API & deploy

Get a RAG API without building a pipeline

Skip chunking scripts and vector DB ops — upload docs, verify citations, and call one endpoint from your backend.

6 min read

Platform engineers evaluate RAG APIs on three axes: time to integrate, citation trust, and operational burden. Building pipelines checks the first box only if you ignore months of hidden work. This workflow gets you to a cited /v1/chat response without owning ingestion infrastructure.

The pipeline you are not building

A typical DIY stack includes upload workers, OCR, chunkers, embedders, vector indexes, rerankers, eval harnesses, and reindex cron jobs. Managed document Q&A collapses that into:

diagram
Your backend → Oprag /v1/chat → cited answer + sources

Documents live on Oprag’s AWS, isolated per project. You do not provision OpenSearch, Pinecone, or Bedrock Knowledge Bases in your account for the baseline path.

Minimal integration architecture

  1. Backend proxy — Your server holds the API key; browsers never see it
  2. Project per product or tenant — Keeps corpora and keys scoped
  3. Citation rendering — Your UI displays sources; Oprag returns structured citation metadata
User → Your app → POST /v1/chat (server-side) → Render answer + citations

Full request examples: API docs and REST integration guide.

Same schema from sandbox to production

Use one OpenAPI contract across environments. Swap API keys and project IDs — not response parsers. That reduces bugs when you promote from staging to prod.

When you still need custom logic

Keep retrieval on Oprag; add your own layers for:

  • Auth and rate limits per end user
  • Logging to your analytics warehouse
  • Guardrails on outbound links

Avoid re-implementing chunking unless eval proves a doc-type-specific gap.

Security defaults

  • Rotate keys on engineer offboarding
  • Never log full user messages if your policy forbids it
  • Read security for isolation model on Oprag’s AWS

B2B embed scenarios with many end customers: tenant-isolated RAG for B2B SaaS.

Evaluate build vs buy in one afternoon

  1. Upload a representative doc set
  2. Run 20 engineering FAQs in the dashboard
  3. Estimate weeks for DIY ingestion + citations
  4. Integrate the API stub if citations pass

Most teams ship the API path and revisit DIY only with measured retrieval gaps.

Ready to try it in your workflow?