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Hierarchical, semantic code chunking for AI systems

Project description

chunkhive

Semantic, hierarchical code chunking for AI systems

Chunkhive is a production-grade code chunking engine designed for modern AI workflows such as code embeddings, retrieval-augmented generation (RAG), agentic systems, and dataset synthesis.

It converts raw repositories into clean, structured, semantically accurate chunks with byte-level precision and preserved hierarchy.


🚀 Why chunkhive?

Modern AI systems need more than naive text splitting.

chunkhive provides:

  • AST-first semantic correctness
  • Hierarchical structure awareness
  • Byte-accurate spans
  • Robust parsing across real-world repositories

🧠 Core Principle

AST is the Authority, Tree-sitter is Enrichment

  • Primary source of truth: Language AST (semantic accuracy)
  • Fallback & enrichment: Tree-sitter (structural robustness)
  • Result: Maximum parsing success across diverse codebases

✨ Features

  • Semantic AST-first chunking (no filename-based chunks)
  • Preserves hierarchy: Module → Class → Method / Function
  • Accurate parent–child relationships
  • Byte-level precision (start_byte, end_byte)
  • Clean symbol naming (ast.name)
  • Import & decorator capture
  • Robust handling of edge cases (empty files, __init__.py)
  • Supports documentation + code chunking flows

🗂 Supported Chunk Types

  • module
  • class
  • method
  • function
  • documentation

📦 Installation

pip install chunkhive


[![PyPI](https://img.shields.io/pypi/v/chunkhive)](https://pypi.org/project/chunkhive/)
[![Python](https://img.shields.io/pypi/pyversions/chunkhive)](https://pypi.org/project/chunkhive/)
[![License](https://img.shields.io/pypi/l/chunkhive)](https://pypi.org/project/chunkhive/)


---

## 📦 Output Schema (Simplified)

```json
{
  "chunk_id": "ts_123_456",
  "file_path": "src/example.py",
  "chunk_type": "function",
  "language": "python",
  "code": "...",
  "ast": {
    "name": "my_function",
    "parent": "MyClass"
  },
  "span": {
    "start_byte": 123,
    "end_byte": 456,
    "start_line": 10,
    "end_line": 25
  },
  "metadata": {
    "byte_accuracy": "exact_bytes"
  }
}

🛠 Use Cases

Code embedding model training

RAG pipelines

Agentic AI systems

Code search & navigation

QA dataset generation

Static analysis & tooling

📜 License

Apache License 2.0 — free to use, modify, and distribute, including commercial use.

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