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Documentation Index

Fetch the complete documentation index at: https://docs.monobot.ai/llms.txt

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Overview

Knowledge Base actions allow your bot to work with structured data (categories, CSV, text files) for dynamic responses and data storage.
Knowledge Base
Adds a new row to a category (CSV file).

Parameters

  • Category name — target category (CSV file)
  • Delimiter — separator used in the CSV (e.g., comma (,))
  • CSV Row — row data to insert (must match column structure)

Output

New csv row successfully added.
Returns all content from a selected category.

Parameters

  • Category Name — category to retrieve data from
Searches for relevant results across categories.

Parameters

  • Query — search input text
  • CSV filtering — filter results using conditions
  • CSV sorting — sort results

Advanced

  • K-Num — number of results to return
  • Threshold — similarity threshold (0–1)
Lower values return more results (less strict).
Higher values return fewer, more relevant matches.
  • Search in output — include output field in search
  • Include — include specific fields
  • Exclude — exclude specific fields
Search multiple queries across selected categories.

Parameters

  • Queries — search input text
  • Categories — categories to search in

Advanced

  • K-Num for each category — number of results per category
  • Number of page — pagination control
  • Threshold — similarity threshold (0–1)
Controls how closely results must match the query.
Higher values = stricter matching.
Updates category content.

Parameters

  • Category Name — target category
  • Data / File Content — new content to replace existing data
Updates multiple records in a category.

Parameters

  • Category Name — target category
  • File Content — new data to apply

Options

  • Extension — file format (e.g., txt)
  • Append File Content — add content instead of replacing

Output

Knowledge Base was successfully updated.

How It Works

User → Flow → Knowledge Base Action → Result → Response

Notes

Categories behave like structured datasets (CSV or text-based).
Use Threshold + K-Num together to control result quality and volume.