ChunkTuner

PyPI version Python versions License: MIT CI Docs

Auto chunking tuner and MCP server for RAG pipelines.

Give it your documents. It tries multiple chunking strategies, measures which setup supports retrieval best, and recommends a configuration for your corpus and use case. Zero API cost to start — run estimate for a dry-run before any paid calls.

Full documentation: shantanu-deshmukh.github.io/chunktuner

flowchart TD
    Lib["Python library"] --> Ingest
    CLI["CLI (chunk-tune)"] --> Ingest
    MCP["MCP server"] --> Ingest

    Ingest["Ingest your documents<br/>files, URLs, repos"] --> Tune

    subgraph Tune ["AutoTuner: for every strategy and param set"]
        direction LR
        Chunk["Chunk document"] --> Embed["Embed chunks<br/>and queries"] --> Score["Score retrieval<br/>recall, MRR, NDCG"]
    end

    Tune --> Rank["Rank all configs<br/>against baseline"] --> Best(["Recommended config<br/>.autochunk.yaml"])

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What it does

When building a RAG pipeline, how you split documents into chunks directly impacts retrieval quality. chunktuner automates the process of finding the optimal chunking strategy for your specific corpus, embedding model, and use case.

It benchmarks strategies like fixed-token windows, recursive character splitting, semantic splitting, PDF structural chunking, and AST-based code chunking — then scores each one against real retrieval metrics (token recall, MRR, NDCG) and optional generation metrics (RAGAS faithfulness, answer relevancy).

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Interfaces

• Python library — programmatic integration into your pipeline
• CLI (chunk-tune) — human-driven tuning from the terminal
• MCP server — use directly from Claude Desktop or any MCP host

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Quickstart

# Install (pick one)
uv tool install chunktuner
pip install chunktuner

# Initialize workspace (embedding_model defaults to null — no API calls)
chunk-tune init

# See cost estimate before running anything
chunk-tune estimate ./my_docs --use-case rag_qa

# Get a recommendation (dummy embeddings by default; add --embedding-model for real ones)
chunk-tune recommend ./my_docs --use-case rag_qa

Python API:

from pathlib import Path
from chunktuner import FileIngestor, DummyEmbeddingFunction, LiteLLMEmbeddingFunction, AutoTuner
from chunktuner import default_registry, Evaluator, ScoreCalculator

docs = FileIngestor().ingest_dir(Path("./my_docs"))

# Free/offline: use dummy embeddings for quick strategy comparison.
# Swap in LiteLLMEmbeddingFunction for real embeddings with any provider:
#   LiteLLMEmbeddingFunction("text-embedding-3-small")          # OpenAI
#   LiteLLMEmbeddingFunction("gemini/gemini-embedding-001")     # Google
#   LiteLLMEmbeddingFunction("openai/<id>", api_base="http://localhost:1234/v1")  # local
embedding_fn = DummyEmbeddingFunction()

tuner = AutoTuner(
    strategies=default_registry,
    evaluator=Evaluator(embedding_fn),
    scorer=ScoreCalculator(use_case="rag_qa"),
)
result = tuner.recommend(docs, use_case="rag_qa")
print(result.best.config)

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Example output

After running recommend, you get a ranked table with the winning config and how much it beats the baseline:

  Rank   Strategy              Params                  Score   Recall   MRR    IOU   AvgTok
 ────────────────────────────────────────────────────────────────────────────────────────
   1 ★   recursive_character   1024 chr / 154 ov        0.821    0.950  0.880  0.062      212
     2   fixed_tokens          512 tok / 51 ov           0.764    0.920  0.840  0.059      444
   ...
  Baseline  fixed_tokens  512 tok / 0 ov  →  score 0.682
  Winner beats baseline by +0.139  (+20.4%)

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Real-world example

See examples/financial_analysis for a full benchmark on S&P 500 earnings call transcripts — a corpus where separator choice and chunk size make a measurable difference in retrieval quality.

Run it offline with zero API cost:

cd examples/financial_analysis
uv sync
uv run python run_benchmark.py --fixture --num-transcripts 2

---

Supported strategies

Strategy	Best for
fixed_tokens	Baseline; uniform token windows
recursive_character	General prose and documentation
semantic	Theme-heavy articles
markdown_semantic	Structured Markdown docs
pdf_structural	PDFs with layout regions and tables
structural_semantic	PDF/DOCX with mixed layout and text
late_chunking	Long docs with dense cross-references
agentic	High-value narrative documents
code_ast	Code repos (Python, JavaScript)
code_window	Code baseline (sliding window)

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MCP server (Claude Desktop)

Python FastMCP (chunk-tune-mcp, stdio). No Node.js build. See docs/mcp_setup.md.

Add to your .mcp.json:

{
  "mcpServers": {
    "chunktuner": {
      "command": "uvx",
      "args": ["--from", "chunktuner[mcp]", "chunk-tune-mcp"],
      "env": {
        "CHUNK_TUNER_BASE_DIR": "/path/to/your/corpus"
      }
    }
  }
}

Tools available: list_strategies, preview_chunks, evaluate_chunking, recommend_config.

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CLI reference

chunk-tune init       Bootstrap workspace config
chunk-tune analyze    Quick structural scan (no API cost)
chunk-tune estimate   Dry-run cost/token estimate
chunk-tune evaluate   Full evaluation across strategies
chunk-tune recommend  Evaluation + best config recommendation
chunk-tune compare    Side-by-side comparison of specific strategies
chunk-tune preview    Inspect how a strategy splits a document
chunk-tune cache      Manage embedding and chunk cache

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Installation options

pip install chunktuner                 # CLI + library
uv add chunktuner                      # library
uv tool install chunktuner             # global CLI
uvx --from chunktuner chunk-tune …     # ephemeral CLI (no install)

# With optional extras
pip install "chunktuner[docling]"      # PDF/DOCX support
uv add "chunktuner[docling]"           # PDF/DOCX support
uv add "chunktuner[ragas]"             # generation metrics
uv add "chunktuner[semantic]"          # semantic chunking
uv add "chunktuner[code]"              # AST code chunking
uv add "chunktuner[all]"               # everything

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Contributing

See CONTRIBUTING.md.

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Author

Shantanu Deshmukh — full stack developer building E2E AI applications.

LinkedIn / Twitter