Forum Discussion
Community ManagerAzure Cognitive Search: Outperforming vector search with hybrid retrieval and ranking capabilities
A common practice for implementing the retrieval step in retrieval-augmented generation (RAG) applications is to use vector search. This approach finds relevant passages using semantic similarity. This pattern is fully supported in Azure Cognitive Search and offers additional capabilities that complement and build on vector search to deliver markedly improved relevance.
Check out the blog post linked below showing the results of experiments conducted on Azure Cognitive Search as well as a quantitative basis to support the use of hybrid retrieval + semantic ranking as the most effective approach for improved relevance out-of–the-box!
Percentage of queries where high-quality chunks are found in the top 1 to 5 results, compared across search configurations. All retrieval modes used the same set of customer query/document benchmark. Document chunks were 512 tokens with 25% overlap. Vector and hybrid retrieval used Ada-002 embeddings.
What are your favorite tips to improve the quality of your vector search results?
