Qdrant vs Searchkick
Overview
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Detailed Comparison
Searchkick learns what your users are looking for. As more people search, it gets smarter and the results get better. It’s friendly for developers - and magical for your users. | It is an open-source Vector Search Engine and Vector Database written in Rust. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more. |
stemming - tomatoes matches tomato;special characters - jalapeno matches jalapeño;extra whitespace - dishwasher matches dish washer;misspellings - zuchini matches zucchini;custom synonyms - qtip matches cotton swab;query like SQL - no need to learn a new query language;reindex without downtime;easily personalize results for each user;autocomplete;“Did you mean” suggestions;works with ActiveRecord and Mongoid | Easy to use API;
Fast and accurate;
Advanced filtering;
Rich data types;
Cloud-native and horizontally scaleable;
Efficient & performant |
Statistics | |
GitHub Stars 6.7K | GitHub Stars 26.9K |
GitHub Forks 766 | GitHub Forks 1.9K |
Stacks 18 | Stacks 52 |
Followers 34 | Followers 20 |
Votes 1 | Votes 0 |
Pros & Cons | |
Pros
| No community feedback yet |
Integrations | |
| No integrations available | |
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