🛠️ How-to guide

<aside> 🧠

WHAT IT DOES

A RAG project is the answering recipe your agent uses. It points at a knowledge base and controls how questions are searched, how answers are written, and how tightly the agent sticks to your content. (RAG is glossed in the Glossary; it's sometimes called a knowledge project.)

</aside>

RAG project vs. knowledge base

Think of it as two layers. A Knowledge Bases is the content and its search index — the what. A RAG project is how you search and answer over that content — the how. One knowledge base can sit behind several RAG projects, each tuned differently. A RAG project with no knowledge base attached can't run yet.

The RAG Projects page — each project shows its bound knowledge base (or a No KB warning), its retrieved-documents count, and its temperature.

Creating a RAG project

Select New RAG project. The setup is organized into five tabs. For what every setting means and when to change it, see RAG project settings explained; to validate a project before connecting it, see Testing a RAG project.

General

• Name and Description.
• Knowledge base — the library this project searches (required for it to run).
• AI model — which model writes the answers, or the workspace default.
• Persona — an optional system prompt that sets tone and role.

Retrieval — how your content is searched

• Query type — Simple (keywords), Semantic (meaning and context), Vector (embedding similarity), or the combined Vector + Simple / Vector + Semantic. Vector modes need a knowledge base with vector search on.
• Search filter mode — Disabled, Hybrid, or Strict control on custom-field filtering.
• Retrieved documents — how many passages the model sees per question (default 5, up to 50).
• Extract relevant sentences — pass only the relevant lines from each passage.
• Query optimization steps — pre-search steps that sharpen the question (classify intent, try a direct answer, spot vague questions, refine wording and pull out key terms).
• Clean search results — strip unrelated text before answering.

Generation — how answers are written