Splitting Code with LangChain
The CodeTextSplitter
in LangChain provides a convenient way to split code into smaller pieces for feeding into large language models (LLMs) by auto-detecting the programming language syntax. This allows breaking up large code files into manageable chunks while preserving structure.
Usage
Import the CodeTextSplitter
and Language
enum:
from langchain.text_splitter import CodeTextSplitter, Language
Create a splitter by specifying the language:
python_splitter = CodeTextSplitter(language=Language.PYTHON)
See the full list of supported languages.
Customization
You can customize the splitter by setting the:
chunk_size
: Maximum chunk lengthoverlap
: Number of characters to overlap between chunksseparators
: List of custom separators
For example:
python_splitter = CodeTextSplitter(
language=Language.PYTHON,
chunk_size=100,
overlap=20,
separators=["\n\n", "\nclass"]
)
See the API docs for more details.
Examples
Here are examples splitting code in different languages:
Python
python_code = """
def hello_world():
print("Hello World!")
hello_world()
"""
python_docs = python_splitter.split(python_code)
Java
String helloWorld = "Hello World!";
System.out.println(helloWorld);
PHP
Go
package main
import "fmt"
func main() {
fmt.Println("Hello World!")
}
The splitter handles comments, strings, and code structure intelligently for each language.
Comparison to Other Splitters
The CodeTextSplitter is specialized for source code, compared to general purpose splitters like the RecursiveCharacterTextSplitter. It understands language syntax to split logically while preserving structure.
For non-code text, use a general splitter. For code, the CodeTextSplitter is recommended.
Conclusion
The CodeTextSplitter provides an easy way to split source code for feeding into LLMs in an intelligent, language-aware manner. With support for many languages and customizable options, it is a valuable tool for working with large codebases.
See the API documentation for additional details.