Astra DB Hybrid Search‌‍‍‍‌‍‌‍‌‍‍‌‌‍‌‌‍‍‌‌‍‍‍‍‍‍‍‍‌‌‍‌‌‍‍‌‍‍‌‌‌‌‍‌‍‍‌‍‍‌‌‍‍‍‍‍‍‌‍‍‌‍‌‍‌‌‌‍‌‍‍‍‍‍‍‍‌‍‍‌‌‌‌‌‌‍‍‍‍‌‍‌‌‍‌‌‌‌‍‌‌‌‌‌‍‌‌‍‍‍‌‌‍‌‌‍‍‌‍‍‌‌‌‌‍‌‍‍‌‌‌‌‌‌‌‍‌‌‍‍‌‌‍‍‌‍‍‌‌‍‍‌‌‌‍‌‌‌‍‍‌‌‍‌‍‌‌‌‍‌‌‍‍‌‌‌‍‌‍‌‌‍‌‍‌‌‍‌‌‌‌‌‍‌‍‌‌‌‌‍‌‌‌‍‍‌‌‌‍‌‌‌‌‍‍‌‌‍‌‍‍‍‌‍‍‌‌‍‌‌‌‌‍‌‍‌‍‍‌‍‍‌‍‍‌‌‍‍‌‍‍‍‌‌‌‍‌‌‌‍‌‍‌‍‌‌‍‌‌‍‌‌‍‌‌‍‍‌‌‍‍‌‌‍‌‌‍‌‌‍‌‌‌‍‌‌‍‍‌‍‌‍‌‍‍‌‌‌‍‌‌‌‍‌‌‌‌‌‍‌‍‌‍‌‌‌‌‌‍‌‌‍‌‌‍‌‌‌‌‍‍‌‌‌‍‌‌‌‍‌‌‌‍‍‌‌‍‌‍‍‌‍‌‌‌‌‌‍‌‌‌‍‍‌‍‌‌‌‍‌‍‌‌‌‌‍‌‌‌‌‍‌‌‍‍‌‍‌‍‌‍‌‍‌‌‍‌‍‌‍‌‍‍‌‍‌‍‌‌‌‌‍‌‌‍‍‍‌‌‌‌‌‌‍‍‌‍‍‌‍‌‌‌‍‌‌‍‌‌‍‍‌‌‍‍‌‍‌‌‍‍‌‍‌‌‌‍‌‌‌‌‌‌‌‍‌‍‌‌‍‍‌‌‌‌‌‌‍‌‌‌‌‍‌‌‍‌‌‍‍‌‌‍‌‌‍‌‍‌‌‍‌‌‌‌‍‌‌‌‌‌‍‌‌‍‍‍‌‌‍‌‌‍‍‌‍‍‌‌‌‌‍‌‍‍‌‌‌‌‌‌‌‍‌‌‍‍‌‌‍‍‌‍‌‍‍‌‌‍‌‌‌‌‍‌‍‌‍‍‌‍‍‌‍‍‌‌‍‍‌‍‍‍‌‌‌‍‌‌‌‍‌‍‌‍‌‌‍‌‌‍‌‌‍‌‌‍‍‌‌‍‍‌‌‍‌‌‍‌‌‍‌‌‌‍‌‌‍‍‌‍‌‍‌‍‍‌‍‌‌‌‍‌‌‌‍‌‌‌‌‌‍‌‍‌‍‌‌‌‌‌‍‌‌‍‌‍‌‌‍‌‌‌‌‍‍‌‌‌‍‌‌‌‍‌‌‌‍‍‌‌‍‌‍‍‌‍‌‌‌‌‌‍‌‌‌‍‍‌‍‌‌‌‍‌‍‌‌‌‌‍‌‌‌‌‍‌‌‍‍‌‍‌‍‌‍‌‍‌‌‍‌‍‌‍‌‍‍‌‍‌‍‌‌‌‌‍‌‌‍‍‍‌‌‌‌‌‌‍‍‌‍‍‌‍‌‌‌‍‌‌‍‌‌‍‍‌‌‍‍‌‍‌‍‌‍‌‌‍‌‌‌‍‌‌‌‍‌‌‌‍‌‌‌‍‍‌‌‌‍‌‍‌‌‌‌‌‌‌‌‍‍‌‍‌‍‍‌‌‌‍‍‌‍‌‌‌‍‌‍‍‌‌

Improve Relevance by 45% with Astra DB Hybrid Search

A lot goes into building accurate AI applications. Astra DB Hybrid Search provides vector search for contextual relevance and lexical search for precise keyword matching, but it also automates reranking with NVIDIA NeMo Retriever reranking microservices. The result? Astra DB Hybrid Search significantly improves AI-powered search, recommendations, and personalization—improving the relevance of your AI apps by up to 45%.

Hybrid Search with Langflow

Easily build RAG apps in minutes with high relevance using Hybrid Search with Langflow.

Natively Built in Astra DB

You don’t have to configure your reranker; you can use it out-of-the-box on Astra DB. You get the power of auto-reranking at the database layer, without your data needing to leave the system.

Boost Result Relevance

Top results are reordered based on fine-tuned LLM models, delivering state-of-the-art relevance ranking and improving relevance by an average of 18.5% up to 45.07%.

Easily Build GenAI Apps

Simply drag and drop with Langflow, or use the Data API or AstraPy client.

Learn How Astra DB Hybrid Search Works

Why Hybrid Search?

AI that Understands Your Data

Hybrid Search combines vector search and lexical search, ensuring your AI retrieves and ranks the most contextually relevant results.

Enterprise AI Performance

Some databases (including MongoDB and others) struggle with performance bottlenecks in high-query environments. Astra DB delivers low-latency hybrid search at scale.

Precision & Recall Boost

Hybrid Search combines BM25 keyword matching with vector search, ensuring both relevance and contextual depth.

Get Started with Astra DB Hybrid Search

FAQs

What is hybrid search?

Hybrid search combines vector search (for semantic understanding) and lexical search (for exact keyword matching) to ensure the best possible results. While vector search helps understand context and meaning, lexical search ensures critical keyword matches aren’t overlooked. Blending these signals effectively requires intelligent ranking.

How does Astra DB Hybrid Search work?

Astra DB performs hybrid retrieval, combining lexical search using BM25 and vector search powered by Apache Cassandra®.

The top results are passed through the NVIDIA NeMo Retriever reranking microservices, which reorders them based on fine-tuned LLM models, significantly improving relevance.

You can achieve up to 45% improvement in search relevance, ensuring GenAI applications return the most accurate responses.

How can I use Astra DB Hybrid Search?

You can use Astra DB Hybrid Search with the Astra DB Python client supported by the schema-less, document-based Data API or Langflow's Astra DB component.