Vectara

The API covers corpora (document collections), document ingestion (with automatic chunking and embedding), and queries (semantic search with hybrid retrieval). The query API returns ranked results with relevance scores and the option for Vectara's own summarization (Grounded Generation) that produces citations alongside generated answers. Agents can use Vectara purely for retrieval, injecting the ranked passages into their own LLM prompts.

Reliability is vendor-managed for the hosted platform. Vectara handles embedding model updates, index maintenance, and query infrastructure scaling. Teams relying on Vectara for production RAG pipelines should verify indexing latency for their document ingestion volumes and query latency SLAs for their expected concurrent query rates.

Vectara is a managed vector search platform designed for production semantic search and RAG pipelines, handling embedding generation, indexing, and retrieval as a managed service. Rather than choosing embedding models and managing vector infrastructure separately, Vectara accepts raw text and handles the full pipeline from embedding to ranked retrieval. For agents building RAG workflows, Vectara's retrieval API returns relevant passages with MMR (maximal marginal relevance) reranking, reducing the engineering work between document ingestion and LLM-ready context.

Authentication uses API keys for corpus-level access. Vectara supports separate query and index keys — query-only keys can be distributed to client-side or less-trusted contexts. The key model enables agents to use minimal-privilege credentials for read-only retrieval operations.

Documentation is thorough and RAG-focused. The ingestion and query API documentation explains the retrieval parameters (number of results, hybrid search tuning, reranking) clearly. Teams building agent RAG pipelines on Vectara will find the documentation covers practical integration patterns, with good guidance on the hybrid search configuration that affects retrieval quality.