VectorLink
Vector/semantic indexing and AI-powered search
13 pages
Clustering Embeddings
How to enable store_clustering in your schema to generate clustering embeddings for projection, deduplication, and entity resolution
Entity Resolution with Versioned Search
How to use duplicate detection and cross-set matching for entity resolution, with tuning guidance
History & Branching in Versioned Search
Per-commit snapshots, branch-out with block reuse, reassignment, and staleness handling
Index Your Data
How to index your content and data with VectorLink
OpenAI and Handlebars Configuration
How to configure OpenAI embeddings and Handlebars templates for VectorLink semantic indexing in TerminusDB.
Searching with Versioned Search
How to search — GET vs POST, three modes, filters, pagination, snippets, similar documents, and duplicates
Set up VectorLink
Steps to set up VectorLink to work with OpenAI
TerminusDB Push Indexing
How TerminusDB automatically indexes documents into VectorLink using store_indices, with a worked example across three commits
Versioned Search Quickstart
Bring up the stack, index a commit, and run a search in five minutes
Versioned Search API Reference
Complete HTTP endpoint reference for the VectorLink 2.0 search engine
How to Use VectorLink
A series of how-to guides to get you started with VectorLink, the semantic indexer
Versioned Search
Semantic search with per-commit snapshots, branching, and hybrid retrieval for TerminusDB data products
Versioned Search Concepts
Core vocabulary for versioned search — domains, commits, branches, chunks, search modes, and distance