Knowledge Graph

1. The Fall of Traditional OCR: The “Garbage In, Garbage Out” Pain In Enterprise RAG architecture, the most ruthless formula is: Garbage In = Garbage Out. Before 2025, data engineers often used traditional OCR tools (like Tesseract, PyMuPDF) to extract text from PDF documents. The result was a disaster: Financial report table structures were shattered, data columns were merged together, and technical diagrams were completely ignored. When a Vector Database contains a messy, contextless heap of text (Context loss), no matter how powerful the LLM is, the answer you receive will only be a Hallucination. ...

Answer-first: Naive RAG works well for simple keyword queries on isolated documents. For complex, global questions spanning multiple entities, GraphRAG is superior as it builds a knowledge graph using LLMs. Enterprise implementations require combining change data capture (CDC) with vector search to keep graphs synchronized. What You’ll Learn That AI Won’t Tell You Schema design for knowledge graphs that speed up global enterprise RAG. Syncing GraphRAG knowledge bases in real-time using PostgreSQL WAL events. Most RAG (Retrieval-Augmented Generation) implementations look the same: chunk documents, embed them into vectors, store them in a vector database, retrieve by cosine similarity, and inject the top-K chunks into the LLM context. This works for simple document Q&A. It fails systematically for enterprise knowledge bases where the answer to a question depends not on a single document chunk, but on the relationships between dozens of interconnected entities. ...