Building Blocks for Robust and Context-Aware Retrieval-Augmented Generation
Most RAG implementations treat documents as flat, unstructured text, leading to:
- Context fragmentation when chunks break across natural document boundaries
- Entity amnesia when references are lost between chunks
- Semantic degradation when document structure is ignored
ByteMeSumAI addresses these issues by preserving document architecture:
- Boundary-aware chunking respects natural document divisions
- Entity tracking maintains references across sections
- Semantic awareness preserves meaning and relationships
- Hierarchical processing maintains document structure
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import bytemesumai as bm
# Load a document
doc = bm.Document.from_file("my_document.txt")
# Process with boundary-aware chunking
chunker = bm.ChunkingProcessor()
chunking_result = chunker.chunk_document(
text=doc,
strategy="boundary_aware",
compute_metrics=True
)
# Print chunking metrics
print(f"Created {len(chunking_result.chunks)} chunks")
print(f"Boundary preservation score: {chunking_result.metrics.get('boundary_preservation_score', 'N/A')}")
# Create a multi-strategy summary
summarizer = bm.SummarizationProcessor()
basic_summary = summarizer.basic_summary(doc.content, style="concise")
entity_summary = summarizer.entity_focused_summary(doc.content)
print(f"Basic Summary: {basic_summary.summary[:100]}...")-
Chunking that respects meaning: When a legal document's sections are split mid-paragraph, key context is lost. ByteMeSumAI preserves these natural boundaries.
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Entity tracking: When "Company X" is referenced across different sections of a document, traditional RAG systems may lose track of which company is being discussed. ByteMeSumAI's entity tracking maintains these references.
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Temporal coherence: When events in a document are chronological, traditional chunking can scramble this timeline. ByteMeSumAI preserves temporal relationships.
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Structure preservation: When document hierarchy matters (e.g., headings, subsections), ByteMeSumAI maintains this structure for improved context.
ByteMeSumAI
├── Chunking Engine # Document segmentation with semantic awareness
├── Summarization Engine # Multi-strategy content distillation
├── Document Processors # Hierarchical document handling
├── Entity Tracking # Cross-document entity reference management
└── Evaluation Framework # Quantitative assessment of output quality
pip install bytemesumaiVisit the full documentation to learn more about ByteMeSumAI's capabilities:
This project is licensed under the MIT License - see the LICENSE file for details.
Document architecture is the foundation of effective RAG systems.
ByteMeSumAI: Building the blocks for semantically-aware document processing.