	✅
Quantization	RaBitQ quantization	Quantized graph traversal with code-only index storage	❌	✅	✅
Quantization	Auto quantization	Background auto-enable, supports empty-table index build	❌	❌	✅
Graph index enhancement	Async insert	Fast ingestion for write-heavy workloads	❌	❌	✅
Graph index enhancement	Graph sharding	Large-scale vectors on small-memory machines	❌	❌	✅
Graph index enhancement	Subgraph index build	Continue using memory for index building even in low-memory scenarios to accelerate build speed	❌	❌	✅
HA	Primary-replica HA	Synchronous replication and backup restore	✅	❌	✅
Maintenance	Parallel vacuum	Parallel index cleanup and reclaim	❌	❌	✅

2.2 DuckDB Extension Comparison (DuckDB VSS vs VexDB-Lite)

Category	Feature	Description	DuckDB VSS	VexDB-Lite (`vexdb_lite`)
Index	Graph index	VSS: HNSW; VexDB: graph_index (self-developed hybrid)	✅	✅
Distance	SIMD dispatch	Inlined distance functions, compile-time optimized	❌	✅
Quantization	PQ	Vector compression for memory-constrained scenarios	❌	✅
Quantization	RaBitQ	Shared quantized graph traversal with optional compact index-side storage	❌	✅
Cache	Buffer management	Disk-to-memory vector caching	❌	✅
Maintenance	Index compaction	Reclaim space from soft-deleted entries	✅	❌
Search	Filtered ANN search	WHERE filter with automatic oversampling	❌	✅
Persistence	Disk-backed index	Index survives database restart without rebuild	✅†	✅

† VSS persistence is experimental — WAL recovery is not implemented, unexpected shutdowns may cause index corruption. VexDB-Lite persists via DuckDB's standard serialization.

✅ Supported · 🟡 Coming soon · ❌ Not included in open-source edition

3. PostgreSQL Syntax Examples

3.1 Install and Create Table

CREATE EXTENSION vexdb_lite;

CREATE TABLE items (
    id  BIGSERIAL PRIMARY KEY,
    vec floatvector(128)
);

INSERT INTO items (vec) VALUES
    ('[0.10, 0.20, 0.30]'),
    ('[0.40, 0.50, 0.60]');

3.2 Build Index

CREATE INDEX idx_items_vec
ON items
USING vexdb_graph (vec floatvector_l2_ops)
WITH (
    m = 16,
    ef_construction = 64
);

3.2.1 PQ Quantized Index (v1)

PQ reduces vector storage by encoding indexed vectors with a trained codebook:

SET maintenance_work_mem = '2GB';   -- required; low memory falls back to a plain graph index

CREATE INDEX idx_pq
ON items
USING vexdb_graph (vec floatvector_l2_ops)
WITH (quantizer = 'pq', pq_m = 4);

Current v1 behavior:

If there are fewer than 256 training samples, or the memory build budget is too low, PQ falls back to a plain graph index with a NOTICE.
PQ indexes support post-build INSERT / UPDATE / DELETE; new vectors are encoded with the trained codebook.
For parallel_workers > 0, PQ training still runs in the leader process; PG parallel workers only participate in the disk-build phase.

Check the active index state:

SELECT indexname, use_pq, pq_m
FROM vexdb_index_info()
WHERE indexname = 'idx_pq';

3.2.2 RaBitQ Index

CREATE INDEX idx_rabitq
ON items
USING vexdb_graph (vec floatvector_l2_ops)
WITH (quantizer = 'rabitq', m = 16, ef_construction = 100);

PG trains RaBitQ only when at least 10,000 samples fit in maintenance_work_mem. In the default full mode, an unavailable quantizer emits a NOTICE and falls back to a plain graph. With memory_mode='compact', activation is mandatory and the build fails instead of writing raw vectors:

CREATE INDEX idx_rabitq_compact
ON items
USING vexdb_graph (vec floatvector_l2_ops)
WITH (quantizer = 'rabitq', memory_mode = 'compact');

PG quantized indexes already store only PQ/RaBitQ codes in the index vector fork; they do not keep a second raw-vector mirror. compact therefore acts as a strict storage contract rather than a second compression pass. Omitting quantizer in compact mode selects PQ; quantizer='none' is rejected. Compact indexes are not supported on unlogged tables. ALTER INDEX ... SET (memory_mode='compact') does not rewrite an existing raw index; use REINDEX and verify the effective state through vexdb_index_info().memory_mode plus index_inspect() attributes Working Quantizer and Vector Storage.

