High-Level Architecture
Relevant source files
The following files were used as context for generating this wiki page:
- scripts/generate_knowledge_base.py
- knowledge/problems/data-storage-architecture-design.json
- knowledge/techniques/hybrid-data-storage-architecture.json
- knowledge/papers/hybrid-data-storage-architecture-a-novel-design-approach-based-on-microservices-.json
- knowledge/problems/code-quality-assessment.json
The CodeScholar architecture is a structured, multi-layer knowledge system designed to transform raw academic research into actionable technical guidance for software engineers. The system facilitates discovery through a hierarchical taxonomy that moves from high-level engineering problems down to specific implementation evidence, optimized for consumption by both human developers and AI agents via the Model Context Protocol (MCP).
The core of the system is a 4-pass processing pipeline that distills paper metadata into three primary architectural layers: Problems, Techniques, and Papers. This structure allows users to navigate from "I have this kind of problem" to "Here is the evidence/proof," ensuring all technical recommendations are grounded in verified research.
Sources: scripts/generate_knowledge_base.py:11-20, scripts/generate_knowledge_base.py:1086-1100
Knowledge Tier Architecture
The system organizes information into four distinct directories, each serving a specific role in the knowledge discovery lifecycle.
Layer 1: Problems (Entry Points)
The Problems layer serves as the initial decision matrix for engineers. It identifies problem types—such as "Data Storage Architecture Design" or "Code Quality Assessment"—and provides a comparison of available techniques based on performance metrics like speed, memory usage, and ease of implementation.
Sources: knowledge/problems/data-storage-architecture-design.json:1-15, knowledge/problems/code-quality-assessment.json:1-12
Layer 2: Techniques (Reference Cards)
Techniques represent synthesized, canonical methods extracted from multiple research papers. Each card provides a "how-it-works" explanation, pseudocode implementation skeletons, and maturity ratings (e.g., "emerging" or "established").
Sources: knowledge/techniques/hybrid-data-storage-architecture.json:1-10, scripts/generate_knowledge_base.py:440-455
Layer 3: Papers (Evidence Layer)
The Papers layer contains the primary source data, including DOIs, specific benchmarks, and methodology details. This layer validates the claims made in the higher-level Technique and Problem cards.
Sources: knowledge/papers/hybrid-data-storage-architecture-a-novel-design-approach-based-on-microservices-.json:1-20, scripts/generate_knowledge_base.py:15-18
Layer 4: Connections (Discovery)
The Connections layer maps cross-domain relationships, identifying "Bridging Techniques" that solve similar problems across different fields, such as Machine Learning and Cybersecurity.
Sources: scripts/generate_knowledge_base.py:19-20, scripts/generate_knowledge_base.py:750-765
Data Processing Pipeline
The system utilizes a multi-pass pipeline to generate the knowledge base from a unified library of paper metadata.
The diagram above illustrates the transformation of raw metadata into a structured 3-layer knowledge architecture.
Sources: scripts/generate_knowledge_base.py:22-30
Pass 1: Extraction & PDF Processing
The system attempts to find local PDF files matching paper titles. If found, it extracts up to 30,000 characters of text (focusing on methodology and results) using PyMuPDF. This text, or the abstract if no PDF exists, is passed to an LLM to generate a structured JSON knowledge card.
Sources: scripts/generate_knowledge_base.py:65-80, scripts/generate_knowledge_base.py:230-245
Pass 2 & 3: Synthesis and Clustering
The generator clusters papers by technique_name to create synthesized Technique Cards and clusters techniques by problem_type to create Problem Entry Points.
Sources: scripts/generate_knowledge_base.py:925-940, scripts/generate_knowledge_base.py:1010-1025
Data Models
The system relies on standardized JSON schemas for each layer to ensure consistency during the synthesis passes.
| Field Type | Description | Relevant Layer |
|---|---|---|
problem_type | Standard CS terminology (e.g., "multi-objective optimization") | Papers, Problems |
technique_category | Classification (e.g., "optimization_algorithm", "framework") | Papers, Techniques |
maturity | Status (established, proven, emerging, experimental) | Techniques |
decision_matrix | Comparison of speed, memory, accuracy, and ease | Problems |
Sources: scripts/generate_knowledge_base.py:120-150, scripts/generate_knowledge_base.py:445-470, knowledge/problems/data-storage-architecture-design.json:20-30
Implementation Detail: Knowledge Synthesis
The following implementation skeleton demonstrates how the system identifies and organizes a hybrid architecture within the knowledge base.
def hybrid_data_storage_architecture(data_requirements: Dict[str, Any]) -> Architecture:
# Step 1: Identify data storage requirements
requirements = identify_requirements(data_requirements)
# Step 2: Design microservices for specific tasks
services = design_microservices(requirements)
# Step 3: Implement hybrid storage (Traditional + Microservices)
hybrid_storage = implement_hybrid_storage(services)
return hybrid_storage
Sources: knowledge/techniques/hybrid-data-storage-architecture.json:60-70
Conclusion
The CodeScholar architecture effectively bridges the gap between academic research and practical software engineering. By automating the extraction, synthesis, and clustering of research data, it provides a scalable framework for technical decision-making and cross-domain discovery.