prrao87/lancedb-study
Comparing LanceDB and Elasticsearch for full-text search and vector search performance
This project helps developers and engineers compare the performance of LanceDB and Elasticsearch for common search tasks. It takes a dataset of text and uses a pre-trained model to generate vector embeddings. The output is a detailed comparison of query speed and latency for both full-text and vector similarity searches, simulating real-world API interactions.
Use this if you are a software engineer or architect evaluating LanceDB versus Elasticsearch for a new application requiring fast full-text or vector search capabilities.
Not ideal if you are a business user looking for a ready-to-use search solution, as this project focuses on benchmark data for developers.
Stars
29
Forks
6
Language
Python
License
MIT
Category
Last pushed
Feb 08, 2026
Commits (30d)
0
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