Document Segmentation Workflow
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The Document Segmentation Workflow is an intelligent system designed to handle large research papers and technical documents that exceed Large Language Model (LLM) token limits. It employs semantic content analysis to decompose complex documents into manageable segments while preserving the integrity of algorithms, mathematical formulas, and core concepts. This workflow is a critical component of the "paper-to-code" pipeline, ensuring that the implementation agents receive context-rich, relevant information for code generation.
Sources: tools/document_segmentation_server.py:1-15, prompts/code_prompts.py:166-175
System Architecture
The workflow operates through a Model Context Protocol (MCP) server that provides tools for document analysis, segmentation, and retrieval. It utilizes specialized analyzers and segmenters to process input text based on its semantic characteristics.
Architecture Overview
The architecture transitions from raw content to a structured index via semantic analysis.
Sources: tools/document_segmentation_server.py:46-60, tools/document_segmentation_server.py:910-940
Core Components
DocumentAnalyzer
The DocumentAnalyzer class uses weighted semantic indicators to identify document types and complexity. It calculates scores for algorithms, technical concepts, and implementation details to determine the most effective processing strategy.
| Indicator Type | High Weight Keywords | Medium Weight Keywords |
|---|---|---|
| Algorithm | algorithm, procedure, method, approach | step, process, implementation |
| Technical Concept | formula, equation, theorem, lemma, proof | parameter, variable, function, model |
| Implementation | code, implementation, programming, software | design, structure, module, component |
Sources: tools/document_segmentation_server.py:101-125
DocumentSegmenter
The DocumentSegmenter implements various strategies to split documents while maintaining logical continuity. It includes specific logic for identifying algorithm blocks, concept groups, and formula derivation chains.
- Algorithm Preservation: Identifies patterns like
input:,output:, and control flow keywords (e.g.,for each,while) to keep logic blocks intact. - Formula Chains: Detects dense mathematical regions using LaTeX delimiters (
$$or$) and merges nearby formulas. - Semantic Boundaries: Detects logical transitions between paragraphs to avoid mechanical splitting.
Sources: tools/document_segmentation_server.py:228-245, tools/document_segmentation_server.py:490-520, tools/document_segmentation_server.py:565-595
Segmentation Strategies
The system dynamically selects a strategy based on the document's characteristics:
| Strategy | Use Case | Description |
|---|---|---|
semantic_research_focused | Academic Papers | Focuses on sections like Methodology and Results. |
algorithm_preserve_integrity | Algorithm-heavy Docs | Maintains step-by-step procedures and pseudo-code. |
concept_implementation_hybrid | Technical Guides | Maps theoretical concepts directly to code examples. |
semantic_chunking_enhanced | Long Documents | Uses adaptive character limits (2k-4k) based on complexity. |
Sources: tools/document_segmentation_server.py:35-42, tools/document_segmentation_server.py:188-205
Retrieval Workflow
The workflow uses an intelligent retrieval system to provide segments based on the specific phase of the code implementation cycle.
The sequence shows how agents query the server using context-aware parameters.
Sources: tools/document_segmentation_server.py:1003-1040, prompts/code_prompts.py:284-290
Query Types and Scoring
Relevance is calculated differently based on the query_type provided by the agent:
- Concept Analysis: Prioritizes "Introduction", "Overview", and "Framework" sections.
- Algorithm Extraction: Prioritizes "Methodology", pseudo-code blocks, and mathematical formulations.
- Code Planning: Focuses on implementation details, architectural descriptions, and parameter tables.
Sources: tools/document_segmentation_server.py:840-875, tools/document_segmentation_server.py:1135-1160
Data Models
DocumentSegment
Represents a semantic unit of the document.
id: Unique hash based on content and position.content_type: introduction, methodology, algorithm, results, etc.relevance_scores: Dictionary mapping query types to float scores (0.0 - 1.0).char_start/char_end: Exact character positions in the source file.
Sources: tools/document_segmentation_server.py:68-81
DocumentIndex
The root metadata object for a segmented document.
document_type: research_paper, technical_doc, etc.segmentation_strategy: The chosen algorithm for splitting.total_chars: Total document length used for adaptive limit calculation.
Sources: tools/document_segmentation_server.py:84-93
Integration with Implementation Workflow
The segmentation results are utilized by agents in the CodeImplementationWorkflow. Agents call read_document_segments during the "Pre-Implementation Analysis" phase to understand dependencies and requirements before executing write_file.
# Agent implementation strategy using segmentation tools
# 1. Primary Algorithm Extraction
read_document_segments(
query_type="algorithm_extraction",
keywords=["algorithm", "formula", "implementation"],
max_segments=3
)
Sources: prompts/code_prompts.py:177-185, workflows/code_implementation_workflow_index.py:215-230
Summary
The Document Segmentation Workflow provides the foundation for reproducing complex research papers by ensuring high-fidelity extraction of technical details. By moving beyond simple character-based splitting to semantic, integrity-preserving segmentation, the system enables LLM agents to process vast amounts of technical information without losing the context of critical algorithms or mathematical foundations.
Sources: tools/document_segmentation_server.py:7-25, prompts/code_prompts.py:16-25