Use Case

Retrieval-Augmented Generation

Grounding models in your own documents and knowledge bases.

21 providers 230 APIs 16 declared variants

Embedding, indexing, and retrieving private content so a language model can answer from it — vector search, chunking, knowledge bases, and the RAG pipelines that wire retrieval into generation.

21 API providers on the APIs.io network offer retrieval-augmented generation. The highest-rated are Airbyte, Merge, Confluence, OpenAI, Vespa.

Providers

Ranked by API Evangelist rating — Exemplar and Strong are expanded by default.

Exemplar 1 Complete, well-documented, and agent-ready
Strong 6 Solid coverage with minor gaps
Developing 7 Usable, with meaningful gaps to close
Thin 4 Limited public surface area
Emerging 2 Early or largely undocumented
Minimal 1 Almost no public developer surface

What Providers Actually Declared

This use case is a canonical term. These are the free-text strings providers wrote in their own apis.yml that map onto it.

AI and RAG ApplicationsAI/ML Vector SearchBuilding retrieval-augmented generation (RAG) applicationsEnterprise Knowledge RetrievalKnowledge BaseKnowledge Base AccessKnowledge base for AIMultimodal Semantic SearchRAG (Retrieval Augmented Generation)RAG ApplicationsRAG Pipeline EvaluationRAG Pipeline TuningRetrieval Augmented GenerationRetrieval-Augmented GenerationSemantic SearchVector Database Population

Scroll for all 16