GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
sqlite-vss is a specialized SQLite extension that enables efficient vector similarity searches by integrating Faiss functionality into the database operations.
Good fit when
- - When you require to perform vector similarity searches within an SQLite environment and want to leverage the performance benefits of Faiss
- - If your application runs primarily on an SQLite backend and can benefit from in-database vector search capabilities without needing a separate vector database service
Avoid when
- - When a standalone, fully managed vector database service that supports additional features beyond Faiss is needed
- - In environments where the overhead of integrating a C++ extension into an SQLite database might not be feasible or acceptable
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (867d since push)
- As of Sep 20, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 20, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/asg017/sqlite-vssSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
This project is a C++ implementation of an SQLite extension that integrates Faiss for facilitating efficient vector similarity searches within database operations.
Capability facts
- Languages
- c++
Source: github.language · Sep 20, 2026
Categories
Tags
README
sqlite vss [!WARNING] is not in active development. Instead, my effort is now going towards , which is a similar vector search SQLite extension, but should be much easier to install and use than . See this blog post for more info. (SQLite <b <u V</u </b ector <b <u S</u </b imila...
For agents
This page has a .md twin and JSON over the API.