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Vector DBFreemiumOpen Source
ELASTICSEARCH
Hybrid keyword and vector search in a production-grade engine
AGPL-3.0-only / SSPL-1.0 / Elastic License 2.0
ABOUT
Teams building semantic search or RAG systems often end up maintaining one engine for keyword search, another for filtering and analytics, and a separate vector store for embeddings. Elasticsearch lets them store vectors and metadata in the same production search stack and run keyword, vector, and hybrid retrieval together instead of operating multiple disconnected systems.
INSTALL
docker pull docker.elastic.co/elasticsearch/elasticsearch:9.4.0INTEGRATION GUIDE
1. Build RAG retrieval over enterprise documents with filtering and relevance tuning
2. Run hybrid keyword and vector search across internal knowledge bases or support content
3. Power semantic product or help-center search with metadata-aware ranking
4. Search logs, tickets, or case histories by similarity while keeping standard search features
5. Deploy one retrieval layer for AI search, analytics, and operational visibility
TAGS
vector-searchhybrid-searchsemantic-searchragretrievalsearch-engineanalytics