marnixkoops/hyperscale
⚡ Scalable recommendation serving and vector similarity search
This tool helps businesses quickly provide personalized recommendations or identify similar items from vast catalogs. It takes numerical representations (embeddings) of users and items, then rapidly delivers lists of the most relevant items. Marketers, e-commerce managers, content strategists, or anyone managing large inventories who needs to offer highly personalized experiences would find this useful.
No commits in the last 6 months.
Use this if you need to serve real-time recommendations or find similar items very quickly from a large collection (millions) of items, without requiring extensive engineering setup.
Not ideal if your recommendation needs are small-scale, not real-time, or if you prefer exact similarity calculations over approximate ones.
Stars
4
Forks
—
Language
Python
License
—
Category
Last pushed
Sep 28, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/marnixkoops/hyperscale"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Praful932/Kitabe
Book Recommendation System built for Book Lovers📖. Simply Rate ⭐ some books and get immediate...
passadis/ai-assistant
Books recommendation AI engine
sujee/mongodb-atlas-vector-search
Using MongDB Atlas with embedding models and LLMs to do vector search and RAG applications
dvsander/mdb-search
Example application querying data in different ways
ahmedshahriar/TwitterCelebrityMatcher
Match celebrity users with their respective tweets by making use of Semantic Textual Similarity...