{"data":{"slug":"shantanu-deshmukh-chunktuner","name":"chunktuner","tagline":"Benchmark and optimize chunking strategies for RAG corpus","github_url":"https://github.com/shantanu-deshmukh/chunktuner","owner":"shantanu-deshmukh","repo":"chunktuner","owner_avatar_url":"https://avatars.githubusercontent.com/u/2664513?v=4","primary_language":"Python","stars":2,"forks":0,"topics":["chunking","embedding","evaluation","langchain","litellm","llamaindex","llm","mcp","optimization","rag","ragas","retrieval","text-splitting","vector-database"],"archived":false,"github_pushed_at":"2026-06-21T06:26:13+00:00","maintenance_label":"Active","url":"https://www.graphcanon.com/tools/shantanu-deshmukh-chunktuner","markdown_url":"https://www.graphcanon.com/tools/shantanu-deshmukh-chunktuner.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/shantanu-deshmukh-chunktuner","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=shantanu-deshmukh-chunktuner","description":"Benchmark chunking strategies for your RAG corpus. 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Includes tools for recommendation, evaluation, and optimization of configurations.","readme_excerpt":"# Install (pick one)\nuv tool install chunktuner\npip install chunktuner\n\n---\n\n# See cost estimate before running anything\nchunk-tune estimate ./my_docs --use-case rag_qa\n\n---\n\n## Installation options\n\n```bash\npip install chunktuner                 # CLI + library\nuv add chunktuner                      # library\nuv tool install chunktuner             # global CLI\nuvx --from chunktuner chunk-tune …     # ephemeral CLI (no install)","github_created_at":"2026-05-02T06:35:01+00:00","created_at":"2026-07-11T23:05:12.5387+00:00","updated_at":"2026-07-12T03:20:47.931086+00:00","categories":[{"slug":"data-retrieval","name":"Data & Retrieval","url":"https://www.graphcanon.com/categories/data-retrieval","markdown_url":"https://www.graphcanon.com/categories/data-retrieval.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/data-retrieval"},{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"}],"tags":[{"slug":"chunking","name":"chunking"},{"slug":"embedding","name":"embedding"},{"slug":"evaluation","name":"evaluation"},{"slug":"langchain","name":"langchain"},{"slug":"litellm","name":"litellm"},{"slug":"llamaindex","name":"llamaindex"},{"slug":"llm","name":"llm"},{"slug":"optimization","name":"optimization"}],"trust":{"provenance":{"is_fork":false,"github_id":1227063460,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-11T23:05:23.037Z","maintenance":{"label":"Active","score":82,"methodology":"github_public_v1","releases_90d":1,"days_since_push":20,"last_release_at":"2026-05-10T18:41:13Z"},"security_summary":{"status":"findings","scanner":"mcp_manifest@v1","low_count":2,"high_count":0,"last_scan_at":"2026-07-11T23:05:23.559Z","medium_count":0,"scan_profile":"mcp_manifest","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-12T03:20:10.657Z"},"has_cli":{"value":true,"source":"pyproject.toml:[project.scripts]","observed_at":"2026-07-12T03:20:10.657Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-07-12T03:20:10.657Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-07-12T03:20:10.657Z"}},"decision_facts":{"hosting":null,"pricing":{"model":"freemium","summary":"Open source with an MIT license, offering free use for both personal and commercial projects. No costs beyond typical computing resources are implied by its usage."},"requirements":{},"constraints":{"pricing_model":"freemium"},"when_to_use":["- You are working specifically with retrieval-augmented generation (RAG) systems which require tailored optimization and evaluation.","- The need arises to evaluate different chunk sizes and configurations for your RAG corpus using an automated recommendation system."],"when_not_to_use":["- If you do not deal with RAG systems or if the nature of your workflow does not benefit from specific optimizations in text chunking strategies across a corpus.","- You are working on projects that don't necessitate evaluation and optimization at the level provided by 'chunktuner', such as simpler tasks that can be managed without extensive configuration tools."],"source":"enrich:decision_facts","observed_at":"2026-07-12T03:20:40.331Z"},"constraint_facets":{"pricing_model":"freemium"},"decision_summary":[{"label":"Pricing","value":"freemium - Open source with an MIT license, offering free use for both personal and commercial projects. No costs beyond typical computing resources are implied by its usage."},{"label":"Adopt for","value":"A specialized benchmarking suite for optimizing chunking strategies in RAG corpora, offering a comprehensive toolkit inclusive of CLI and server components."}]}}