{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hn-45936997","slug":"rag-chunk-a-cli-to-test-rag-chunking-strategies","name":"RAG-chunk – A CLI to test RAG chunking strategies","type":"cli","url":"https://github.com/messkan/rag-chunk","page_url":"https://unfragile.ai/rag-chunk-a-cli-to-test-rag-chunking-strategies","categories":["rag-knowledge","testing-quality"],"tags":["hackernews","show-hn"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hn-45936997__cap_0","uri":"capability://data.processing.analysis.multi.strategy.chunking.algorithm.comparison","name":"multi-strategy chunking algorithm comparison","description":"Implements and executes multiple text chunking strategies (fixed-size, semantic, recursive, sliding-window) against the same input document, allowing side-by-side comparison of how different chunking approaches segment content. The CLI loads documents, applies each strategy with configurable parameters, and outputs the resulting chunks for analysis. This enables developers to empirically evaluate which chunking strategy produces optimal retrieval performance for their specific RAG use case before deploying to production.","intents":["I need to test how different chunking strategies affect my RAG retrieval quality","I want to compare chunk overlap, size, and semantic coherence across multiple algorithms","I'm trying to find the optimal chunking parameters for my domain-specific documents","I need to visualize how different strategies handle document boundaries and structure"],"best_for":["ML engineers optimizing RAG pipelines","teams evaluating chunking strategies before production deployment","researchers benchmarking retrieval performance across chunking methods"],"limitations":["No built-in evaluation metrics — requires manual inspection or external evaluation framework","Limited to text documents; no native support for PDFs, images, or structured data","Chunking strategies are fixed implementations; custom strategy development requires code modification","No persistence of chunking results; each run is stateless"],"requires":["Node.js 14+ or Python 3.8+","Text input files in supported formats (plain text, markdown)","CLI environment with standard I/O capabilities"],"input_types":["plain text","markdown","raw document content"],"output_types":["structured chunk objects with metadata","JSON-formatted chunk arrays","CLI-formatted chunk display"],"categories":["data-processing-analysis","rag-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-45936997__cap_1","uri":"capability://data.processing.analysis.configurable.chunk.parameter.tuning","name":"configurable chunk parameter tuning","description":"Exposes chunking algorithm parameters (chunk size, overlap percentage, separator patterns, semantic similarity thresholds) as CLI flags or configuration files, allowing users to adjust strategy behavior without modifying source code. The tool parses configuration inputs, validates parameter ranges, and applies them to each chunking strategy execution. This enables rapid iteration on parameter values to optimize for specific document types, languages, or retrieval objectives.","intents":["I want to test how chunk size affects retrieval quality without rewriting code","I need to adjust overlap percentages to find the sweet spot for my documents","I'm experimenting with different separator patterns for domain-specific content","I want to batch-test multiple parameter combinations to find optimal settings"],"best_for":["data scientists tuning RAG hyperparameters","teams without deep ML infrastructure","rapid prototyping and experimentation workflows"],"limitations":["Parameter validation is basic; invalid combinations may produce unexpected results","No built-in parameter search or optimization algorithm (e.g., grid search, Bayesian optimization)","Configuration format may be tool-specific; not standardized across RAG frameworks","No automatic parameter recommendation based on document characteristics"],"requires":["CLI tool installed and accessible in PATH","Configuration file in supported format (JSON, YAML, or CLI flags)","Understanding of chunking algorithm parameters and their effects"],"input_types":["CLI flags","configuration files (JSON/YAML)","environment variables"],"output_types":["chunked text with applied parameters","parameter summary in output","configuration validation feedback"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-45936997__cap_2","uri":"capability://data.processing.analysis.document.chunking.with.metadata.preservation","name":"document chunking with metadata preservation","description":"Retains and propagates document metadata (source file, line numbers, section headers, document structure) through the chunking process, attaching this context to each output chunk. The implementation tracks chunk origins and relationships, enabling downstream retrieval systems to maintain document context and enable features like source attribution and hierarchical retrieval. Metadata is output alongside chunks in structured formats (JSON with metadata fields).","intents":["I need to know which source document and section each chunk came from for attribution","I want to preserve document hierarchy (chapters, sections) in my chunks","I need line numbers or character offsets for chunk-to-source mapping","I'm building a retrieval system that needs to return chunks with full context"],"best_for":["RAG systems requiring source attribution and traceability","document-heavy applications (legal, medical, technical documentation)","teams building retrieval systems with hierarchical context"],"limitations":["Metadata extraction is document-format dependent; limited to text/markdown","No automatic section detection; requires pre-structured documents or manual markup","Metadata overhead increases output size; may impact storage and transmission","Metadata schema is tool-specific; not standardized across RAG frameworks"],"requires":["Input documents with consistent structure or markup","Support for metadata fields in output format (JSON)","Downstream