Document Processing & Chunking

The Document Processing & Chunking step configures how your uploaded documents are processed, divided into chunks, and prepared for embedding. This critical step directly impacts retrieval quality and system performance.

Chunking Strategy Overview

Chunking divides large documents into smaller, semantically meaningful pieces that can be efficiently processed and retrieved. The choice of chunking strategy significantly affects your RAG system's performance and accuracy.

Why Chunking Matters

• Embedding Efficiency: Models have token limits (typically 512-8192 tokens)
• Retrieval Precision: Smaller chunks provide more focused context
• Response Quality: Well-chunked content produces more relevant responses
• Performance: Optimized chunks improve search and retrieval speed

Chunking Methods

Fixed-Size Chunking

Description: Divides text into chunks of predetermined size with configurable overlap.

Best For:

• Consistent processing requirements
• Uniform document types
• High-volume processing needs
• Predictable performance characteristics

Configuration Options:

• Chunk Size: 100-8192 tokens (recommended: 512-1024)
• Overlap Percentage: 0-50% (recommended: 10-20%)
• Boundary Respect: Sentence or paragraph boundaries
• Minimum Chunk Size: Prevent overly small chunks

Example Use Cases:

• Large PDF documents
• Legal documents with uniform structure
• Technical manuals and documentation
• Academic papers and research documents

Semantic Chunking

Description: Divides text based on semantic boundaries and topic coherence.

Best For:

• Documents with clear topic transitions
• Content requiring contextual integrity
• Mixed content types
• Quality over quantity prioritization

Configuration Options:

• Semantic Threshold: Similarity cutoff for topic boundaries
• Maximum Chunk Size: Upper limit for chunk length
• Minimum Chunk Size: Lower limit to ensure content adequacy
• Topic Modeling: Advanced semantic boundary detection

Example Use Cases:

• Blog posts and articles
• Multi-topic research papers
• Meeting transcripts and conversations
• Educational content with distinct sections

Recursive Chunking

Description: Hierarchical chunking that creates nested chunks at multiple levels.

Best For:

• Complex document structures
• Hierarchical content organization
• Multi-level information retrieval
• Comprehensive context preservation

Configuration Options:

• Hierarchy Levels: Number of chunking levels (typically 2-4)
• Size Ratios: Relative sizes for each hierarchy level
• Overlap Strategy: How overlap applies across levels
• Parent-Child Linking: Relationship preservation between levels

Example Use Cases:

• Technical specifications with nested sections
• Books and long-form content
• Policy documents with hierarchical structure
• Complex research papers with multiple subsections

Document-Based Chunking

Description: Treats entire documents or major sections as chunks.

Best For:

• Short documents that shouldn't be divided
• Documents with strong internal coherence
• Reference materials requiring complete context
• High-level document classification needs

Configuration Options:

• Document Grouping: Combine related small documents
• Section Detection: Automatic major section identification
• Metadata Preservation: Enhanced metadata at document level
• Quality Filtering: Document-level quality assessment

Example Use Cases:

• FAQ entries and knowledge base articles
• Product descriptions and specifications
• Short reports and summaries
• Individual emails or messages

Advanced Processing Options

Sentence Boundary Respect

Enable When:

• Content quality is more important than strict size limits
• Working with well-structured prose
• Need to maintain grammatical coherence

Impact:

• Prevents mid-sentence breaks
• May result in variable chunk sizes
• Improves readability and context quality

Context Coherence Maintenance

Enable When:

• Documents contain interconnected concepts
• Cross-references are important
• Topic continuity matters for understanding

Features:

• Preserves conceptual relationships
• Maintains narrative flow
• Handles transitional phrases and connections

Section Header Preservation

Enable When:

• Documents have clear hierarchical structure
• Headers provide important context
• Navigation and organization are priorities

Benefits:

• Maintains document structure information
• Improves chunk categorization
• Enables hierarchical retrieval strategies

Cross-Reference Handling

Enable When:

