autochunks 0.0.12

Autonomous Retrieval Optimization for RAG

AutoChunks

The Intelligent Data Optimization Layer for RAG Engineering

AutoChunks is a specialized engine designed to eliminate the guesswork from Retrieval-Augmented Generation (RAG). By treating chunking as an optimization problem rather than a set of heuristics, it empirically discovers the most performant data structures for your specific documents and retrieval models.

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🚀 Key Features

• The Vectorized Tournament: Runs parallel searches across 15+ strategy families (Recursive, Semantic, Layout-Aware) using NumPy-accelerated simulation.
• Adversarial Synthetic QA: Automatically generates "needle-in-a-haystack" QA pairs to test your data structure against real-world search intent.
• Multi-Objective Optimization: Align engineering with goals like Speed and Precision, Cost Efficiency, or Comprehensive Recall.
• Framework Native: Built-in bridges for LangChain, LlamaIndex, and Haystack.
• Enterprise Ready: Air-gap compatible, local model support, and SHA-256 binary fingerprinting.

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📦 Installation

Install the stable version from PyPI:

pip install autochunks

For GPU acceleration or RAGAS semantic evaluation, see the Advanced Installation Guide.

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🛠️ Usage

Launch the Dashboard

The easiest way to optimize is through the interactive visual dashboard:

autochunks serve

Navigate to http://localhost:8000 to start your first optimization run.

CLI Optimization

Search for the best plan directly from the terminal:

autochunks optimize --docs ./my_data_folder --mode light --objective balanced

Python API

from autochunk import AutoChunker

# Initialize and Discover the optimal plan
optimizer = AutoChunker(mode="light")
plan, report = optimizer.optimize(documents="./my_data", objective="balanced")

# Apply the winning strategy
chunks = plan.apply("./new_documents", optimizer)

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Development

If you want to contribute or build from source:

1. Clone the repository:

   git clone https://github.com/s8ilabs/AutoChunks.git
   cd AutoChunks
   

2. Setup virtual environment:

   python -m venv venv
   source venv/bin/activate  # venv\Scripts\activate on Windows
   

3. Install in editable mode:

   pip install -e .
   

4. Running Tests:

   pytest tests/
   

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📖 Documentation and Resources

• Full Documentation Portal
• PyPI Project Page
• Getting Started Guide
• The Optimization Lifecycle
• Metric Definitions and Scoring

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Developed with ❤️ for the RAG and LLM Community. AutoChunks is released under the Apache License 2.0.