---
title: "Knowledge Base"
id: "1835"
type: "page"
slug: "knowledge"
published_at: "2026-09-20T23:49:51+00:00"
modified_at: "2026-10-22T16:38:23+00:00"
url: "https://xedant.com/agents/chatbot/docs/knowledge/"
markdown_url: "https://xedant.com/agents/chatbot/docs/knowledge.md"
excerpt: "The knowledge base is your documents, from which the bot answers client questions. Instead of…"
---

# Knowledge Base

[https://xedant.com/agents/chatbot/docs/knowledge/.md](https://xedant.com/agents/chatbot/docs/knowledge/.md)

The knowledge base is your documents, from which the bot answers client questions. Instead of inventing answers, AI looks for a matching piece in your texts and answers from it, attaching the source. Knowledge bases belong to a bot: one bot can have several bases — the price list separately, the delivery rules separately, for example.

## Documents

You can add plain text, marked-up text, a table and a website page by link. Each document in the list shows its processing state: queued, processing or ready. An unneeded document is deleted — its traces in search go with it, so outdated facts never surface in answers.

## Preparing documents

To make documents searchable, the application splits them into small fragments and turns each into a set of numbers — a so-called vector — using the embedding model you choose. That model is fixed to the base when it is created. If you later change the model, the base is marked “needs re-indexing”: until that is done, search quality will be lower. The application warns about it.

## Search two ways at once

Search runs simultaneously by meaning and by words, and the results are merged and ranked. That is how both the question “how much is a haircut” and the exact wording from the price list are found. The best fragments found are shown with a score and a link to the document they came from — you can see what the answer is based on.

## Answers from documents

The “AI chat” node can answer strictly from the base and attach sources. A mandatory “no answer” rule applies here: if nothing suitable was found in the base, the bot does not invent — it says so honestly and can, for example, call an operator. That is what protects you from wrong promises to a client.

## The gaps report

The application collects the questions that found no answer in the base and shows them with a count over the last thirty days. It is a ready list of what is worth adding to the documents. From any question you can create a draft answer in one click and index it right away.

## Testing the base

The “Knowledge” section has a testing window: ask any question and see which document fragments were found, with what score, and what the model would have answered. So answer quality is visible before clients ever see it — you can calmly extend the documents and check again.

## Refining with scripts

If an export arrives with extra markup or junk, the document’s text can be run through your own Python script before it is split into fragments — it cleans or rewrites the document. The second place for a script is the order of what was found: after the search, a script can refine which fragments matter more and drop the extra ones. Both are optional and configured separately; details in the [Scripts](/agents/chatbot/docs/scripts)
 section.

Next: how to configure models — in the [Models & Prompts](/agents/chatbot/docs/models)
 section, how to use search in the scenario — in [Flow Nodes](/agents/chatbot/docs/nodes)
, and how to put it all into a bot — in [Bots](/agents/chatbot/docs/bots)
.

[← Back to the documentation index](/agents/chatbot/docs)