<ul><li>Astra DB now supports hybrid search, combining vector search and BM25 keyword search to improve search relevancy by up to 45%.</li><li>Hybrid search involves performing both vector and keyword searches, then reranking the results for better accuracy.</li><li>Reranking is done by a cross-encoder model that scores relevance based on the query and document.</li><li>To use Astra DB Hybrid Search in Python, install astrapy and python-dotenv dependencies.</li><li>Creating a collection in Astra DB for hybrid search involves setting vector metrics and lexical settings.</li><li>You can insert and index data for hybrid search with provided embeddings and text for keyword search.</li><li>Performing a hybrid search involves using $hybrid as the sort field and reranking based on different scores.</li><li>Providing your own vectors for search is possible and reranking can be based on a custom query.</li><li>Different search queries can be used for initial searches to improve relevancy in RAG applications.</li><li>Hybrid search combines vector search, keyword search, and reranking to enhance search accuracy and relevance in Astra DB.</li></ul>

Improve Your Python Search Relevancy with Astra DB Hybrid Search

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