esteininger/vector-search
The definitive guide to using Vector Search to solve your semantic search production workload needs.
This guide helps application developers implement semantic search capabilities. It explains how to store and compare data based on meaning, rather than just keywords. Developers can use this to enhance features like question-answering, personalization, and intelligent file search within their applications.
270 stars. No commits in the last 6 months.
Use this if you are an application developer looking to integrate advanced semantic search features, like understanding the context of user queries or classifying text, into your software.
Not ideal if you are looking for an out-of-the-box, no-code solution for search without needing to understand underlying architectural concepts.
Stars
270
Forks
15
Language
Jupyter Notebook
License
—
Category
Last pushed
Jun 26, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/vector-db/esteininger/vector-search"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
meilisearch/meilisearch
A lightning-fast search engine API bringing AI-powered hybrid search to your sites and applications.
nuclia/nucliadb
NucliaDB, The AI Search database for RAG
vespa-engine/vespa
AI + Data, online. https://vespa.ai
ICIJ/datashare
A self‑hosted search engine for documents
PrithivirajDamodaran/FlashRank
Lite & Super-fast re-ranking for your search & retrieval pipelines. Supports SoTA Listwise and...