The secure data, document, search, and AI back-end you build apps on.
Vectros gives your application a typed data store, a document pipeline, hybrid search (BM25 + vector with RRF fusion), and citation-grounded retrieval — with multi-tenant isolation, scoped access keys, and an audited compliance posture, behind one coherent API.
- Docs & quickstart → https://docs.vectros.ai
- Website → https://vectros.ai
| Language | Install | Repository |
|---|---|---|
| TypeScript / Node.js | npm install @vectros-ai/sdk |
vectros-sdk-node |
| Python | pip install vectros |
vectros-sdk-python |
| Java | ai.vectros:vectros-sdk (Maven Central) |
vectros-sdk-java |
The full API surface is described in vectros-api-spec (OpenAPI).
- Hybrid search over your documents and structured records — keyword + vector, fused.
- Document ingestion with schema-defined indexing, lookup/range queries, and folders.
- Grounded inference — chat, RAG, and document-ask, answered with citations.
- Multi-tenant by design — per-customer isolation, scoped keys, and app-contexts.
Building something with Vectros? Start at docs.vectros.ai.