3.3 ANN Query

SET vexdb.ef_search = 100;
SET enable_seqscan = off;

SELECT id, vec <-> '[0.15, 0.25, 0.35]' AS dist
FROM items
ORDER BY vec <-> '[0.15, 0.25, 0.35]'
LIMIT 10;

3.4 Other Metrics

SELECT id
FROM items
ORDER BY vec <~> '[0.15, 0.25, 0.35]'
LIMIT 10;

SELECT id
FROM items
ORDER BY vec <=> '[0.15, 0.25, 0.35]'
LIMIT 10;

4. DuckDB Syntax Examples

4.1 Load Extension

LOAD '/path/to/vexdb_lite.duckdb_extension';
SELECT vexdb_version();

Typical Python usage:

import duckdb

con = duckdb.connect(config={"allow_unsigned_extensions": "true"})
con.execute("LOAD '/path/to/vexdb_lite.duckdb_extension'")

4.2 Create Table and Index

CREATE TABLE items (
    id       INTEGER,
    category VARCHAR,
    vec      FLOAT[128]
);

CREATE INDEX idx_items_vec
ON items
USING GRAPH_INDEX (vec)
WITH (
    metric = 'l2',
    m = 16,
    ef_construction = 64
);

4.3 ANN Query

SET vexdb_ef_search = 100;

SELECT id
FROM items
ORDER BY l2_distance(vec, [0.15, 0.25, 0.35]::FLOAT[3])
LIMIT 10;

4.4 Filtered Index Example

CREATE INDEX idx_items_vec_meta
ON items
USING GRAPH_INDEX (vec, category);

SELECT id
FROM items
WHERE category = 'book'
ORDER BY l2_distance(vec, [0.15, 0.25, 0.35]::FLOAT[3])
LIMIT 10;

4.5 Other Functions

SELECT inner_product([1.0, 0.0]::FLOAT[2], [0.5, 0.5]::FLOAT[2]);
SELECT cosine_distance([1.0, 0.0]::FLOAT[2], [0.5, 0.5]::FLOAT[2]);
SELECT vector_dims([1.0, 2.0, 3.0]::FLOAT[3]);
SELECT l2_normalize([3.0, 4.0]::FLOAT[2]);
SELECT * FROM vexdb_index_info();

5. Build

Prebuilt packages are recommended. Download the archive for your database, platform, and architecture from the v0.0.17 release, then verify it with the published checksum manifest:

shasum -a 256 -c SHA256SUMS.txt

SQLite package names include linux-x86_64, linux-aarch64, macos-arm64, macos-x86_64, ios-xcframework, android-arm64-v8a, android-x86_64, and wasm. Windows prebuilt packages are not available in v0.0.17.

5.1 Build the PostgreSQL Variant

Dependencies

PostgreSQL 16 ~ 19 (PG 16/17/18/19 supported; primary validation target is 19devel)
CMake ≥ 3.14
C++17 compiler (GCC 9+ or Clang 10+)

Build PostgreSQL (release example)

cd /path/to/postgresql-19-source
./configure \
  --prefix=/opt/postgresql-19rel-install \
  --without-icu \
  --without-readline \
  --without-zlib \
  CFLAGS="-O3 -DNDEBUG"
make -j$(nproc)
make install

Build `vexdb_lite`

cd /path/to/VexDB
mkdir -p build-pg19rel-release
cd build-pg19rel-release

export PG_CONFIG=/opt/postgresql-19rel-install/bin/pg_config
cmake -DCMAKE_BUILD_TYPE=Release ..
make -j$(nproc)
make install

PostgreSQL Configuration

At minimum:

shared_preload_libraries = 'vexdb_lite'

Then restart PostgreSQL and run:

CREATE EXTENSION vexdb_lite;

5.2 Build the DuckDB Variant

Recommended: use build_duck.sh — it handles DuckDB clone, cmake configuration, compilation, and metadata processing in one command.

bash build_duck.sh setup   # First time: clone DuckDB v1.5.2 and cmake configure
bash build_duck.sh build   # Compile the extension (incremental)

Output: build/duck/build/extension/vexdb_lite/vexdb_lite.duckdb_extension

Dependencies

CMake ≥ 3.28 and < 4.x
C++17 compiler (GCC 9+ or Clang 10+)
Git

Why a shell script and not plain cmake?

