raphaelsty/cherche
Neural Search
This project helps you build a search engine that understands what your users are looking for, even if they don't use exact keywords. You provide a collection of documents (like articles or product descriptions) and user queries, and it returns the most relevant documents, ranked by how semantically similar they are to the query. This is ideal for knowledge managers, e-commerce specialists, or anyone managing large text corpuses.
333 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to create a smart search system that finds relevant information based on meaning, not just keywords, across a large set of text documents.
Not ideal if you only need a basic keyword search or if your search data is not primarily text-based.
Stars
333
Forks
14
Language
Python
License
MIT
Category
Last pushed
Jun 01, 2024
Commits (30d)
0
Dependencies
9
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/raphaelsty/cherche"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
AmenRa/retriv
A Python Search Engine for Humans 🥸
AKSW/sante
The Ontology, Dataset and Knowledge Search Engine
gnes-ai/gnes
GNES is Generic Neural Elastic Search, a cloud-native semantic search system based on deep...
erenisci/wikipedia_synonym_search
Semantic search engine over Turkish Wikipedia. Uses a 3-stage pipeline (MongoDB → Word2Vec →...
eswar-7116/wiki-semantic-crawler
A Semantic A* Pathfinding agent that navigates Wikipedia using high-dimensional vector space....