systems capable of consuming metadata"],"input_types":["structured text documents","markdown with headers","documents with line/section markers"],"output_types":["JSON with chunk and metadata fields","structured chunk objects with source references","CSV with chunk and metadata columns"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-45936997__cap_3","uri":"capability://data.processing.analysis.batch.document.chunking.and.export","name":"batch document chunking and export","description":"Processes multiple documents in a single CLI invocation, applying selected chunking strategies to each document and exporting results in bulk to files or structured formats. The tool handles directory traversal, file format detection, and batch output organization (e.g., one output file per input document, or consolidated output). This enables efficient processing of document collections without manual iteration or scripting.","intents":["I need to chunk an entire document collection for RAG ingestion","I want to process 100+ documents with consistent chunking parameters","I need to export chunks in a format ready for vector database ingestion","I'm preparing a dataset for RAG evaluation and need bulk chunking"],"best_for":["teams preparing document collections for RAG systems","data engineers building RAG data pipelines","batch processing workflows and scheduled jobs"],"limitations":["No streaming output; entire batch must complete before results are available","Memory usage scales with total document size; may struggle with very large collections","No built-in parallelization; single-threaded processing may be slow for large batches","Error handling is batch-level; one document failure may halt entire batch"],"requires":["CLI tool with batch processing support","Input directory or file list","Sufficient disk space for output files","Write permissions to output directory"],"input_types":["directory of text files","file list (manifest)","glob patterns"],"output_types":["JSON files (one per input document or consolidated)","CSV with all chunks","directory structure mirroring input"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-45936997__cap_4","uri":"capability://data.processing.analysis.interactive.chunking.strategy.visualization","name":"interactive chunking strategy visualization","description":"Displays chunking results in a human-readable format (CLI output, formatted tables, or interactive preview) showing how each strategy segments the input document, with visual indicators for chunk boundaries, overlap regions, and metadata. The implementation formats chunks with context (surrounding text, chunk indices) and may support interactive navigation through large chunk sets. This enables developers to visually inspect chunking quality and understand strategy behavior without parsing raw output.","intents":["I want to visually see how each chunking strategy breaks up my document","I need to understand where chunk boundaries fall and why","I want to inspect overlap regions to ensure semantic coherence","I'm debugging chunking behavior and need clear visual feedback"],"best_for":["developers iterating on chunking strategies","non-technical stakeholders evaluating chunking quality","rapid prototyping and experimentation"],"limitations":["Large documents may produce overwhelming output; pagination or filtering required","Terminal-based visualization is limited compared to graphical tools","No interactive exploration features (e.g., search, filter, drill-down) in basic implementation","Formatting may not preserve document structure (indentation, lists, tables)"],"requires":["Terminal with ANSI color support (optional, for colored output)","Reasonable document size for readable output","CLI tool with formatting support"],"input_types":["text documents","chunking results (JSON)"],"output_types":["formatted CLI output","colored/highlighted text","table-formatted chunks","HTML preview (if supported)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-45936997__cap_5","uri":"capability://data.processing.analysis.semantic.chunking.with.embedding.based.similarity","name":"semantic chunking with embedding-based similarity","description":"Implements semantic chunking by computing embeddings for text segments and grouping segments with high semantic similarity into chunks, rather than relying on fixed sizes or delimiters. The tool integrates with embedding models (local or API-based) to compute similarity scores and uses threshold-based or clustering algorithms to determine chunk boundaries. This produces chunks that are semantically coherent rather than arbitrary size-based splits, improving retrieval quality for RAG systems.","intents":["I want chunks that are semantically coherent, not just fixed-size blocks","I need to group related sentences or paragraphs together based on meaning","I'm using embeddings for retrieval and want chunks aligned with semantic boundaries","I want to avoid splitting semantically related content across chunk boundaries"],"best_for":["RAG systems using embedding-based retrieval","teams with access to embedding models","applications where semantic coherence is critical (QA, summarization)"],"limitations":["Requires embedding model (local or API); adds computational cost and latency","Embedding quality depends on model choice; may not work well for domain-specific text","Similarity threshold tuning is required; no automatic threshold selection","Slower than fixed-size chunking; not suitable for real-time streaming"],"requires":["Embedding model (e.g., sentence-transformers, OpenAI embeddings API)","Sufficient compute for embedding generation (GPU recommended for large documents)","API key if using cloud-based embeddings"],"input_types":["text documents","pre-computed embeddings (optional)"],"output_types":["semantically coherent chunks","chunks with similarity scores","chunk boundaries with semantic justification"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-45936997__cap_6","uri":"capability://data.processing.analysis.recursive.hierarchical.chunking.with.fallback","name":"recursive