DuckDB extensions must be compiled inside DuckDB's source tree — you cannot run cmake -B build vexdb_duckdb/ standalone. build_duck.sh automates:

Cloning DuckDB v1.5.2
Writing extension_config_local.cmake to register the vexdb_lite extension
Running cmake + cmake --build
Appending the extension metadata footer (required by DuckDB's release format)

6. Running Tests

DuckDB Extension Tests

bash build_duck.sh build          # Build the extension
bash tests/spec/_lib/docker/run_duckdb.sh test  # Run full spec tests (requires Docker)

PostgreSQL Plugin Tests

bash tests/spec/_lib/docker/run_pg.sh test      # Run PG spec tests (requires Docker + PG19)

SQLite Extension Tests

bash build_sqlite.sh test
bash tests/spec/_lib/docker/run_sqlite.sh test

Tests are driven by a YAML spec DSL; test files live under tests/spec/.

The v0.0.17 release passed 127/127 DuckDB tests on Linux x86_64 and AArch64, 88/88 PostgreSQL 19 specs on AArch64, and 32/32 SQLite specs against SQLite 3.46.0 and 3.53.4. The iOS Simulator suite also passed M0, M1, SIMD, M2, M3, and M3+ scenarios, including the 40,000-vector parallel-build test with recall@10 of 1.0000.

7. Benchmark Results

Dataset: SIFT-1M 128-dim, m=16, ef_construction=128. Columns: QPS (reads=1) / QPS (reads=16) / Recall@10.

Test environment: Intel Core Ultra 7-265K (20c/20t, 3.9 GHz) / 16 GB DDR5 / x86_64 Linux

7.1 Comparison with pgvector / VSS (x86_64)

ef_search = 50

System	QPS (r=1)	QPS (r=16)	Recall@10
pgvector	507.9	7153.5	96.22%
vexdb_lite (PostgreSQL)	994.7	12084.6	95.97%
vexdb_lite (DuckDB)	717.5	8667.8	95.06%
duckdb-vss	496.1	5360.9	94.07%

ef_search = 100

System	QPS (r=1)	QPS (r=16)	Recall@10
pgvector	313.4	4272.5	98.82%
vexdb_lite (PostgreSQL)	618.5	7883.1	98.62%
vexdb_lite (DuckDB)	547.2	5379.1	98.40%
duckdb-vss	405.2	4433.3	98.04%

ef_search = 200

System	QPS (r=1)	QPS (r=16)	Recall@10
pgvector	193.1	2694.1	99.66%
vexdb_lite (PostgreSQL)	421.3	5038.0	99.58%
vexdb_lite (DuckDB)	383.6	4298.8	99.53%
duckdb-vss	321.9	3809.3	99.42%

8. Known Limitations

PostgreSQL

Supports PostgreSQL 16 ~ 19; primary validation target is PostgreSQL 19

DuckDB

threads and pq_m options are compatibility placeholders on some code paths
ARM Duck builds currently use scalar (GENERAL) distance dispatch without SIMD acceleration

SQLite and release packages

iOS, Android, and WASM use static registration; runtime .load is for desktop and server builds
v0.0.17 does not include Windows prebuilt packages

9. Repository Structure

Directory	Description
`common/`	Shared core: graph index algorithm, SIMD distance dispatch, quantizer (PQ/RaBitQ), template containers
`vexdb_pg/`	PostgreSQL extension: index AM, build, search, DML, WAL, distance entry
`vexdb_duckdb/`	DuckDB extension: index lifecycle, optimizer rewrite, distance functions → README
`vexdb_sqlite/`	SQLite extension: virtual table, shadow-table persistence, PQ/RaBitQ → README
`documentation/`	Feature docs, build guide
`tests/spec/`	YAML-based spec tests (shared / pg / duckdb / sqlite)
`scripts/`	Build, release, and packaging scripts
`thirdparties/`	Vendored dependencies (patched Boost)

Community

Channel	Description
GitHub Issues	Bug reports and feature requests
GitHub Discussions	Questions, proposals and general discussion
Discord	Real-time chat and Q&A
WeChat Group	Scan QR code at vexdb.com/community · Chinese community

License

MIT License. See LICENSE.

