chunking-strategy

Optimal chunking strategies for RAG systems and document processing pipelines. Provides chunking strategies for RAG systems, vector databases, and document processing. Recommends chunk sizes, overlap percentages, and boundary detection methods; validates semantic coherence; evaluates retrieval metrics. Use when building or optimizing RAG systems, vector search pipelines, document chunking workflows, or performance-tuning existing systems with poor retrieval quality. Select based on document type and use case: Fixed-Size Chunking (Level 1) Recursive Character Chunking (Level 2) Structure-Aware Chunking (Level 3) Semantic Chunking (Level 4) Advanced Methods (Level 5) Reference: references/strategies.md . Pre-process documents Select parameters Process and validate Evaluate and iterate Reference: references/implementation.md . Run validation commands to assess chunk quality: Reference: references/evaluation.md . - Five strategy levels from fixed-size to advanced methods (late chunking, contextual retrieval), each suited to different document types and complexity - Includes recursive character chunking with hierarchical separators, structure-aware chunking for Markdown/code/PDFs, and embedding-based semantic chunking with configurable thresholds - Provides evaluation framework covering retrieval precision, recall, end-to-end accuracy, processing latency, and resource usage - Best...