Upgrade your RAG pipeline with LlamaIndex's advanced chunking. Parse markdown while preserving structure, create semantic chunks, and get more accurate AI retrieval.
LlamaIndex analyzes your markdown documents and creates intelligent chunks that preserve structure and semantic meaning
LlamaIndex analyzes document structure: headers, lists, code blocks, paragraphs
Find natural semantic boundaries: section breaks, topic changes, complete thoughts
Split into self-contained chunks that preserve context and structure
AI finds complete, accurate context leading to better answers
Same markdown document, two different chunking approaches
Understands markdown structure: headers, lists, code blocks, tables. Preserves formatting and hierarchy.
Splits at natural boundaries, not arbitrary character limits. Each chunk is a complete, meaningful unit.
AI retrieves complete context with full sentences, code examples, and relevant sections intact.
Powered by LlamaIndex's proven node parsing algorithms used by thousands of RAG applications.
Designed for technical docs, API references, knowledge bases, and structured content.
Works with your existing LLM Knowledge setup. Install and your chunking automatically improves.
API docs, developer guides, installation instructions. Preserve code examples, command snippets, and configuration blocks.
Example: "How do I install the SDK?" → AI returns complete installation section with all commands intact
Company wikis, training materials, process documentation. Maintain hierarchical structure and complete procedures.
Example: "What's our deployment process?" → AI retrieves entire step-by-step procedure with all sub-steps
Tutorials, courses, learning materials. Keep examples and explanations together for better comprehension.
Example: "Explain recursion" → AI shows complete concept with code example and explanation in one chunk
Research papers, specifications, long-form content. Preserve section organization and maintain context across subsections.
Example: "What were the Q3 findings?" → AI retrieves complete quarterly section with all data points
pip install llama_index nltk
Apps → Search "LLM Knowledge LlamaIndex" → Install
Your existing knowledge collections now use LlamaIndex chunking. Process new resources to see improved accuracy.
Requires LLM Knowledge module. Works with any vector store (pgvector, Qdrant, Chroma).
Advanced RAG chunking for better AI retrieval accuracy
Email: info@apexive.com
GitHub: github.com/apexive/odoo-llm