Rendered by GitHub from the current README

Read on GitHub

Top contributors3 shown

Quality score

Would you bet a product on this? Bounded 0–100 and slow moving.

78/10078

Maintenance: 90%; How recently the default branch was pushed to
Releases: 84%; Recency and cadence of tagged releases
Community: 15%; Contributor breadth and bus factor
Issue backlog: 100%; Open issues measured against the size of the audience
Documentation: 100%; README depth, plus a homepage and topics
Licensing: 100%; How freely the code can be adopted

What to know before adopting

Bus factor of 1 — one contributor dominates
Usable in commercial products with attribution.

Details

Created: 5 months ago
Last push: 7 days ago
Last release: 8 days ago
Release tag: v0.0.17
Releases in the last year: 5
Default branch: main
Size: 12k KB
Owner type: Organization
Owner location: China
Discovered via: phrase:vector database
First indexed: 7 days ago
Last synced: 2 days ago

VexDB-THU/VexDB-Lite

A cross-platform vector database, which can be integrated into existing databases as a plugin.

78MITC++

View on GitHub Website

Stars: 1,675
Forks: 300
Watchers: 146
Open issues: 0
Contributors: 3

Star history, last 90 days+147 over 3 snapshots

2026-07-31 · 1,5282026-08-06 · 1,675

Star history, last 90 days
Date	Stars
2026-07-31	1528
2026-08-04	1675
2026-08-06	1675

Today

— stars today

This week

— stars this week

This month

— stars this month

Momentum

35.0

growth rate 0.00%/day

From the README

VexDB

English | 中文

VexDB-Lite is a vector similarity search engine for PostgreSQL, DuckDB, and SQLite. The backends share the same graph index algorithm, SIMD distance dispatch, and PQ/RaBitQ quantization kernels.

See vexdb_duckdb/README.md for the DuckDB extension docs.
This root README is a project-level overview and build guide.

Latest release: v0.0.17

PostgreSQL 16–19 packages for Linux x86_64 and AArch64
DuckDB v1.5.2 packages for Linux x86_64 and AArch64
SQLite packages for Linux, macOS, iOS, Android, and WASM
SHA256SUMS.txt covering all 30 release archives

1. Components

1.1 PostgreSQL: `vexdb_lite`

Current functionality:

floatvector(N) type
Distance functions and operators:
- l2_distance (<->)
- cosine_distance (<=>)
- inner_product (use <~> for negative inner product / MIPS)
Scalar helpers: vector_dims(), vector_norm(), l2_normalize(), vexdb_index_info()
CREATE INDEX ... USING vexdb_graph
Index options: m, ef_construction, parallel_workers (parallel build), quantizer (pq / rabitq), pq_m, and memory_mode
PQ and RaBitQ code-only storage; memory_mode='compact' makes quantizer activation mandatory
Optimizer rewrite into an ANN Index Scan
Shared-memory vector buffer cache and parallel index build
Runtime settings: vexdb.ef_search, vexdb.vec_architecture

1.2 DuckDB: `vexdb_lite`

Current functionality:

GRAPH_INDEX on FLOAT[N] vector columns
Distance functions and operators:
- l2_distance (<->)
- cosine_distance (<=>)
- inner_product (use <~> for negative inner product / MIPS)
Note: <#> is unavailable in DuckDB (# clashes with its comment syntax); use <~> for negative inner product — same meaning as in PG.
Scalar helpers: vector_dims(), l2_normalize(), vexdb_version(), vexdb_index_info()
CREATE INDEX ... USING GRAPH_INDEX (vec [, metadata...]) with metadata filtering
Index options: m, ef_construction, parallel_workers (parallel build), quantizer (pq / rabitq) and pq_m
Both PQ and RaBitQ support memory_mode='compact'; compact RaBitQ still traverses the graph using quantized codes
Optimizer rewrite into VEXDB_INDEX_SCAN
Vector buffer cache and parallel index build
Runtime settings: vexdb_ef_search, vexdb_brute_force_threshold, vexdb_pq_search_mode, vexdb_pq_refine_k_factor