• Documents contain internal references
• Citations and links are important
• Reference resolution is needed

Capabilities:

• Link resolution and context inclusion
• Citation tracking and management
• Reference validation and updating

Code Block Handling

Enable When:

• Technical documents with code examples
• API documentation
• Programming tutorials and guides

Features:

• Syntax-aware chunking
• Language detection and highlighting
• Separate processing for code vs. prose

Content Filtering Options

Low Information Content Filtering

Skip chunks containing:

• Repetitive boilerplate text
• Navigation elements and menus
• Copyright notices and disclaimers
• Formatting artifacts and noise

Metadata-Only Chunk Filtering

Skip chunks that contain:

• Only file metadata
• Pure formatting information
• Empty or whitespace-only content
• System-generated content without value

Non-English Content Handling

Options:

• Skip: Exclude non-English chunks entirely
• Process: Include with language identification
• Translate: Auto-translate before processing
• Flag: Mark for manual review

Configuration Best Practices

Choosing Chunk Size

Small Chunks (100-300 tokens)

• Pros: Precise retrieval, specific context
• Cons: May lose broader context, requires more chunks
• Best for: FAQ systems, specific fact retrieval

Medium Chunks (300-800 tokens)

• Pros: Balance of precision and context
• Cons: May still fragment complex topics
• Best for: General knowledge bases, mixed content

Large Chunks (800-2048 tokens)

• Pros: Rich context, fewer total chunks
• Cons: Less precise retrieval, higher processing costs
• Best for: Complex analysis, long-form content

Overlap Configuration

Low Overlap (5-10%)

• Minimal redundancy
• Efficient storage
• Risk of losing context at boundaries

Medium Overlap (10-20%)

• Good balance of efficiency and context
• Recommended for most use cases
• Handles boundary issues effectively

High Overlap (20-50%)

• Maximum context preservation
• Higher storage requirements
• Best for critical context preservation

Quality Thresholds

Minimum Chunk Length

• Set based on meaningful content requirements
• Typically 50-100 tokens minimum
• Prevents noise from very short chunks

Maximum Chunk Length

• Model-dependent (respect embedding limits)
• Consider processing performance
• Balance with retrieval precision needs

Validation and Testing

Chunk Quality Assessment

Automatic Validation:

• Content completeness checking
• Semantic coherence scoring
• Boundary quality evaluation
• Overlap effectiveness analysis

Manual Review Tools:

• Chunk preview and inspection
• Boundary adjustment capabilities
• Quality scoring and feedback
• Batch review and approval workflows

Processing Pipeline Testing

Sample Processing:

• Test configurations on sample documents
• Performance benchmarking
• Quality metric evaluation
• A/B testing of different strategies

Iterative Optimization:

• Performance monitoring and adjustment
• Quality feedback incorporation
• Configuration refinement tools
• Automated optimization suggestions

Performance Considerations

Processing Speed

Factors Affecting Speed:

• Document size and complexity
• Chunking method selection
• Processing option complexity
• Hardware and resource allocation

Optimization Strategies:

• Batch processing for efficiency
• Parallel processing where possible
• Caching of intermediate results
• Progressive processing for large collections

Storage Efficiency

Chunk Storage Optimization:

• Deduplication of similar chunks
• Compression of text content
• Efficient metadata storage
• Hierarchical storage management

Retrieval Performance

Chunk Design for Retrieval:

• Optimal chunk size for embedding models
• Effective overlap for context preservation
• Quality filtering for relevant results
• Indexing strategy alignment

Prerequisites for Next Step

Before proceeding to Pipeline Configuration:

• ✅ Chunking strategy selected and configured
• ✅ Processing options configured appropriately
• ✅ Sample processing completed successfully
• ✅ Chunk quality validation passed
• ✅ Performance requirements satisfied

The chunks created in this step will be embedded and indexed in the Pipeline Configuration phase, where you'll set up the retrieval and generation components of your RAG system.