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Data sources FAQ

This document addresses common questions about data sources in Kapa.ai.

Does Kapa support ingesting source code?​

No, Kapa does not currently support ingesting source code directly. The platform is designed for knowledge articles such as documentation, help guides, and support content.

Source code ingestion involves different challenges than processing documentation text and isn't part of the core product at present.

Alternative: Consider ingesting documented code examples, tutorials, or explanatory documentation instead of raw source code.

Does adding an LLMs.txt file improve accuracy?​

Kapa does not currently support the LLMs.txt standard. The platform already includes built-in features that address similar goals:

  • Project-specific context configuration
  • Retrieval mechanisms for finding relevant sources
  • Optimized parsing of documentation content

Kapa's existing functionality provides solutions for accuracy without requiring this file. The platform may consider supporting LLMs.txt if it becomes more widely adopted in the industry.

Does Kapa support Jira Service Management issues?​

Yes, the Jira data source covers issues from Jira Service Management (JSM). When connecting Jira as a data source:

  • Ensure the authentication token includes permissions for JSM projects
  • Kapa ingests JSM issues along with standard Jira issues
  • Standard Jira issue fields are fully supported as documented

While JSM adds special fields to support customer-facing tickets, Kapa focuses on processing the standard Jira issue fields that are common across both systems.

How should I format markdown files for AI ingestion?​

For optimal AI comprehension, markdown files must be properly formatted with clear structure and appropriate use of markdown elements. Poorly formatted documents significantly degrade answer quality.

Required formatting​

  • Use proper heading hierarchy (# Title, ## Section, ### Subsection)
  • Include meaningful headings that describe the content
  • Use standard markdown syntax for lists, code blocks, and emphasis
  • Avoid documents that are purely code blocks without explanatory text

Common formatting issues to avoid​

  • Documents without any headings or structure
  • Files containing only code blocks with no context or explanation
  • Pseudo-structured formats (like key-value pairs) instead of proper markdown
  • Content where meaning depends on visual formatting rather than semantic structure

Example: Support ticket format​

Instead of unstructured key-value pairs:

Title: Connection timeout issue
Description: Customer experiencing timeouts when connecting to API
Tags: api, timeout, connectivity
CreatedBy: user@example.com

Use proper markdown structure:

# Support Ticket: Connection timeout issue

**Timestamp**: 2025-01-15 14:30 UTC  

**Status**: Resolved  

**Tags**: api, timeout, connectivity

## Issue description

Customer experiencing timeouts when connecting to API endpoint.

## Conversation

**User**: I'm getting timeout errors when trying to connect to the API.

**Agent**: I can help you troubleshoot the connection timeout. Let me check...

For detailed guidance on writing content optimized for AI systems, see our comprehensive Writing documentation for AI: best practices guide.