1.3 SQLite: `vexdb_lite`

See vexdb_sqlite/README.md for the full API and build guide. Current functionality:

GRAPH_INDEX virtual tables with shadow-table persistence
L2, cosine, and inner-product search with JSON or float32 BLOB vectors
Incremental insert, update, delete, transaction rollback, and reopen recovery
Metadata filtering, LIMIT pushdown, and parallel graph construction
PQ and RaBitQ in full or compact memory mode
Loadable desktop extension plus static registration for iOS, Android, and WASM

2. Capability Matrix

2.1 PG Extension Comparison (PGVector vs VexDB-Lite vs VexDB)

Category	Feature	Description	PGVector	VexDB-Lite (open-source)	VexDB (commercial)
Graph Index	graph_index	A fully self-developed high-performance graph index that merges the advantages of various graph indexes and works seamlessly across all scenarios.	❌	✅	✅
Distance	Distance function dispatch	Inlined distance functions, compile-time optimized	❌	✅	✅
Cache	vector buffer	General vector cache, all scenarios	❌	✅	✅
Cache	bulk buffer	Full in-memory vector cache for max throughput	❌	❌	✅
Cache	Async I/O cache	Accelerated disk-to-cache reads under memory pressure	❌	❌	✅
Data types	floatvector	Standard float32 vector type	✅	✅	✅
Data types	halfvector	Float16 vector type	✅	🟡	✅
Data types	int8vector	Int8 vector type	❌	🟡	✅
Quantization	PQ quantization	Maximum compression, QPS close to raw vectors	❌	🟡	✅
Quantization	RaBitQ quantization	Quantized graph traversal with code-only index storage	❌	✅	✅
Quantization	Auto quantization	Background auto-enable, supports empty-table index build	❌	❌	✅
Graph index enhancement	Async insert	Fast ingestion for write-heavy workloads	❌	❌	✅
Graph index enhancement	Graph sharding	Large-scale vectors on small-memory machines	❌	❌	✅
Graph index enhancement	Subgraph index build	Continue using memory for index building even in low-memory scenarios to accelerate build speed	❌	❌	✅
HA	Primary-replica HA	Synchronous replication and backup restore	✅	❌	✅
Maintenance	Parallel vacuum	Parallel index cleanup and reclaim	❌	❌	✅

2.2 DuckDB Extension Comparison (DuckDB VSS vs VexDB-Lite)

Category	Feature	Description	DuckDB VSS	VexDB-Lite (`vexdb_lite`)
Index	Graph index	VSS: HNSW; VexDB: graph_index (self-developed hybrid)	✅	✅
Distance	SIMD dispatch	Inlined distance functions, compile-time optimized	❌	✅
Quantization	PQ	Vector compression for memory-constrained scenarios	❌	✅
Quantization	RaBitQ	Shared quantized graph traversal with optional compact index-side storage	❌	✅
Cache	Buffer management	Disk-to-memory vector caching	❌	✅
Maintenance	Index compaction	Reclaim space from soft-deleted entries	✅	❌
Search	Filtered ANN search	WHERE filter with automatic oversampling	❌	✅
Persistence	Disk-backed index	Index survives database restart without rebuild	✅†	✅

† VSS persistence is experimental — WAL recovery is not implemented, unexpected shutdowns may cause index corruption. VexDB-Lite persists via DuckDB's standard serialization.

✅ Supported · 🟡 Coming soon · ❌ Not included in open-source edition

3. PostgreSQL Syntax Examples

3.1 Install and Create Table

CREATE EXTENSION vexdb_lite;

CREATE TABLE items (
    id  BIGSERIAL PRIMARY KEY,
    vec floatvector(128)
);

INSERT INTO items (vec) VALUES
    ('[0.10, 0.20, 0.30]'),
    ('[0.40, 0.50, 0.60]');

3.2 Build Index

CREATE INDEX idx_items_vec
ON items
USING vexdb_graph (vec floatvector_l2_ops)
WITH (
    m = 16,
    ef_construction = 64
);

3.2.1 PQ Quantized Index (v1)

PQ reduces vector storage by encoding indexed vectors with a trained codebook:

SET maintenance_work_mem = '2GB';   -- required; low memory falls back to a plain graph index

CREATE INDEX idx_pq
ON items
USING vexdb_graph (vec floatvector_l2_ops)
WITH (quantizer = 'pq', pq_m = 4);

Current v1 behavior:

If there are fewer than 256 training samples, or the memory build budget is too low, PQ falls back to a plain graph index with a NOTICE.
PQ indexes support post-build INSERT / UPDATE / DELETE; new vectors are encoded with the trained codebook.
For parallel_workers > 0, PQ training still runs in the leader process; PG parallel workers only participate in the disk-build phase.

