vecadvisor 0.1.0a1

Cost-based advisor for filtered vector search in PostgreSQL/pgvector

VecAdvisor

Cost-based CLI advisor for filtered vector search in PostgreSQL and pgvector.

VecAdvisor helps answer a question PostgreSQL does not currently cost well:

SELECT id
FROM documents
WHERE tenant_id = 42
ORDER BY embedding <-> $query_vector
LIMIT 10;

When a vector index is searched first and SQL filters are applied after the ANN candidate set, selective filters can silently lose recall or return fewer than k rows. Sometimes a filter-first exact scan is faster and exact. Sometimes pgvector iterative scans are the right answer. Sometimes a partial HNSW index or partitioning is the durable fix.

This project models that choice with:

• PostgreSQL catalog and statistics introspection.
• Global selectivity estimation and PostgreSQL plan-row cross-checks.
• Local selectivity probes over the query vector neighborhood.
• A calibrated cost model for exact, post-filter ANN, iterative ANN, partial index, and partition-pruned strategies.
• Reproducible synthetic benchmark, calibration, validation, crossover, and proof reports.

Status: alpha. The Python CLI is useful today for experimentation and design validation. It does not modify PostgreSQL's planner.

VecAdvisor is an independent third-party tool. It works with PostgreSQL and pgvector, but it is not affiliated with the official pgvector project.

Why Local Selectivity Matters

Global selectivity is the fraction of the whole table matching a filter. Local selectivity is the fraction of near-neighbor candidates matching the filter for a query vector.

For filtered ANN, local selectivity is the important signal. A tenant may be globally rare but locally dense near its own documents. Or it may be locally sparse for a query outside that tenant's embedding cluster. A naive k / s model misses this. VecAdvisor probes a bounded unfiltered top-m neighborhood and costs durable recommendations against p10 local selectivity across representative query vectors.

Benchmark Evidence

The repository includes a deterministic synthetic crossover sweep under docs/benchmarks/ and a rendered chart:

This sweep varies filter selectivity (0.01, 0.05, 0.1, 0.3) and filter/vector correlation (-0.6, 0, 0.6) across 12 points. In this strategy-semantics simulation:

• Prediction match rate: 100%.
• Default post-filter ANN missed recall or returns-k targets in 12/12 bins.
• VecAdvisor avoided post-filter ANN in all post-filter failure bins.
• Mean speedup versus post-filter ANN: 1.98x.

Reproduce the artifact:

vecadvisor calibrate \
  --dataset synthetic \
  --rows 2048 \
  --dim 32 \
  --queries 24 \
  --clusters 8 \
  --filter-selectivity 0.1 \
  --correlation 0.5 \
  --limit 10 \
  --ef-sweep 10,20,40,80,160 \
  --seed 410 \
  --dataset-id synthetic-readme \
  --hardware-id github-readme-synthetic \
  --out docs/benchmarks/synthetic-calibration.json

vecadvisor benchmark-sweep \
  --dataset synthetic \
  --rows 2048 \
  --dim 32 \
  --queries 24 \
  --clusters 8 \
  --filter-selectivities 0.01,0.05,0.1,0.3 \
  --correlations -0.6,0,0.6 \
  --limit 10 \
  --ef-search 40 \
  --max-scan-tuples 1000 \
  --probe-rows 200 \
  --calibration docs/benchmarks/synthetic-calibration.json \
  --seed 411 \
  --out docs/benchmarks/synthetic-sweep.json

vecadvisor proof docs/benchmarks/synthetic-sweep.json \
  --out docs/benchmarks/synthetic-proof.json

vecadvisor plot-crossover docs/benchmarks/synthetic-sweep.json \
  --out docs/assets/synthetic-crossover.svg \
  --title "VecAdvisor synthetic crossover"

The current proof is synthetic and intentionally small enough to run quickly in a developer checkout. It validates the advisor's cost-model behavior and quality safeguards; it is not a replacement for workload-specific calibration against a production pgvector index.

Install

Install the current alpha from the GitHub release:

python -m pip install https://github.com/vecadvisor/vecadvisor/releases/download/v0.1.0a1/vecadvisor-0.1.0a1-py3-none-any.whl

For local development:

python -m pip install -e ".[dev]"

PyPI publishing is prepared through GitHub Actions Trusted Publishing. After the PyPI project is configured:

python -m pip install vecadvisor

The CLI entry point is:

vecadvisor --help

30-Second Demo

From a checkout:

python -m pip install -e ".[dev]"
docker compose -f docker/docker-compose.yml up -d
docker compose -f docker/docker-compose.yml exec -T postgres \
  psql -U postgres -d vecadvisor < examples/demo.sql

vecadvisor explain \
  --dsn postgresql://postgres:postgres@localhost:5432/vecadvisor \
  --table public.documents \
  --vector embedding \
  --query "tenant_id = 1" \
  --q-vector examples/query-vector.json \
  --probe-rows 16 \
  --format text

vecadvisor recommend \
  --dsn postgresql://postgres:postgres@localhost:5432/vecadvisor \
  --table public.documents \
  --vector embedding \
  --query "tenant_id = 1" \
  --q-vectors examples/query-vectors.json \
  --probe-rows 16 \
  --max-query-vectors 3

Local PostgreSQL

Start the bundled pgvector database:

docker compose -f docker/docker-compose.yml up -d

Load the example table:

psql "postgresql://postgres:postgres@localhost:5432/vecadvisor" -f examples/demo.sql

If you do not have psql locally, use Docker:

docker compose -f docker/docker-compose.yml exec -T postgres \
  psql -U postgres -d vecadvisor < examples/demo.sql

The default test DSN is:

postgresql://postgres:postgres@localhost:5432/vecadvisor

Analyze A Table

vecadvisor analyze \
  --dsn postgresql://postgres:postgres@localhost:5432/vecadvisor \
  --table public.documents \
  --vector embedding

This reports table rows, vector dimension, indexes, extended statistics, catalog fingerprints, stats freshness, and pgvector capabilities.

