> ## Documentation Index > Fetch the complete documentation index at: https://docs.upsolve.ai/llms.txt > Use this file to discover all available pages before exploring further. # Knowledge Base > Store reference documents the agent searches on demand to interpret your terms, rules, and policies. The Knowledge Base is available for orgs on the skills + persistent-memory architecture. The Knowledge Base is a tab in the Data Model editor — alongside **Tables** and **Global Data Security** — that holds reference documents the agent must read before it can interpret your business correctly: definitions, business rules, policies, company background, and known caveats about the data. It answers a different need than [System Prompts](/ai-agent-builder/system-prompts). A system prompt is a compact set of rules the agent applies on **every** message. The Knowledge Base is a library of reference material the agent **searches on demand** — long-form documents that would swamp a system prompt but are invaluable when a question touches them. If your refund policy is two pages long, it belongs in the Knowledge Base, not the system prompt. Knowledge Base tab in the Data Model editor showing uploaded markdown files with tags ## Adding files Files can be added two ways from the Knowledge Base tab: **Create inline** — write a new document directly in the built-in markdown editor. Best for definitions and rules you're authoring for the agent in the first place. **Upload** — drop in existing `.md`, `.txt`, or `.docx` files. Uploads are converted to Markdown so that every file is editable in the same editor, diffable between versions, and readable by agents without a parser. The original file is not stored. Upload zone for dropping markdown, text, or Word documents into the Knowledge Base Organize files with **tags** so related documents stay grouped as the library grows. ## What belongs in the Knowledge Base * **Definitions** — the canonical meaning of terms users will use loosely ("gross margin", "active account", "churn") * **Business rules and policies** — refund windows, SLA tiers, pricing rules the agent should cite rather than guess * **Company background** — how the business is structured, what entities relate to what * **Data caveats** — "the `legacy_orders` table stops at March 2024", "EMEA revenue excludes partner channels" The test is: would the agent need to **read** this to interpret a term correctly? If yes, it belongs here. If it's a short rule that must always apply without being looked up, it belongs in the [system prompt](/ai-agent-builder/system-prompts) instead. ## Versioning The Knowledge Base is version-aware, just like the rest of the data model. There is one working copy per data model, and writes land on the **draft** version. Files are checkpointed when you publish a data model version, so every published version carries the exact set of documents the agent had at that point — and edits never silently alter a version that's live in production. See [Data Model & Schema](/ai-agent-builder/data-model-schema) for how draft and production versions work. ## How the agent uses it At chat time, the agent doesn't preload your Knowledge Base. It reaches in with two tools: * `search_context_files` — finds files relevant to the user's question * `read_context_file` — reads a specific file's contents in full So the cost of a large library is low: documents only enter the agent's context when retrieval says they're relevant. This is also why descriptive file names and good tagging matter — they improve what retrieval surfaces. ## Next steps The foundation the Knowledge Base sits in — tables, columns, descriptions, and versioning. Package procedures and conventions the agent loads on demand. Short, always-on rules that apply to every message. Example questions with trusted SQL and charts, retrieved by similarity.