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E
Vector DBFreemiumOpen Source
EDGEDB
Graph-relational database with pgvector for AI workloads
Apache-2.0
ABOUT
Traditional databases lack native vector search, forcing developers to run a separate vector database alongside their primary store. EdgeDB solves this by integrating pgvector directly into a graph-relational database on PostgreSQL, letting teams store structured data, relationships, and AI embeddings together with a single query language — eliminating data silos and reducing operational complexity for AI applications.
INSTALL
pip install gelINTEGRATION GUIDE
1. Store document chunk embeddings and perform semantic similarity search to retrieve relevant context for RAG pipelines within the same database as application data
2. Build search engines that understand meaning using cosine similarity and pgvector indexes on product descriptions or knowledge bases
3. Persist AI agent conversation history, tool outputs, and contextual state as vector embeddings for long-term memory and retrieval-augmented reasoning
4. Find similar items via vector distance queries while combining graph relationships with embedding similarity for hybrid recommendations
TAGS
graph-relationalvector-databasepostgresqlpgvectorembeddingssemantic-searchedgeqlgel