Explain One Query Vector

Use explain when you have one concrete query vector and want a one-query diagnostic:

vecadvisor explain \
  --dsn postgresql://postgres:postgres@localhost:5432/vecadvisor \
  --table public.documents \
  --vector embedding \
  --query "tenant_id = 1" \
  --q-vector examples/query-vector.json \
  --probe-rows 16 \
  --format text

explain reads --q-vector, runs one local selectivity probe, observes the planner's vector query shape, and renders an EXPLAIN VECTOR style report.

Recommend A Durable Strategy

Use recommend when you have representative query vectors for a workload:

vecadvisor recommend \
  --dsn postgresql://postgres:postgres@localhost:5432/vecadvisor \
  --table public.documents \
  --vector embedding \
  --query "tenant_id = 1" \
  --q-vectors examples/query-vectors.json \
  --probe-rows 16 \
  --max-query-vectors 3 \
  --local-cache-dir .vecadvisor-cache

recommend uses p10 local selectivity across the vector sample. It emits:

• Ranked candidate strategies.
• Estimated latency, recall, returns-k, and confidence.
• A concise decision summary.
• PostgreSQL observed plan, when a vector sample is available.
• Statistics suggestions such as CREATE STATISTICS.
• Optional partial index or partition strategy suggestions.

If you do not have production query vectors yet, you may explicitly opt in to a low-confidence table-sample fallback:

vecadvisor recommend \
  --dsn postgresql://postgres:postgres@localhost:5432/vecadvisor \
  --table public.documents \
  --vector embedding \
  --query "tenant_id = 1" \
  --allow-table-sample-vectors

Table-sampled vectors are marked in the output and reduce confidence.

Local Selectivity Cache

Repeated multi-vector probes can be cached:

vecadvisor recommend ... --local-cache-dir .vecadvisor-cache

Cache keys include:

• table name
• filter shape
• probe rows
• query vector source
• query vector fingerprint
• table statistics fingerprint
• index fingerprint

Changing table statistics or index definitions naturally produces a different cache key. Cache entries contain only aggregate local selectivity, not raw vectors or row ids.

Use --refresh-local-cache to ignore and replace an existing cache entry.

Calibrate

Synthetic calibration fits cost constants and an unfiltered recall curve:

vecadvisor calibrate \
  --dataset synthetic \
  --rows 5000 \
  --dim 64 \
  --queries 50 \
  --clusters 16 \
  --filter-selectivity 0.1 \
  --correlation 0.5 \
  --out profiles/local.json

Load a profile during explain or recommend:

vecadvisor recommend ... --calibration profiles/local.json

Without a profile, the advisor uses conservative default constants and marks that in candidate notes.

Benchmark, Sweep, Validate, Prove

Run a small synthetic benchmark:

vecadvisor benchmark \
  --dataset synthetic \
  --strategies exact,postfilter,iterative \
  --rows 512 \
  --dim 16 \
  --queries 8 \
  --clusters 8 \
  --filter-selectivity 0.1 \
  --correlation 0.5 \
  --limit 10 \
  --ef-search 40 \
  --out benchmarks/synthetic.json

Run a selectivity and correlation sweep:

vecadvisor benchmark-sweep \
  --dataset synthetic \
  --rows 512 \
  --dim 16 \
  --queries 8 \
  --clusters 8 \
  --filter-selectivities 0.01,0.05,0.1 \
  --correlations 0,0.5 \
  --limit 10 \
  --ef-search 40 \
  --out benchmarks/sweep.json

Analyze crossover behavior:

vecadvisor crossover benchmarks/sweep.json --out benchmarks/crossover.json

Build a publishability proof report:

vecadvisor proof benchmarks/sweep.json --out benchmarks/proof.json

Render a crossover chart:

vecadvisor plot-crossover benchmarks/sweep.json --out benchmarks/crossover.svg

Output Contract Highlights

Important JSON fields:

• catalog_snapshot: stats and index fingerprints for reproducibility.
• selectivity: advisor-vs-PostgreSQL selectivity cross-check with severity.
• local_selectivity: p10 and median local selectivity, confidence, and notes.
• local_selectivity_cache: cache hit/store metadata when enabled.
• recommendation.decision: winner, runner-up, viability, confidence, and why.
• recommendation.ranked: full candidate plan and estimate details.

Current Limitations

• Alpha quality: APIs and output shape may change before v0.1.
• The current release is an external CLI. It does not change PostgreSQL planner behavior.
• Realistic public benchmark datasets are not bundled yet.
• Calibration constants are workload and hardware dependent.
• Predicate parsing intentionally supports a restricted safe subset of filters.
• 10M-scale exact ground truth is deferred to the planned C++ SIMD kernel work.
• Postgres integration tests require a reachable PostgreSQL database with pgvector installed. Tests skip those cases if the database is unavailable.

Development

Install dev dependencies:

python -m pip install -e ".[dev]"

Run checks:

python -m pytest
python -m ruff check .
python -m mypy src/vecadvisor
python -m build

Prior Art And Clean-Room Notes

This project builds from public PostgreSQL, pgvector, and filtered ANN literature. It does not copy source from AGPL or source-available vector extensions. Ideas such as local selectivity, filtered ANN recall risk, iterative scans, partial indexes, and calibration are implemented from public documentation, papers, and first-principles cost modeling.

License

Apache License 2.0. See LICENSE.

Built and maintained by Varun Sharma.