5 best practices for implementing a vector database for semantic search

Discover how vector databases are transforming search and information retrieval in the age of AI. This practical, five-step guide dives into the architecture, models, and workflows behind high-performance semantic retrieval. Learn how to choose between dense and sparse vector models, prepare and vectorize unstructured data, and leverage Elasticsearch features such as the semantic_text field type for automated chunking and embedding.

This guide covers best practices for indexing strategies, query optimization, and performance monitoring to ensure low-latency, high-precision results at scale. It’s ideal for developers building AI-powered search, retrieval augmented generation (RAG) pipelines, or real-time knowledge systems.

Highlights

• Learn the fundamentals of vector databases and their role in semantic search.
• Explore dense and sparse vector models, including E5 and ELSER, for different use cases.
• Get step-by-step best practices for data preparation, indexing, and query tuning.
• Discover how to implement scalable, high-performance AI search with Elasticsearch.
• See real-world applications of vector search across ecommerce, public sector, and financial services.

Additional resources

• The most widely deployed, open source vector database
• [Guide] Semantic Search: Bringing search experiences into the AI era
• [Webinar] Agentic search powered by Elastic's vector database