# RAG Implementation for Cybersecurity Knowledge Base at TrainGRC ## Company Overview and Problem Statement TrainGRC, a startup focused on cloud security training, developed a platform called stinkbait.io for cybersecurity research and reporting. The company faced several unique challenges in the cybersecurity domain: - Knowledge fragmentation across different subcommunities - LLM censorship of security-related topics - Need for accurate and comprehensive information sharing ## Technical Implementation Challenges ### Data Quality Management ### PDF Processing Challenges - Historical context: PDFs have existed since 1993 with numerous extensions - Common issues: ### PDF Processing Solution - Primary tool: Amazon Textract for OCR - Fallback strategy: ### Web Scraping Challenges - Inconsistent HTML structure across sources - Various web frameworks and WYSIWYG editors - Poor data structure standardization ### Web Scraping Solution - Manual inspection of websites required - Custom parsing based on specific HTML elements - Careful validation of extracted data quality - Avoiding reliance on generic third-party tools ### Search Quality Optimization ### Vector Search Algorithm Considerations - Trade-offs between exact KNN and approximate nearest neighbor searches - Implementation options: ### Database Technology Selection - Initial approach: - Challenges encountered: ### Vector Database Considerations - Evaluation criteria: - Decision factors: ### Embedding Model Selection ### Model Options Analysis - Default choice: Ada embeddings ### Specialized Models Consideration - Types of embedding models: ### Context Management ### Context Chunking Strategies - Ideal approach: - Practical implementation: ### Long-term Storage Considerations - Vector storage strategy: ### Model Migration Planning - Key considerations: ## Implementation Lessons ### Best Practices - Importance of upfront data quality - "Garbage in, garbage out" principle - Need for manual inspection and validation - Balance between automation and quality control ### Future Considerations - Active research in context boundary detection - Evolution of embedding models - Continuous evaluation of vector database solutions - Adaptation to changing security landscape ## Technical Infrastructure ### Tools and Technologies - Amazon Textract for OCR - Vector databases - Embedding models (Ada and alternatives) - Custom data processing pipelines - Storage solutions for vectors and raw data ### Architecture Decisions - Modular approach to data processing - Flexible storage solutions - Scalable vector search implementation - Backup strategies for data extraction