Vector embeddings meet SQLite in one repo: vectorlitedb. It promises embedding vectors stored inside a SQLite database, with a pip install promise. If you’re into SQL-powered workflows, this combo is intriguing [1].
What is vectorlitedb? vectorlitedb bills itself as SQLite for embeddings, bringing vector storage into the familiar relational database world. The project aims to keep embeddings inside a SQLite database file, alongside your existing data [1].
Installation at a glance - Pip install in 30 seconds [1]. - The post framing suggests a quick setup, with the GitHub repo acting as the source of truth [1].
Storing vectors in a .db - Vectors are stored inside a SQLite .db file [1]. - This design aims to keep embedding data alongside relational data in a single database file [1].
Example usage & integration ideas - The GitHub repository shows the concept in action and invites you to explore usage details [1]. - The approach sparks ideas for SQL-based analytics and workflows that blend embeddings with traditional tables, all inside SQLite’s ecosystem [1].
Closing thought: vectorlitedb points to a future where embedding data sits side-by-side with relational data in a single .db, making SQL your one-stop tool for vectors and tables.
References
Show HN proposes using SQLite to store embedding vectors; Python pip install, .db-backed vectors, GitHub project vectorlitedb for vector storage.
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