Elastic announced the Elasticsearch vector database now stores and automatically chunks embeddings from mistral-embed, with native integrations to the Open Inference API and the semantic_text field.
This reduces time to market for RAG applications and simplifies the development process by eliminating the need to architect bespoke chunking strategies and combining chunking with vector storage.
“We are invested in delivering open-first, enterprise-grade GenAI tools to help developers build next generation search applications,” said Shay Banon, founder and chief technology officer at Elastic. “Through our collaboration with the Mistral AI team, we’re simplifying the process of storing and chunking embeddings in Elasticsearch to a single API call.”
“Mistral AI has always been committed to open-weights and making AI accessible to all,” said Arthur Mensch, co-founder and CEO of Mistral AI. “Working with Elastic allows us to bring Mistral’s tools to more developers through the Elastic open inference API, and gives us the opportunity to work with a company that shares our value of accessibility. We’re excited to see what developers will create.”
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