📄 Markdown Header Splitter Actor Split Markdown content into structured chunks using header hierarchy This actor intelligently splits Markdown documents into semantically meaningful chunks based on header hierarchy. Built with LangChain's MarkdownHeaderTextSplitter, it preserves metadata and structure for downstream applications like RAG systems, documentation processing, and content analysis. --- ## 🚀 Key Features ✅ Header-Based Chunking Split content at specified header levels (e.g., #, ##, ###). ✅ Metadata Preservation Each chunk includes hierarchical header metadata for context tracking. ✅ Flexible Configuration - Choose which headers to split on - Strip headers from content or retain them - Works with any Markdown content ✅ RAG-Ready Output Chunks are formatted for direct use in vector databases or LLM pipelines. --- ## 📝 Use Cases 1. RAG Systems Split long documents into context-aware chunks for: - Vector database indexing - Context window optimization - Knowledge retrieval 2. Documentation Processing Break technical docs into sections for: - Summarization - Translation - Search indexing 3. Content Analysis Analyze/report on document structure (e.g., API reference parsing). --- ## 🛠️ Getting Started ### Installation 1. Create an Apify account at apify.com 2. Navigate to Actor → Search for markdown-splitter 3. Run with Sample Input: json { "markdown_text": "# Title\n## Section 1\nContent...\n## Section 2\nMore content...", "headers_to_split_on": ["#", "##"], "strip_headers": true } ### Input Parameters | Field | Type | Description | |---------------------|---------------|-----------------------------------------------------------------------------| | markdown_text | string | Markdown content to split (required) | | headers_to_split_on | array | Header levels to split on (default: ["#", "##", "###", "####", "#####", "######"]) | | strip_headers | boolean | Remove headers from chunk content (default: true) | ### Output Format json { "chunks": [ { "content": "Section content here", "metadata": { "Header 1": "Title", "Header 2": "Section 1" } }, ... ] } --- ## 📚 Example Workflow: RAG System Integration 1. Input: Technical documentation in Markdown 2. Process: - Split into sections using ## headers - Strip headers to focus on content 3. Output: - Chunks with hierarchy metadata → Embed with OpenAI/LLama - Store in your favorite vector database like : ChromaDB/Pinecone/Weaviate for retrieval