Semantic Search with Embeddings

Coming Soon

This tutorial is under development. Check back soon for a complete walkthrough.

What You'll Learn

This tutorial will walk you through configuring automatic embedding generation and running vector similarity searches using RaisinDB's built-in HNSW index and SQL extensions.

Planned Outline

1. Prerequisites — RaisinDB instance running, OpenAI or Ollama configured
2. Configure an embedding provider — set up API keys and model selection
3. Create nodes with content — insert documents that will be auto-embedded
4. Verify embeddings — confirm vectors were generated via the job system
5. Run a basic vector search — use VECTOR_SEARCH() in SQL
6. Interpret distance scores — understand cosine distance results
7. Hybrid search — combine vector search with property and path filters
8. KNN with node type filtering — restrict search to specific content types
9. Search modes — compare Documents vs. Chunks mode

Related Guides

• Embeddings and Vector Search — conceptual guide
• AI Provider Configuration — provider setup