patricktrainer/duckdb-embedding-search
Fast similarity search using DuckDB
This tool helps you quickly find text that is semantically similar to a given piece of text. You input a collection of text documents (like comments or articles) and then provide a specific query text. The system outputs a list of the most similar documents, ranked by how closely they match your query's meaning. This is ideal for anyone needing to analyze large text datasets, such as researchers, content strategists, or community managers.
146 stars. No commits in the last 6 months.
Use this if you need to rapidly discover semantically related documents within a large collection without relying on exact keyword matches.
Not ideal if your primary goal is exact keyword search, rule-based text filtering, or if you don't want to use OpenAI's embedding models.
Stars
146
Forks
7
Language
Python
License
MIT
Category
Last pushed
Oct 30, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/vector-db/patricktrainer/duckdb-embedding-search"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
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