GraphRAG

📖 Bản tiếng Việt (Vietnamese Edition) Prerequisite: Review the previous module in the ai-data-engineering-pipeline series before proceeding. Executive Summary: The Disruption of Naive RAG and the GraphRAG Era Answer-first: Naive RAG collapses in enterprise environments due to relational blindness, unstructured document chunk destruction, and lack of fine-grained access control. Modern AI architectures combine Knowledge Graphs with vector search (GraphRAG) and event-driven data ingestion to deliver 100% data freshness, 38% higher retrieval precision, and deterministic row-level security. Establishing a Zero-Copy Vector Lakehouse backed by LanceDB and Apache Iceberg v3 delivers sub-50ms retrieval latencies and strict document-level RBAC enforcement while eliminating costly vector database silos. ...

📖 Bản tiếng Việt (Vietnamese Edition) Prerequisite: Familiarity with the concepts introduced in Executive Summary. Review it first if the terminology in this part is unfamiliar. Part 1 — Agentic GraphRAG vs. Long-Context Window: Architectural Trade-offs Answer-first: Relying exclusively on 1M+ token context windows introduces quadratic latency degradation ($O(N^2)$ attention overhead), severe token cost inflation, and needle-in-a-haystack recall loss. Agentic GraphRAG extracts focused entity subgraphs to achieve 65% faster Time-To-First-Token (TTFT) at less than 10% of the inference cost. By deploying Hierarchical GraphRAG with Leiden community detection, enterprises achieve 65% faster Time-To-First-Token (TTFT) and eliminate the multi-dollar token penalties of 1M+ context window prefill while preserving cross-document multi-hop reasoning. ...

← Previous Chapter: Part 1: Swarm Topologies | Series Hub | Next Chapter: Part 3: Resilient Tool Calling → Answer-first: Efficient agent memory requires a 3-tier hierarchy: (1) Working Memory (short-term buffer in Redis), (2) Episodic Memory (summarized past trajectories in PostgreSQL), and (3) Semantic Memory (entity relationships in a Temporal Knowledge Graph).

📖 Bản tiếng Việt (Vietnamese Edition) Answer-first: The Enterprise AI Data Pipeline & GraphRAG Architecture (2027 SOTA) Masterclass provides a complete engineering blueprint for building resilient, low-latency, and hallucination-resistant knowledge engines. By converging Hierarchical GraphRAG, Zero-Copy Vector Lakehouses (Apache Iceberg v3 + LanceDB), ColPali visual document retrieval, and streaming Change Data Capture (CDC), enterprises eliminate relational blindness, reduce cloud storage costs by 62%, and achieve sub-50ms retrieval latencies under zero-trust governance. ...

GraphRAG vs Naive RAG: Enterprise Architecture Guide Answer-first: GraphRAG outperforms naive RAG in enterprise applications by combining knowledge graph entity extraction with vector search, resolving complex multi-hop relationship queries accurately. 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. ...