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Qdrant 1.15 - Smarter Quantization & better Text Filtering

Blog post from Qdrant

Post Details
Company
Date Published
Author
Derrick Mwiti
Word Count
1,794
Company Posts That Month
14
Language
English
Hacker News Points
-
Post removed?
No
Summary

Qdrant 1.15 introduces several enhancements aimed at improving vector search and text filtering capabilities. The update includes advanced quantization techniques, such as asymmetric quantization and 1.5 and 2-bit quantization, which optimize memory usage and accuracy for high-dimensional vectors. The release also features significant upgrades to the text index, including a new multilingual tokenizer supporting languages like Japanese and Chinese, stopwords filtering, stemming for better query matching, and phrase matching for exact searches. Additionally, Maximal Marginal Relevance (MMR) reranking is introduced to balance result relevance and diversity, improving search output in dense datasets. The transition from RocksDB to Gridstore as the default storage backend enhances ingestion speeds and storage management, while optimizations such as HNSW healing and connectivity estimation improve indexing efficiency. The release also includes an updated Web UI for easier collection configuration and encourages best practices through an intuitive setup flow.

Trends Found in this Post
Trend Post Mentions Total Month Mentions Posts Companies MoM
Vector Search 4 2,058 362 133 +24%
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