hierarchical chunking with fallback","description":"Implements recursive chunking that attempts to split documents using a hierarchy of delimiters (e.g., paragraphs → sentences → words) and falls back to smaller units if chunks exceed size limits. The algorithm respects document structure by preferring semantic boundaries (paragraph breaks) over arbitrary splits, and recursively applies the strategy until all chunks meet size constraints. This balances semantic coherence with size requirements, producing chunks that preserve document structure while meeting retrieval constraints.","intents":["I want chunks that respect document structure (paragraphs, sentences) but stay within size limits","I need to avoid splitting semantic units (sentences, paragraphs) across chunks","I'm chunking documents with varying structure and need adaptive splitting","I want a strategy that handles edge cases (very long sentences, no paragraph breaks)"],"best_for":["documents with clear hierarchical structure (articles, books, technical docs)","RAG systems requiring semantic coherence and size constraints","teams wanting a balanced approach between fixed-size and semantic chunking"],"limitations":["Requires well-defined delimiter hierarchy; may fail on unstructured text","Recursive depth and fallback behavior may produce unpredictable chunk sizes","Configuration requires understanding of document structure and delimiter choice","Performance degrades on documents with very long semantic units (e.g., long sentences)"],"requires":["Documents with consistent structure and delimiters","Configuration of delimiter hierarchy and size constraints","Understanding of target document format"],"input_types":["structured text documents","markdown with clear hierarchy","documents with consistent delimiters"],"output_types":["chunks respecting document structure","chunks with hierarchy metadata","fallback information (which delimiter was used)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-45936997__cap_7","uri":"capability://data.processing.analysis.sliding.window.chunking.with.configurable.stride","name":"sliding-window chunking with configurable stride","description":"Implements sliding-window chunking where a fixed-size window moves across the document with a configurable stride (step size), creating overlapping chunks. The tool allows tuning of window size and stride independently, enabling control over chunk overlap percentage and granularity. This produces dense, overlapping chunks useful for retrieval systems where context around query terms is important, and enables fine-grained control over coverage and redundancy.","intents":["I want overlapping chunks to ensure query terms aren't split across boundaries","I need to control the overlap percentage between chunks independently of size","I'm building a retrieval system that benefits from redundant context","I want to tune chunk density and coverage for my specific use case"],"best_for":["retrieval systems where context around query terms is critical","dense document collections requiring high coverage","teams tuning chunk overlap for specific retrieval patterns"],"limitations":["Overlapping chunks increase storage and indexing overhead","Stride tuning requires experimentation; no automatic optimization","May produce redundant chunks with minimal new information","Less effective for documents with clear semantic boundaries"],"requires":["Configuration of window size and stride","Understanding of overlap percentage calculation","Storage capacity for overlapping chunks"],"input_types":["text documents","any document format"],"output_types":["overlapping chunks with position metadata","chunks with overlap percentage","stride and window size in output"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+ or Python 3.8+","Text input files in supported formats (plain text, markdown)","CLI environment with standard I/O capabilities","CLI tool installed and accessible in PATH","Configuration file in supported format (JSON, YAML, or CLI flags)","Understanding of chunking algorithm parameters and their effects","Input documents with consistent structure or markup","Support for metadata fields in output format (JSON)","Downstream systems capable of consuming metadata","CLI tool with batch processing support"],"failure_modes":["No built-in evaluation metrics — requires manual inspection or external evaluation framework","Limited to text documents; no native support for PDFs, images, or structured data","Chunking strategies are fixed implementations; custom strategy development requires code modification","No persistence of chunking results; each run is stateless","Parameter validation is basic; invalid combinations may produce unexpected results","No built-in parameter search or optimization algorithm (e.g., grid search, Bayesian optimization)","Configuration format may be tool-specific; not standardized across RAG frameworks","No automatic parameter recommendation based on document characteristics","Metadata extraction is document-format dependent; limited to text/markdown","No automatic section detection; requires pre-structured documents or manual markup","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36,"quality":0.26,"ecosystem":0.56,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.691Z","last_scraped_at":"2026-05-04T08:10:07.465Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=rag-chunk-a-cli-to-test-rag-chunking-strategies","compare_url":"https://unfragile.ai/compare?artifact=rag-chunk-a-cli-to-test-rag-chunking-strategies"}},"signature":"+1/K3GzhHsXWRBi3AmvoU/yvzgpkihCnwSTg5go+JbsWjYJIutCxYDXQARnZyORu21SyKg0hor0Cu2U90quPDA==","signedAt":"2026-06-21T01:47:08.937Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/rag-chunk-a-cli-to-test-rag-chunking-strategies","artifact":"https://unfragile.ai/rag-chunk-a-cli-to-test-rag-chunking-strategies","verify":"https://unfragile.ai/api/v1/verify?slug=rag-chunk-a-cli-to-test-rag-chunking-strategies","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}