Check the active index state:

SELECT indexname, use_pq, pq_m
FROM vexdb_index_info()
WHERE indexname = 'idx_pq';

3.2.2 RaBitQ Index

CREATE INDEX idx_rabitq
ON items
USING vexdb_graph (vec floatvector_l2_ops)
WITH (quantizer = 'rabitq', m = 16, ef_construction = 100);

CREATE INDEX idx_rabitq_compact
ON items
USING vexdb_graph (vec floatvector_l2_ops)
WITH (quantizer = 'rabitq', memory_mode = 'compact');

3.3 ANN Query

SET vexdb.ef_search = 100;
SET enable_seqscan = off;

SELECT id, vec <-> '[0.15, 0.25, 0.35]' AS dist
FROM items
ORDER BY vec <-> '[0.15, 0.25, 0.35]'
LIMIT 10;

3.4 Other Metrics

SELECT id
FROM items
ORDER BY vec <~> '[0.15, 0.25, 0.35]'
LIMIT 10;

SELECT id
FROM items
ORDER BY vec <=> '[0.15, 0.25, 0.35]'
LIMIT 10;

4. DuckDB Syntax Examples

4.1 Load Extension

LOAD '/path/to/vexdb_lite.duckdb_extension';
SELECT vexdb_version();

Typical Python usage:

import duckdb

con = duckdb.connect(config={"allow_unsigned_extensions": "true"})
con.execute("LOAD '/path/to/vexdb_lite.duckdb_extension'")

4.2 Create Table and Index

CREATE TABLE items (
    id       INTEGER,
    category VARCHAR,
    vec      FLOAT[128]
);

CREATE INDEX idx_items_vec
ON items
USING GRAPH_INDEX (vec)
WITH (
    metric = 'l2',
    m = 16,
    ef_construction = 64
);

4.3 ANN Query

SET vexdb_ef_search = 100;

SELECT id
FROM items
ORDER BY l2_distance(vec, [0.15, 0.25, 0.35]::FLOAT[3])
LIMIT 10;

4.4 Filtered Index Example

CREATE INDEX idx_items_vec_meta
ON items
USING GRAPH_INDEX (vec, category);

SELECT id
FROM items
WHERE category = 'book'
ORDER BY l2_distance(vec, [0.15, 0.25, 0.35]::FLOAT[3])
LIMIT 10;

4.5 Other Functions

SELECT inner_product([1.0, 0.0]::FLOAT[2], [0.5, 0.5]::FLOAT[2]);
SELECT cosine_distance([1.0, 0.0]::FLOAT[2], [0.5, 0.5]::FLOAT[2]);
SELECT vector_dims([1.0, 2.0, 3.0]::FLOAT[3]);
SELECT l2_normalize([3.0, 4.0]::FLOAT[2]);
SELECT * FROM vexdb_index_info();

5. Build

Prebuilt packages are recommended. Download the archive for your database, platform, and architecture from the v0.0.17 release, then verify it with the published checksum manifest:

shasum -a 256 -c SHA256SUMS.txt

5.1 Build the PostgreSQL Variant

Dependencies

PostgreSQL 16 ~ 19 (PG 16/17/18/19 supported; primary validation target is 19devel)
CMake ≥ 3.14
C++17 compiler (GCC 9+ or Clang 10+)

Build PostgreSQL (release example)

cd /path/to/postgresql-19-source
./configure \
  --prefix=/opt/postgresql-19rel-install \
  --without-icu \
  --without-readline \
  --without-zlib \
  CFLAGS="-O3 -DNDEBUG"
make -j$(nproc)
make install

