About This Project

Project Vision

docpack_confluence is a bridge between Confluence knowledge bases and AI systems. It enables seamless integration of Confluence content with any AI platform, whether through API-based systems like RAG pipelines or simple drag-and-drop file uploads to AI knowledge bases.

The core idea is simple: make Confluence content AI-ready with minimal configuration.

The Three Pain Points We Solve

1. Precise Batch Selection

Confluence is not a traditional file system. Manually specifying which pages to include is tedious and error-prone. We solve this with a familiar, low-code approach:

  • gitignore-style syntax: Use include and exclude patterns just like .gitignore

  • Hierarchical wildcards: Use /* suffix to include a page and all its descendants

  • URL or ID support: Specify pages using their Confluence URLs or page IDs

Example configuration:

{
    "include": [
        "https://example.atlassian.net/wiki/spaces/KB/pages/123456/Getting+Started/*",
        "https://example.atlassian.net/wiki/spaces/KB/pages/789012/API+Reference"
    ],
    "exclude": [
        "https://example.atlassian.net/wiki/spaces/KB/pages/111111/Draft+Pages/*"
    ]
}

This approach eliminates the need to maintain a list of individual files. Add or remove entire sections with a single pattern.

Note

Currently, filtering is based on page hierarchy (parent-child relationships) using the /* syntax. Title-based filtering with DSL expressions may be added in future versions.

2. Rich Metadata Output

AI systems work best when they have context. Each exported page includes:

  • Source information: The original Confluence URL for citation and reference

  • Page title: For navigation and identification

  • Markdown content: Human-readable, AI-parseable content converted from Confluence’s Atlas Document Format

  • Hierarchical metadata: Breadcrumb paths showing the page’s position in the knowledge structure

The output format uses XML for metadata encapsulation with Markdown for content, providing both machine-parseability and human readability:

<document>
  <source_type>Confluence Page</source_type>
  <confluence_url>https://example.atlassian.net/wiki/spaces/KB/pages/123/Title</confluence_url>
  <title>Page Title</title>
  <markdown_content>
# Page Title

Your page content in Markdown format...
  </markdown_content>
</document>

3. Single-File Packaging

Managing multiple files is painful for AI knowledge bases:

  • Version tracking nightmare: Which files changed? Which need updating?

  • Sync complexity: Uploading dozens of files is error-prone

  • Update friction: Deleting and re-uploading many files is tedious

Our solution: pack everything into a single file.

  • Delete the old file, upload the new one - done

  • Perfect for platforms like Claude Projects, ChatGPT Knowledge, or any drag-and-drop AI system

  • Works equally well with API-based RAG systems

Whether your AI platform has an API or just accepts file uploads, a single consolidated file makes synchronization trivial.

How It Works

  1. Configure: Write a simple JSON configuration specifying your Confluence space and filtering rules

  2. Fetch: The tool connects to Confluence API and retrieves matching pages

  3. Transform: Pages are converted from Atlas Doc Format to Markdown with metadata

  4. Export: Each page becomes a structured XML file, optionally merged into one bundle

Confluence Space
     |
     v
[Filter with include/exclude patterns]
     |
     v
[Convert to Markdown + Metadata]
     |
     v
Individual XML files  -->  Single packed file
                            (optional)

Use Cases

  • RAG Pipelines: Feed Confluence documentation into vector databases for retrieval-augmented generation

  • AI Knowledge Bases: Upload company wikis to Claude Projects, ChatGPT, or similar platforms

  • Documentation AI: Create AI assistants that understand your internal documentation

  • Content Migration: Export Confluence content in a portable, structured format

Current Limitations

  • Page filtering is hierarchy-based only (using /* for descendants)

  • Title-based filtering with DSL expressions is planned for future releases

  • Requires Confluence Cloud with API access

Summary

docpack_confluence transforms Confluence from a static wiki into an AI-ready knowledge source. With simple JSON configuration and familiar gitignore-style patterns, you can:

  1. Select exactly the pages you need

  2. Export with rich metadata for AI consumption

  3. Package into a single file for easy synchronization

It’s the missing link between your Confluence knowledge base and the AI tools that can unlock its value.