Optimize your RAG workflows with Elasticsearch and Vectorize

We’re excited to announce Vectorize now integrates with Elasticsearch vector database! This powerful combination simplifies building retrieval augmented generation (RAG) pipelines, allowing AI engineers to focus on building applications with unprecedented speed and accuracy.

Elasticsearch vector database enables fast and efficient real-time search and retrieval of vector data, making it an excellent database for RAG applications. Vectorize’s RAG pipelines extract and transform unstructured data, load vector search indices into your database, and ensure the indices stay current so your large language model (LLM) always has the latest data. By automating your RAG pipeline, you can focus on building solid, robust, accurate AI applications.

Vectorization strategies can be quantitatively evaluated using Vectorize’s RAG Evaluation tools, enabling you to identify the best approach before building your RAG pipeline. You can compare how different embedding models and chunking strategies perform on your data and look at metrics such as NDCG (Normalized Discounted Cumulative Gain) and relevancy scores. The evaluation capability in Vectorize significantly speeds up the identification of the strategies that provide the most relevance and generate the most accurate responses. This allows you to confidently build a RAG pipeline based on metrics instead of guesswork.

Vectorize’s integration with the Elasticsearch vector database allows AI engineers to quickly create a reliable RAG pipeline and focus on building applications instead of spending time on preprocessing and determining the best vectorization strategy.

Elastic's vector and hybrid search capabilities offer powerful tools for searching and analyzing large data sets. It handles structured and unstructured data, making it ideal for real-world generative AI models. Elastic's semantic search improves understanding of context, leading to more accurate and relevant AI-generated responses. These features are especially useful for tasks that require specific knowledge, like personalized recommendations, product searches, and user behavior-based conversations, among many other applications.

Elastic’s search tools for AI search app development, combined with Vectorize’s intelligent automation and quantitative, data-driven approach, enable AI engineers to build and deliver production-ready RAG pipelines faster than ever — and with unparalleled accuracy.

The release and timing of any features or functionality described in this post remain at Elastic's sole discretion. Any features or functionality not currently available may not be delivered on time or at all.

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