Build `vexdb_lite`

cd /path/to/VexDB
mkdir -p build-pg19rel-release
cd build-pg19rel-release

export PG_CONFIG=/opt/postgresql-19rel-install/bin/pg_config
cmake -DCMAKE_BUILD_TYPE=Release ..
make -j$(nproc)
make install

PostgreSQL Configuration

At minimum:

shared_preload_libraries = 'vexdb_lite'

Then restart PostgreSQL and run:

CREATE EXTENSION vexdb_lite;

5.2 Build the DuckDB Variant

Recommended: use build_duck.sh — it handles DuckDB clone, cmake configuration, compilation, and metadata processing in one command.

bash build_duck.sh setup   # First time: clone DuckDB v1.5.2 and cmake configure
bash build_duck.sh build   # Compile the extension (incremental)

Output: build/duck/build/extension/vexdb_lite/vexdb_lite.duckdb_extension

Dependencies

CMake ≥ 3.28 and < 4.x
C++17 compiler (GCC 9+ or Clang 10+)
Git

Why a shell script and not plain cmake?

DuckDB extensions must be compiled inside DuckDB's source tree — you cannot run cmake -B build vexdb_duckdb/ standalone. build_duck.sh automates:

Cloning DuckDB v1.5.2
Writing extension_config_local.cmake to register the vexdb_lite extension
Running cmake + cmake --build
Appending the extension metadata footer (required by DuckDB's release format)

6. Running Tests

DuckDB Extension Tests

bash build_duck.sh build          # Build the extension
bash tests/spec/_lib/docker/run_duckdb.sh test  # Run full spec tests (requires Docker)

PostgreSQL Plugin Tests

bash tests/spec/_lib/docker/run_pg.sh test      # Run PG spec tests (requires Docker + PG19)

SQLite Extension Tests

bash build_sqlite.sh test
bash tests/spec/_lib/docker/run_sqlite.sh test

Tests are driven by a YAML spec DSL; test files live under tests/spec/.

7. Benchmark Results

Dataset: SIFT-1M 128-dim, m=16, ef_construction=128. Columns: QPS (reads=1) / QPS (reads=16) / Recall@10.

Test environment: Intel Core Ultra 7-265K (20c/20t, 3.9 GHz) / 16 GB DDR5 / x86_64 Linux

7.1 Comparison with pgvector / VSS (x86_64)

ef_search = 50

System	QPS (r=1)	QPS (r=16)	Recall@10
pgvector	507.9	7153.5	96.22%
vexdb_lite (PostgreSQL)	994.7	12084.6	95.97%
vexdb_lite (DuckDB)	717.5	8667.8	95.06%
duckdb-vss	496.1	5360.9	94.07%

ef_search = 100

System	QPS (r=1)	QPS (r=16)	Recall@10
pgvector	313.4	4272.5	98.82%
vexdb_lite (PostgreSQL)	618.5	7883.1	98.62%
vexdb_lite (DuckDB)	547.2	5379.1	98.40%
duckdb-vss	405.2	4433.3	98.04%

ef_search = 200

System	QPS (r=1)	QPS (r=16)	Recall@10
pgvector	193.1	2694.1	99.66%
vexdb_lite (PostgreSQL)	421.3	5038.0	99.58%
vexdb_lite (DuckDB)	383.6	4298.8	99.53%
duckdb-vss	321.9	3809.3	99.42%

8. Known Limitations

PostgreSQL

Supports PostgreSQL 16 ~ 19; primary validation target is PostgreSQL 19

DuckDB

threads and pq_m options are compatibility placeholders on some code paths
ARM Duck builds currently use scalar (GENERAL) distance dispatch without SIMD acceleration

SQLite and release packages

iOS, Android, and WASM use static registration; runtime .load is for desktop and server builds
v0.0.17 does not include Windows prebuilt packages

9. Repository Structure

Directory	Description
`common/`	Shared core: graph index algorithm, SIMD distance dispatch, quantizer (PQ/RaBitQ), template containers
`vexdb_pg/`	PostgreSQL extension: index AM, build, search, DML, WAL, distance entry
`vexdb_duckdb/`	DuckDB extension: index lifecycle, optimizer rewrite, distance functions → README
`vexdb_sqlite/`	SQLite extension: virtual table, shadow-table persistence, PQ/RaBitQ → README
`documentation/`	Feature docs, build guide
`tests/spec/`	YAML-based spec tests (shared / pg / duckdb / sqlite)
`scripts/`	Build, release, and packaging scripts
`thirdparties/`	Vendored dependencies (patched Boost)

Community

Channel	Description
GitHub Issues	Bug reports and feature requests
GitHub Discussions	Questions, proposals and general discussion
Discord	Real-time chat and Q&A
WeChat Group	Scan QR code at vexdb.com/community · Chinese community

License

MIT License. See LICENSE.

