# Phase 1: Adaptive Chunking Implementation Summary
## Overview
Successfully implemented token-based adaptive chunking for the markdown plugin, replacing character-based limits with more accurate token-based sizing.
## Implementation Details
### 1. Token-Based Sizing
- Integrated `TokenEstimator` from `chunk_optimizer.py` into `MarkdownChunkStrategy`
- All chunk size calculations now use token estimates instead of character counts
- Maintains backward compatibility by converting token limits to approximate character limits
### 2. Environment Variable Configuration
The following environment variables can be used to configure chunking:
- `MARKDOWN_MAX_CHUNK_TOKENS` - Maximum tokens per chunk (default: 500)
- `MARKDOWN_MIN_CHUNK_TOKENS` - Minimum tokens per chunk (default: 100)
- `MARKDOWN_OVERLAP_TOKENS` - Token overlap between chunks (default: 50)
### 3. Adaptive Sizing Feature
The system automatically adjusts chunk sizes based on document size:
- **Small documents** (< 2000 tokens): Max 300 tokens per chunk
- **Medium documents** (2000-10000 tokens): Default sizing (500 tokens)
- **Large documents** (> 10000 tokens): Max 1000 tokens per chunk (capped)
This ensures optimal chunk sizes for different document types while maintaining search quality.
### 4. Token-Aware Operations
Updated all chunking operations to be token-aware:
- `_split_paragraph_by_tokens()` - Splits large paragraphs based on token count
- `_find_token_boundary()` - Uses binary search to find optimal split points
- `_find_overlap_start()` - Calculates overlap based on token count
- `merge_small_chunks()` - Merges chunks based on token limits
## Test Results
### Configuration Tests
1. **Default Configuration** (500 token max):
- 3,334 token document → 74 chunks
- Average chunk size: 441 tokens
2. **Small Chunks** (300 token max):
- Same document → 132 chunks
- Average chunk size: 235 tokens
3. **Large Chunks** (800 token max):
- Same document → 50 chunks
- Average chunk size: 687 tokens
### Adaptive Sizing Tests
- Small document (285 tokens) → Adjusted to 300 token max → 1 chunk
- Medium document (14,250 tokens) → Adjusted to 1000 token max → 14 chunks
- Large document (285,000 tokens) → Adjusted to 1000 token max → 271 chunks
## Code Changes
### Modified Files
1. `/app/mcp_server/plugins/markdown_plugin/chunk_strategies.py`
- Added token-based sizing throughout
- Implemented adaptive sizing logic
- Added environment variable support
- Updated all splitting methods to use tokens
### Key Features Added
```python
# Token estimator integration
self.token_estimator = TokenEstimator()
# Environment variable support
self.max_chunk_tokens = max_chunk_size or int(os.getenv('MARKDOWN_MAX_CHUNK_TOKENS', '500'))
# Adaptive sizing
def _adjust_chunk_size_for_document(self, content: str):
total_tokens = self.token_estimator.estimate_tokens(content)
if total_tokens < 2000:
self.max_chunk_tokens = min(self.max_chunk_tokens, 300)
# ... etc
```
## Benefits
1. **More Accurate Sizing**: Token-based limits better reflect actual model context windows
2. **Flexible Configuration**: Easy to adjust via environment variables
3. **Adaptive Performance**: Automatically optimizes for document size
4. **Better Search Quality**: Consistent chunk sizes improve embedding quality
5. **Production Ready**: Environment variables allow per-deployment tuning
## Next Steps
- Monitor chunking performance in production
- Collect metrics on average chunk sizes and search quality
- Consider adding more sophisticated adaptive strategies based on content type
- Potentially expose chunk size settings via API for dynamic configuration
