Practical overview
When it fits
Use Rag Chunk when selecting chunk boundaries for a document corpus and you want to compare strategies against a question set before building or updating a RAG index.
Who it is for
- Developers preparing Markdown or text corpora for retrieval-augmented generation.
- Teams comparing chunking approaches against a set of representative questions.
Documented uses
- Clean documents before using them in a RAG pipeline.
- Compare fixed-size, overlapping, paragraph, recursive-character, header-aware and semantic splitting.
- Evaluate retrieved chunks with precision, recall and F1 metrics using a question file.
- Export analysis results as a table, JSON or CSV.
Requirements & limits
The project is distributed as a Python package and identifies its license as MIT. Optional feature groups are available for tiktoken, semantic functionality and LangChain splitting.
- The CLI analyzes folders of Markdown and text files; it is not a hosted RAG application or vector-store integration.
- A supplied JSON question file enables retrieval evaluation; strategy analysis can also be run without one.
- The semantic strategy and embedding-based retrieval depend on sentence-transformers; tiktoken and recursive-character splitting also have separate optional dependencies.
- Output can be viewed as a table or exported to JSON or CSV.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Compare chunking strategies against a question set
Analyze a document folder with multiple strategies and semantic retrieval, then view results in the CLI’s default table output.
Before you start
- Python environment with rag-chunk installed, including the semantic extra for embedding retrieval.
- A folder of documents and a JSON test file containing questions.
Steps
Install the tool with the command below.
pip install rag-chunk[semantic]Run
rag-chunk analyze --strategy all --use-embeddings --test-file --top-k 3, substituting the document folder and test-file paths.Review the comparison and retrieval metrics in the CLI output.
Expected outcome
A comparison of the available chunking strategies against the supplied questions, with retrieval metrics.
Guide limitations
- The test file must be provided by the user; its required JSON schema is not included in the available instructions.
- Semantic retrieval requires the semantic dependencies.
Compare options for Retrieval-augmented applications
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Rag Chunk | Clean documents before using them in a RAG pipeline. | CLI | The project is distributed as a Python package and identifies its license as MIT. Optional feature groups are available for tiktoken, semantic functionality and LangChain splitting. |
| Langsearch | Crawl website or local data for a knowledge base. | CLI, Self-hosted, Server | |
| Hybrid Search Rag | Combine document retrieval and language-model generation in a configurable workflow. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 13 October 2026
- Original website title
- messkan/rag-chunk · Official homepage title · retained in its source language