VectorAmp Guides
VectorAmp guides show teams and developers how to build governed AI search, cited RAG, ingestion, tenant-scoped retrieval, hybrid search, REST integrations, LangChain assistants, and MCP agents. Start with one of 13 implementation guides, then use the linked API, SDK, CLI, and concept references for production details.
These guides show how to use VectorAmp for real implementation goals: governed company search, cited RAG, compliance assistants, support workflows, tenant-scoped search, hybrid retrieval, CLI ingestion, REST integrations, LangChain, and MCP agents.
The homepage goal router links here because these pages are implementation-oriented. Each guide includes prerequisites, setup steps, code or dashboard workflows, validation checks, production notes, and links to related reference docs.
What is filter-native retrieval?
Filter-native retrieval is a vector search design where metadata constraints participate in retrieval planning instead of being bolted on after nearest-neighbor search. In governed systems, that matters because a query often needs both semantic relevance and strict scope: the right tenant, role, source, region, document type, or date range.
Use this concept when you are evaluating tenant-scoped semantic search, hybrid search with filters, or filter-heavy vector search options.
What is governed AI search?
Governed AI search is retrieval over enterprise knowledge that preserves permissions, source boundaries, audit needs, and citations. A useful answer is not just semantically plausible; it must come from approved content, expose evidence, and respect access rules before content is shown to users or AI agents.
Common governed AI search use cases include policy and compliance Q&A, RFP and security questionnaires, support escalations, and MCP access for AI agents.
What is cited RAG?
Cited RAG is retrieval-augmented generation where every generated answer is grounded in retrieved evidence and returns source references. The citation layer helps users verify claims, inspect the underlying document, and decide whether the model answer is acceptable for policy, legal, support, finance, or operational workflows.
For implementation patterns, see company knowledge search with citations, LangChain RAG with VectorAmp, and the REST API cited search backend guide.
For teams
- Search and chat across company knowledge
- Answer policy and compliance questions with evidence
- Draft RFP and security answers from approved sources
- Resolve support escalations with product context
- Find contract risks and renewals across legal docs
- Create weekly briefings from team updates
For developers
- Build a RAG assistant with LangChain
- Add tenant-scoped semantic search to your app
- Build hybrid search with filters and reranking
- Compare vector search options for filter-heavy retrieval
- Ingest cloud documents with the CLI
- Give AI agents access to enterprise knowledge
- Call VectorAmp from any backend with the REST API
- Prepare a retrieval workflow for production