RAG User Guide

Build retrieval-augmented generation systems with data sources, pipelines, indexes, retrieval tuning, security, analytics, and workflows.

Who This Guide Is For

Where To Go

Page Use It For
/rag RAG dashboard.
/rag/sources Connect document, web, database, and API sources.
/rag/pipelines Build ingestion and retrieval pipelines.
/rag/indexes Manage vector and keyword indexes.
/rag/retrieval-studio Tune retrieval behavior.
/rag/playground Test retrieval and generation.
/rag/knowledge-graph Explore entity relationships.
/rag/security Configure leak detection and prompt injection protection.
/rag/analytics Review retrieval performance.
/rag/workflows Automate RAG workflows.

Core Concepts

Concept Meaning
Source An upstream content system such as documents, web pages, databases, or APIs.
Pipeline The ingestion, chunking, embedding, indexing, retrieval, and generation flow.
Index A searchable store for vectors, keywords, or hybrid retrieval.
Retriever The component that selects context for a query.
RAG security Controls for redaction, leak detection, prompt injection, and auditability.

Common Workflows

Create a RAG pipeline

  1. Add a source.
  2. Configure parsing, chunking, and metadata extraction.
  3. Choose embedding model and index settings.
  4. Run ingestion.
  5. Test retrieval in the playground.
  6. Add security policies.
  7. Monitor analytics and iterate.

Improve retrieval quality

  1. Open Retrieval Studio.
  2. Review failed or low-confidence queries.
  3. Adjust chunk size, metadata filters, hybrid weights, or reranking.
  4. Reindex if needed.
  5. Compare before and after results.

Best Practices