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Vector DBFreemiumOpen Source
ARANGODB
Multi-model database with vector search for hybrid AI retrieval
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ABOUT
AI applications often need to query across different data models — documents for content, graphs for relationships, vectors for semantic similarity — requiring multiple databases. ArangoDB solves this with a single multi-model engine featuring ArangoSearch for vector and full-text search, allowing developers to combine document retrieval, graph traversal, and vector similarity in one query language (AQL).
INSTALL
docker run -p 8529:8529 arangodb/arangodbINTEGRATION GUIDE
1. Build a hybrid search system that combines vector similarity, full-text search, and graph traversal in a single query
2. Create a unified data layer for AI agents that stores documents, relationships, and embeddings in one database
3. Implement a recommendation engine using collaborative filtering over graphs combined with semantic vector search
4. Power a knowledge management application with flexible schema supporting both structured data and embeddings
5. Develop a geospatial AI application combining location data, document content, and vector similarity
TAGS
multi-modelgraph-databasedocument-databasevector-searchArangoSearchAQLdockerenterprise