How can vector search improve search accuracy The azure cognitive search langchain integration, built in python, provides the ability to chunk the documents, seamlessly connect an embedding model for document vectorization, store the vectorized contents in a predefined index, perform similarity search (pure vector), hybrid search and hybrid with semantic search. Azure cognitive search is now azure ai search, and semantic search is now semantic ranker
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This feature is designed to streamline the process of chunking, generating, storing, and querying.
Over the past few months, we have delivered new capabilities as part of our goal to ensure our customers find the best the market has to offer in azure ai search when it comes to retrieval systems for generative ai applications Today, we are pleased to announce vector search. This feature is designed to streamline the process of chunking, generating, storing, and querying vectors for vector search in azure ai search Microsoft’s cognitive search api now offers vector search as a service, ready for use with large language models in azure openai and beyond.
Optimize azure ai search with vector search for improved document retrieval Add embedding, integration, and improve search results! Azure ai search provides a dedicated search engine and persistent storage of your searchable content for full text and vector search scenarios It also includes optional, integrated ai to extract more text and structure from raw content, and to chunk and vectorize content for vector search.
Organizations can store, index and search their own data, delivering current information to ai models
