# code-splitter Python Bindings
[](https://github.com/wangxj03/code-splitter/blob/main/LICENSE)
[](https://pypi.org/project/code-splitter/)
The `code-splitter` Python package provides bindings for the [code-splitter](https://crates.io/crates/code-splitter) Rust crate. It leverages the [tree-sitter](https://tree-sitter.github.io/tree-sitter/) parsing library and tokenizers to split code into semantically meaningful chunks. This functionality is particularly useful in Retrieval Augmented Generation (RAG), a technique that enhances the generation capabilities of Large Language Models (LLMs) by leveraging external knowledge sources.
## Installation
You can install the package from PyPI:
```shell
pip install code-splitter
```
## Usage
Here's an example of how to use the package:
```python
from code_splitter import Language, CharSplitter
# Load the code you want to split
with open("example.py", "rb") as f:
code = f.read()
# Create a splitter instance
splitter = CharSplitter(Language.Python, max_size=200)
# Split the code into chunks
chunks = splitter.split(code)
# Print the chunks
for chunk in chunks:
print(f"Start: {chunk.start}, End: {chunk.end}, Size: {chunk.size}")
print(chunk.text)
print()
```
This example uses the `CharSplitter` to split Python code into chunks of maximum 200 characters. The `Chunk` objects contain information about the start and end lines, size, and the actual text of the chunk.
### Available Splitters
The package provides the following splitters:
- `CharSplitter`: Splits code based on character count.
- `WordSplitter`: Splits code based on word count.
- `TiktokenSplitter`: Splits code based on Tiktoken tokenizer.
- `HuggingfaceSplitter`: Splits code based on HuggingFace tokenizers.
### Supported Languages
The following programming languages are currently supported:
- Golang
- Markdown
- Python
- Rust
## Examples
Here are some examples of splitting code using different splitters and languages:
### Split Python Code by Characters
```python
from code_splitter import Language, CharSplitter
splitter = CharSplitter(Language.Python, max_size=200)
chunks = splitter.split(code)
```
### Split Markdown by Words
```python
from code_splitter import Language, WordSplitter
splitter = WordSplitter(Language.Markdown, max_size=50)
chunks = splitter.split(code)
```
### Split Rust Code by Tiktoken Tokenizer
```python
from code_splitter import Language, TiktokenSplitter
splitter = TiktokenSplitter(Language.Rust, max_size=100)
chunks = splitter.split(code)
```
### Split Go Code by HuggingFace Tokenizer
```python
from code_splitter import Language, HuggingfaceSplitter
splitter = HuggingfaceSplitter(Language.Golang, max_size=100, pretrained_model_name_or_path="bert-base-cased")
chunks = splitter.split(code)
```
For more examples, please refer to the [tests](https://github.com/wangxj03/code-splitter/tree/main/bindings/python/tests) directory in the repository.
## Contributing
Contributions are welcome! Please feel free to submit issues or pull requests.
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"description": "# code-splitter Python Bindings\n\n[](https://github.com/wangxj03/code-splitter/blob/main/LICENSE)\n[](https://pypi.org/project/code-splitter/)\n\nThe `code-splitter` Python package provides bindings for the [code-splitter](https://crates.io/crates/code-splitter) Rust crate. It leverages the [tree-sitter](https://tree-sitter.github.io/tree-sitter/) parsing library and tokenizers to split code into semantically meaningful chunks. This functionality is particularly useful in Retrieval Augmented Generation (RAG), a technique that enhances the generation capabilities of Large Language Models (LLMs) by leveraging external knowledge sources.\n\n## Installation\n\nYou can install the package from PyPI:\n\n```shell\npip install code-splitter\n```\n\n## Usage\n\nHere's an example of how to use the package:\n\n```python\nfrom code_splitter import Language, CharSplitter\n\n# Load the code you want to split\nwith open(\"example.py\", \"rb\") as f:\n code = f.read()\n\n# Create a splitter instance\nsplitter = CharSplitter(Language.Python, max_size=200)\n\n# Split the code into chunks\nchunks = splitter.split(code)\n\n# Print the chunks\nfor chunk in chunks:\n print(f\"Start: {chunk.start}, End: {chunk.end}, Size: {chunk.size}\")\n print(chunk.text)\n print()\n```\n\nThis example uses the `CharSplitter` to split Python code into chunks of maximum 200 characters. The `Chunk` objects contain information about the start and end lines, size, and the actual text of the chunk.\n\n### Available Splitters\n\nThe package provides the following splitters:\n\n- `CharSplitter`: Splits code based on character count.\n- `WordSplitter`: Splits code based on word count.\n- `TiktokenSplitter`: Splits code based on Tiktoken tokenizer.\n- `HuggingfaceSplitter`: Splits code based on HuggingFace tokenizers.\n\n### Supported Languages\n\nThe following programming languages are currently supported:\n\n- Golang\n- Markdown\n- Python\n- Rust\n\n## Examples\n\nHere are some examples of splitting code using different splitters and languages:\n\n### Split Python Code by Characters\n\n```python\nfrom code_splitter import Language, CharSplitter\n\nsplitter = CharSplitter(Language.Python, max_size=200)\nchunks = splitter.split(code)\n```\n\n### Split Markdown by Words\n\n```python\nfrom code_splitter import Language, WordSplitter\n\nsplitter = WordSplitter(Language.Markdown, max_size=50)\nchunks = splitter.split(code)\n```\n\n### Split Rust Code by Tiktoken Tokenizer\n\n```python\nfrom code_splitter import Language, TiktokenSplitter\n\nsplitter = TiktokenSplitter(Language.Rust, max_size=100)\nchunks = splitter.split(code)\n```\n\n### Split Go Code by HuggingFace Tokenizer\n\n```python\nfrom code_splitter import Language, HuggingfaceSplitter\n\nsplitter = HuggingfaceSplitter(Language.Golang, max_size=100, pretrained_model_name_or_path=\"bert-base-cased\")\nchunks = splitter.split(code)\n```\n\nFor more examples, please refer to the [tests](https://github.com/wangxj03/code-splitter/tree/main/bindings/python/tests) directory in the repository.\n\n## Contributing\n\nContributions are welcome! Please feel free to submit issues or pull requests.\n\n",
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