Sqlite Vec Skilld

asg017/sqlite-vec sqlite-vec

Version: 0.1.7 Tags: latest: 0.1.7, alpha: 0.1.7-alpha.13

References: [package.json](./.skilld/pkg/package.json) — exports, entry points • [README](./.skilld/pkg/README.md) — setup, basic usage • [Docs](./.skilld/docs/INDEX.md) — API reference, guides • [GitHub Issues](./.skilld/issues/INDEX.md) — bugs, workarounds, edge cases • [Releases](./.skilld/releases/_INDEX.md) — changelog, breaking changes, new APIs

Search

Use skilld search instead of grepping .skilld/ directories — hybrid semantic + keyword search across all indexed docs, issues, and releases. If skilld is unavailable, use npx -y skilld search.

skilld search "query" -p sqlite-vec
skilld search "issues:error handling" -p sqlite-vec
skilld search "releases:deprecated" -p sqlite-vec

Filters: docs:, issues:, releases: prefix narrows by source type.

API Changes

This section documents version-specific API changes — prioritize recent major/minor releases.

• BREAKING: DELETE operations now properly clear vector data and free space — v0.1.7 changed behavior from only setting validity bits. Code using DELETE statements may see different storage behavior [source](./.skilld/releases/v0.1.7.md:L16)

• NEW: Distance column constraints in KNN queries — v0.1.7 adds support for >, >=, threshold or distance < threshold constraints in WHERE clauses to paginate through KNN results without fetching excess candidates [source](./.skilld/releases/v0.1.7.md#L17)

• Monitor the k value limit when performing large KNN queries — the default maximum k is 4096 (configurable) to prevent memory exhaustion; be aware that kNN results are materialized in memory and internally use O(n²) complexity on k [source](./.skilld/issues/issue-157.md#L22:33)

• Rely on v0.1.7+ for automatic DELETE cleanup — vector space is now reclaimed when enough vectors are deleted to clear a chunk (~1024 vectors); previous versions only marked entries as deleted without freeing space [source](./.skilld/releases/v0.1.7.md#L16)

• Select embedding models with quantization support for better results — models like nomic-embed-text-v1.5, mxbai-embed-large-v1, and OpenAI's text-embedding-3 are specifically trained to maintain quality after quantization and Matryoshka truncation [source](./.skilld/docs/binary-quant.md#L114:125)

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: skilld-dev
• Source: skilld-dev/skilld
• License: MIT
• Homepage: https://skilld.dev

Install and usage instructions live in the source repository linked above.