Semantic Chunking Guide

One of the most important techniques for working with large codebases in Claude 3.7 Sonnet is semantic chunking. This guide explains what semantic chunking is, why it's important, and how to use it effectively.

What is Semantic Chunking?

Semantic chunking is the process of dividing large codebases into smaller, semantically meaningful segments before sending them to an LLM like Claude 3.7 Sonnet. Unlike simple character or line-based chunking, semantic chunking preserves the logical structure and context of the code.

Key Characteristics:

• Preserves semantic units: Functions, classes, and methods stay intact
• Respects code boundaries: Doesn't break in the middle of syntax structures
• Maintains context: Related code stays together
• Optimizes for token limits: Chunks fit within model context windows

Why Semantic Chunking Matters

1. Better Code Understanding

When code is chunked semantically, the model receives complete logical units, improving its understanding of the codebase.

Example: Consider this function in Python:

def process_user_data(user_id, data):
    """
    Process user data and update database records.
    
    Args:
        user_id: The user identifier
        data: Dict containing user information
        
    Returns:
        Updated user record or None if failed
    """
    # Validate inputs
    if not isinstance(user_id, str) or not user_id:
        raise ValueError("Invalid user ID")
    
    if not isinstance(data, dict):
        raise TypeError("Data must be a dictionary")
    
    # Update database
    try:
        user = database.get_user(user_id)
        if not user:
            return None
        
        # Apply updates
        for key, value in data.items():
            if key in user.editable_fields:
                setattr(user, key, value)
        
        # Save changes
        database.save_user(user)
        return user
    except DatabaseError as e:
        logger.error(f"Database error: {e}")
        return None

With semantic chunking, this entire function is kept as a single chunk, preserving its logic and structure. With naive chunking, it might be split in the middle, making it harder for the model to understand.

2. Reduced Token Waste

Semantic chunking minimizes token waste by ensuring related code is kept together, reducing duplication and improving the efficiency of your queries.

3. More Accurate Responses

By providing complete logical units, the model can provide more accurate and relevant responses to your questions about the code.

How CodeCompass Implements Semantic Chunking

CodeCompass uses language-specific parsers to identify semantic units in code:

1. Language Detection: Identifies the programming language based on file extension
2. Parse Tree Analysis: Analyzes the syntax structure of the code
3. Semantic Unit Identification: Identifies functions, classes, methods, and other semantic units
4. Chunking Logic: Creates chunks that preserve these semantic units while respecting token limits
5. Metadata Annotation: Adds metadata to chunks for better context management

Using Semantic Chunking in CodeCompass

Basic Usage

Semantic chunking is enabled by default in CodeCompass. When processing a file or directory:

codecompass process path/to/your/code

Customizing Chunking Options

You can customize the chunking behavior:

codecompass process --chunk-size 10000 --chunk-overlap 300 --semantic-chunking path/to/your/code

To disable semantic chunking and use simple chunking instead:

codecompass process --no-semantic-chunking path/to/your/code

Configuration File Options

In your configuration file, you can set these options:

{
  "chunk_size": 8000,
  "chunk_overlap": 200,
  "semantic_chunking": {
    "enabled": true,
    "language_parsers": {
      ".py": "python",
      ".js": "javascript",
      ".ts": "typescript",
      ".java": "java",
      ".cpp": "cpp"
    }
  }
}

Supported Languages

CodeCompass currently supports semantic chunking for these languages:

Language	Extensions	Parser
Python	.py	Built-in AST
JavaScript	.js, .jsx	Tree-sitter
TypeScript	.ts, .tsx	Tree-sitter
Java	.java	Tree-sitter
C++	.cpp, .h, .cc, .hpp	Tree-sitter
Go	.go	Tree-sitter
Ruby	.rb	Tree-sitter
Rust	.rs	Tree-sitter

For other languages, CodeCompass falls back to a syntax-aware heuristic chunker.

Best Practices

1. Choose the Right Chunk Size

For most codebases, a chunk size of 4,000-8,000 characters works well. This balances context preservation with token efficiency.

2. Use Appropriate Overlap

An overlap of 200-500 characters helps maintain context between chunks. Larger overlaps might be needed for complex code.

3. Adjust Based on Language

Different languages may benefit from different chunking parameters:

• For languages with verbose syntax (like Java), larger chunks may be more effective
• For dense languages (like Python), smaller chunks might suffice

4. Use Metadata for Context

When querying the model, include relevant metadata about the chunk:

# Example of using chunk metadata in a prompt
prompt = f"""
This code is from {chunk['file_path']}, which is {chunk['type']} 
and has the name {chunk.get('name', 'unknown')}.

Please analyze this code and explain its purpose:

{chunk['content']}
"""

Advanced Techniques

Custom Chunking Rules

You can define custom chunking rules for specific file types:

from codecompass.chunking import SemanticChunker, register_parser

@register_parser('.custom')
def custom_file_parser(content, file_path):
    """Custom parser for .custom files"""
    chunks = []
    # Custom chunking logic here
    return chunks

# Use the custom parser
chunker = SemanticChunker()
chunks = chunker.chunk_file('my_file.custom')

Chunk Visualization

CodeCompass includes tools to visualize how your code is being chunked:

codecompass visualize --output chunks.html path/to/your/code

This generates an HTML file that shows how your code was divided into chunks, with different colors for different semantic units.

Example: Analyzing a Large Python Module

Here's a complete example of analyzing a large Python module:

from codecompass import CodeCompass

# Initialize CodeCompass
compass = CodeCompass()

# Process a large Python module
chunks = compass.chunk_file('large_module.py')

# Print chunk information
for i, chunk in enumerate(chunks):
    print(f"Chunk {i+1}:")
    print(f"  Type: {chunk['type']}")
    if 'name' in chunk:
        print(f"  Name: {chunk['name']}")
    print(f"  Size: {chunk['size']} characters")
    print(f"  Content preview: {chunk['content'][:100]}...")
    print()

# Ask a question about the module
response = compass.ask("What does this module do? What are its main components?")
print(response)

Further Reading

• Semantic Chunking Implementation Details
• Advanced Chunking Configuration
• Prompt Engineering for Code Understanding