Chunking Advisor

About

Chunking Advisor is a specialized utility for RAG developers designed to optimize document segmentation for better AI retrieval. It analyzes various content types—ranging from technical documentation and legal contracts to product catalogs and structured tables—to suggest the most effective chunking strategy, size, and overlap based on your specific embedding model. By providing implementation-ready code snippets and decision trees, it helps engineers eliminate retrieval noise, preserve semantic context, and build more reliable AI search systems.

Key Features

• Context-aware chunking recommendations for diverse document types
• Ready-to-use Python implementation code for LangChain and custom logic
• Diagnostic checklists to identify and fix poor retrieval performance
• 17 GitHub stars
• Hierarchical and semantic chunking strategies for complex data structures
• Embedding model-specific optimization for OpenAI, Voyage, and Cohere

Use Cases

• Adjusting chunk sizes and overlap when switching to new embedding models
• Optimizing retrieval accuracy in an existing RAG-based application
• Configuring initial indexing for diverse document sets like PDFs, code, and legal text