retrieval augmented generation

5 stories

Stories

Introducing our new University course: “SurrealDB for AI Engineers”
Learn AI engineering hands-on with SurrealDB, from vector search and RAG to hybrid retrieval, agent memory, Text-to-SurrealQL, and context-layer architecture.
SurrealDB BlogSep 30, 20263 min read
Announcing the Mastra integration for SurrealDB and Agent Memory
One package gives Mastra agents a single database for conversations, workflow state, observability and vectors, plus managed memory that extracts and semantically recalls facts.
SurrealDB BlogSep 3, 20264 min read
How Cobrainer built graph-based agent memory on one engine
Cobrainer runs graph-based agent memory and a Rust-native agentic graph RAG on SurrealDB, replacing a separate vector store and search engine.
SurrealDB BlogAug 25, 20265 min read
Embedding models comparison: OpenAI, Google, Qwen, Nomic, more
A practical guide to eight embedding models — text-embedding-3-small/large, gemini-embedding-001, embeddinggemma-300m, qwen3-embedding, and more.
SurrealDB BlogJul 30, 20269 min read
One query, not two stores: how SurrealDB makes agents accurate
Vector search and graph traversal in a single SurrealDB query. How single-engine hybrid retrieval makes RAG agents more accurate.
SurrealDB BlogJul 14, 20267 min read