Rendered by GitHub from the current README

Read on GitHub

Top contributors3 shown

Quality score

Would you bet a product on this? Bounded 0–100 and slow moving.

78/10078

Maintenance: 90%; How recently the default branch was pushed to
Releases: 84%; Recency and cadence of tagged releases
Community: 15%; Contributor breadth and bus factor
Issue backlog: 100%; Open issues measured against the size of the audience
Documentation: 100%; README depth, plus a homepage and topics
Licensing: 100%; How freely the code can be adopted

What to know before adopting

Bus factor of 1 — one contributor dominates
Usable in commercial products with attribution.

Details

Created: 5 months ago
Last push: 7 days ago
Last release: 8 days ago
Release tag: v0.0.17
Releases in the last year: 5
Default branch: main
Size: 12k KB
Owner type: Organization
Owner location: China
Discovered via: phrase:vector database
First indexed: 7 days ago
Last synced: 2 days ago

Top AI Repos — open-source AI, indexed and scored

Trend

From the README

VexDB

1. Components

1.1 PostgreSQL: vexdb_lite

1.2 DuckDB: vexdb_lite

1.3 SQLite: vexdb_lite

2. Capability Matrix

2.1 PG Extension Comparison (PGVector vs VexDB-Lite vs VexDB)

2.2 DuckDB Extension Comparison (DuckDB VSS vs VexDB-Lite)

3. PostgreSQL Syntax Examples

3.1 Install and Create Table

3.2 Build Index

3.2.1 PQ Quantized Index (v1)

3.2.2 RaBitQ Index

3.3 ANN Query

3.4 Other Metrics

4. DuckDB Syntax Examples

4.1 Load Extension

4.2 Create Table and Index

4.3 ANN Query

4.4 Filtered Index Example

4.5 Other Functions

5. Build

5.1 Build the PostgreSQL Variant

Dependencies

Build PostgreSQL (release example)

Build vexdb_lite

PostgreSQL Configuration

5.2 Build the DuckDB Variant

Dependencies

Why a shell script and not plain cmake?

6. Running Tests

DuckDB Extension Tests

PostgreSQL Plugin Tests

SQLite Extension Tests

7. Benchmark Results

7.1 Comparison with pgvector / VSS (x86_64)

8. Known Limitations

PostgreSQL

DuckDB

SQLite and release packages

9. Repository Structure

Community

License

Top contributors3 shown

Quality score

What to know before adopting

Categories

Details

Trend

From the README

VexDB

1. Components

1.1 PostgreSQL: vexdb_lite

1.2 DuckDB: vexdb_lite

1.3 SQLite: vexdb_lite

2. Capability Matrix

2.1 PG Extension Comparison (PGVector vs VexDB-Lite vs VexDB)

2.2 DuckDB Extension Comparison (DuckDB VSS vs VexDB-Lite)

3. PostgreSQL Syntax Examples

3.1 Install and Create Table

3.2 Build Index

3.2.1 PQ Quantized Index (v1)

3.2.2 RaBitQ Index

3.3 ANN Query

3.4 Other Metrics

4. DuckDB Syntax Examples

4.1 Load Extension

4.2 Create Table and Index

4.3 ANN Query

4.4 Filtered Index Example

4.5 Other Functions

5. Build

5.1 Build the PostgreSQL Variant

Dependencies

Build PostgreSQL (release example)

Build vexdb_lite

PostgreSQL Configuration

1.1 PostgreSQL: `vexdb_lite`

1.2 DuckDB: `vexdb_lite`

1.3 SQLite: `vexdb_lite`

Build `vexdb_lite`

1.1 PostgreSQL: `vexdb_lite`

1.2 DuckDB: `vexdb_lite`

1.3 SQLite: `vexdb_lite`

Build `vexdb_lite`