VectorAmp Documentation

VectorAmp Docs

Build governed AI search with SABLE, citations, and production-ready retrieval.

VectorAmp gives teams the ingestion, indexing, filtering, APIs, and intelligence layer needed to turn scattered knowledge into trusted answers. Start with a dataset, add source content, retrieve with filters, and expose citations to users or agents.

Start fast

Create your first dataset, add content, and run a cited search.

• Introduction
• Quickstart
• Production readiness checklist

Build search and RAG

Use implementation guides for governed knowledge search, compliance assistants, RFP workflows, support escalation, and agent access.

• Guides overview
• Company knowledge with citations
• REST API cited search backend

Operate the platform

Manage datasets, ingestion, embeddings, organizations, and API keys from the dashboard.

• Application overview
• Datasets
• Pipelines
• API Keys

Integrate from code

Call VectorAmp from your backend, SDK, CLI automation, LangChain app, or MCP-compatible agent.

• Client libraries
• CLI
• Python SDK
• JavaScript SDK
• API Reference

What VectorAmp is optimized for

Capability	Where to go
Filter-aware vector retrieval for tenant, role, source, and document boundaries	Vector indexes, tenant-scoped search
Cited answers grounded in approved sources	Company knowledge with citations, Intelligence API
Ingestion workflows for files and source systems	Pipelines, CLI ingestion guide
Hybrid search when exact terms and semantic meaning both matter	Hybrid search guide, search comparison
Agent knowledge access without handing agents unrestricted data	MCP guide, enterprise agents guide