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Compression Strategy Design

1. Metadata Compression​

UUID Compression​

class UUIDCompressor:
    @staticmethod
    def compress_uuid(uuid_str: str, length: int = 8) -> str:
        """Compress UUID to shorter format while maintaining uniqueness"""
        return uuid_str[:length]
    
    @staticmethod
    def compress_metadata(metadata: Dict) -> Dict:
        """Compress all UUIDs in metadata"""
        return {
            "d": metadata["doc_uuid"][:8],      # document id
            "c": metadata["chunk_uuid"][:8],    # chunk id
            "p": metadata["previous_uuid"][:8] if metadata["previous_uuid"] else None,
            "n": metadata["next_uuid"][:8] if metadata["next_uuid"] else None,
            "s": f"{metadata['doc_uuid'][:4]}_{metadata['chunk_sequence']}"  # compressed sequence
        }
    
    @staticmethod
    def decompress_metadata(compressed: Dict, original_metadata: Dict) -> Dict:
        """Restore original UUIDs using lookup or stored mapping"""
        return {
            "doc_uuid": original_metadata["doc_uuid"],
            "chunk_uuid": original_metadata["chunk_uuid"],
            "previous_uuid": original_metadata["previous_uuid"],
            "next_uuid": original_metadata["next_uuid"],
            "chunk_sequence": original_metadata["chunk_sequence"]
        }

class MetadataStore:
    """Store full metadata while working with compressed versions"""
    def __init__(self):
        self.metadata_map = {}
        
    def store(self, full_metadata: Dict) -> str:
        """Store full metadata and return compressed version"""
        compressed = UUIDCompressor.compress_metadata(full_metadata)
        self.metadata_map[compressed['c']] = full_metadata
        return compressed
    
    def retrieve(self, compressed_chunk_id: str) -> Dict:
        """Retrieve full metadata from compressed chunk id"""
        return self.metadata_map.get(compressed_chunk_id)

Workflow Checklist​

  • Prerequisites verified
  • Configuration applied
  • Process executed
  • Results validated
  • Documentation updated

Workflow Steps​

  1. Initialize - Set up the environment
  2. Configure - Apply settings
  3. Execute - Run the process
  4. Validate - Check results
  5. Complete - Finalize workflow

Workflow Phases​

Phase 1: Initialization​

Set up prerequisites and validate inputs.

Phase 2: Processing​

Execute the main workflow steps.

Phase 3: Verification​

Validate outputs and confirm completion.

Phase 4: Finalization​

Clean up and generate reports.

2. Content Compression​

Semantic Compression​

class SemanticCompressor:
    """Compress content while preserving meaning"""
    
    @staticmethod
    def compress_whitespace(content: str) -> str:
        """Normalize whitespace while preserving code structure"""
        import re
        # Preserve empty lines in code but remove excessive whitespace
        lines = content.split('\n')
        compressed_lines = []
        for line in lines:
            # Preserve indentation
            indent = len(line) - len(line.lstrip())
            compressed = ' ' * indent + ' '.join(line.strip().split())
            compressed_lines.append(compressed)
        return '\n'.join(compressed_lines)
    
    @staticmethod
    def compress_code(content: str) -> str:
        """Compress code content while maintaining readability"""
        import re
        # Remove comments
        content = re.sub(r'\s*#.*$', '', content, flags=re.MULTILINE)
        # Remove empty lines
        content = re.sub(r'\n\s*\n', '\n', content)
        # Normalize function/class spacing
        content = re.sub(r'\n\s*def\s+', '\ndef ', content)
        content = re.sub(r'\n\s*class\s+', '\nclass ', content)
        return content.strip()
    
    @staticmethod
    def compress_imports(content: str) -> str:
        """Compress import statements"""
        import re
        # Combine imports from same module
        import_map = {}
        for match in re.finditer(r'from\s+(\w+)\s+import\s+([^;\n]+)', content):
            module, imports = match.groups()
            if module not in import_map:
                import_map[module] = set()
            import_map[module].update(imp.strip() for imp in imports.split(','))
        
        # Reconstruct imports
        compressed_imports = []
        for module, imports in import_map.items():
            compressed_imports.append(f"from {module} import {', '.join(sorted(imports))}")
        
        return '\n'.join(compressed_imports)

