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Data Science
Towards AI·21d ago·5 min read
OpenSearch Optimizations for Production RAG
TL;DR
OpenSearch has been optimized for production-ready Retrieval-Augmented Generation (RAG) systems, addressing performance bottlenecks. Key enhancements include improved indexing and query execution times, leading to faster response rates.
✦ Why It Matters
Engineers can implement these OpenSearch optimizations to enhance the performance of their RAG systems immediately.
Key Takeaways
- Tune ef_search to balance recall and latency effectively.
- Use efficient filtering to improve recall in ANN queries.
- Consider Exact KNN for strict recall requirements.
- Normalize document vectors at index time to reduce latency.
How It Works
HNSW builds a layered graph where each vector is a node connected to its nearest neighbors, allowing for efficient navigation during queries. The search starts at the top layer and refines results layer by layer, significantly reducing the number of comparisons needed.