3. Chunk Optimization​

Smart Chunking with Compression​

class ChunkOptimizer:
    def __init__(self, target_chunk_size: int = 4000):
        self.target_chunk_size = target_chunk_size
        self.compressor = SemanticCompressor()
    
    def optimize_chunk(self, content: str, content_type: str) -> str:
        """Optimize chunk content based on type"""
        if content_type == 'code':
            return self._optimize_code_chunk(content)
        elif content_type == 'text':
            return self._optimize_text_chunk(content)
        return content
    
    def _optimize_code_chunk(self, content: str) -> str:
        """Optimize code chunk"""
        content = self.compressor.compress_whitespace(content)
        content = self.compressor.compress_code(content)
        content = self.compressor.compress_imports(content)
        return content
    
    def _optimize_text_chunk(self, content: str) -> str:
        """Optimize text chunk"""
        return self.compressor.compress_whitespace(content)

4. Implementation in Document Processing​

Compressed Document Processing​

class CompressedDocumentProcessor:
    def __init__(self):
        self.metadata_store = MetadataStore()
        self.chunk_optimizer = ChunkOptimizer()
        self.uuid_compressor = UUIDCompressor()
    
    def process_document(self, content: str, doc_type: str) -> List[Dict]:
        """Process document with compression"""
        chunks = []
        # Create chunks with original content and metadata
        original_chunks = self._create_chunks(content)
        
        for chunk in original_chunks:
            # Compress chunk content
            optimized_content = self.chunk_optimizer.optimize_chunk(
                chunk['content'],
                doc_type
            )
            
            # Store full metadata and get compressed version
            compressed_metadata = self.metadata_store.store(chunk['metadata'])
            
            # Create compressed chunk
            compressed_chunk = {
                'content': optimized_content,
                'metadata': compressed_metadata
            }
            chunks.append(compressed_chunk)
        
        return chunks
    
    def reconstruct_document(self, compressed_chunks: List[Dict]) -> str:
        """Reconstruct original document from compressed chunks"""
        original_chunks = []
        
        for chunk in compressed_chunks:
            # Retrieve original metadata
            original_metadata = self.metadata_store.retrieve(
                chunk['metadata']['c']
            )
            
            original_chunks.append({
                'content': chunk['content'],
                'metadata': original_metadata
            })
        
        return self._merge_chunks(original_chunks)

5. Token Optimization Analysis​

Example Token Savings:​

  1. Metadata Compression

    • Original UUID (36 chars) → Compressed (8 chars)
    • Savings: ~78% per UUID
    • Total metadata savings: ~70%

  2. Content Compression

    • Whitespace normalization: ~10-20% reduction
    • Code compression: ~15-30% reduction
    • Import compression: ~5-15% reduction
    • Total content savings: ~30-65% depending on content type

  3. Overall Impact

    def calculate_token_savings(original_size: int, content_type: str) -> dict:
        savings = {
            'code': {
                'metadata': 0.70,  # 70% reduction
                'content': 0.35    # 35% reduction
            },
            'text': {
                'metadata': 0.70,  # 70% reduction
                'content': 0.15    # 15% reduction
            }
        }
        
        content_reduction = savings[content_type]['content']
        metadata_reduction = savings[content_type]['metadata']
        
        return {
            'original_tokens': original_size,
            'compressed_tokens': int(original_size * (1 - content_reduction)),
            'metadata_savings': int(original_size * metadata_reduction * 0.1),  # Assuming metadata is 10% of total
            'total_savings': int(original_size * (content_reduction + metadata_reduction * 0.1))
        }

## 6. Integration with Anthropic API

### Compressed API Calls
```python
class AnthropicHandler:
    def __init__(self):
        self.processor = CompressedDocumentProcessor()
    
    async def process_chunks(self, chunks: List[Dict], prompt: str) -> List[Dict]:
        """Process compressed chunks with Anthropic API"""
        responses = []
        
        for chunk in chunks:
            # Create compressed prompt
            compressed_prompt = self._create_compressed_prompt(chunk, prompt)
            
            # Make API call with compressed data
            response = await self._call_anthropic_api(compressed_prompt)
            
            # Store response with compressed metadata reference
            responses.append({
                'chunk_id': chunk['metadata']['c'],
                'response': response
            })
        
        return responses
    
    def _create_compressed_prompt(self, chunk: Dict, prompt: str) -> Dict:
        """Create compressed prompt for API call"""
        return {
            'metadata': chunk['metadata'],
            'content': chunk['content'],
            'prompt': prompt,
            'format': 'compressed'
        }

  1. Implement compression pipeline in stages:

  2. Monitor token usage and adjust compression levels:

    async def monitor_compression(self, document: str, duration: int = 24):
        """Monitor compression effectiveness over time"""
        metrics = {
            'original_size': len(document),
            'compressed_size': 0,
            'api_calls': 0,
            'token_savings': 0
        }
        # Implement monitoring logic
        return metrics

  3. Implement adaptive compression based on content type and API response quality.

This compression strategy can significantly reduce token usage while maintaining the benefits of our chunking and UUID system. Would you like me to focus on implementing any specific part of this compression system?