Build your own GraphRAG Assistant: github.com/mobintmu/mobinshaterian.com

Have you ever wondered how to turn years of scattered technical articles, markdown notes, and complex JSON payloads into an interconnected, highly intelligent AI assistant? In this video, we walk through the step-by-step technical journey of building a GraphRAG (Graph Retrieval-Augmented Generation) reasoning engine from scratch to power a highly context-aware software architecture chatbot. 🚨 THE PROBLEM: Why Traditional RAG Fails Standard Retrieval-Augmented Generation (RAG) relies on flat vector similarity and keyword search. While this works fine for finding isolated paragraphs, it completely breaks down on complex, multi-hop architectural questions. For example, if you ask, "How does our streaming Kafka ingestion pipeline handle deadlocks and schemas when inserting into ClickHouse?", standard RAG will likely return disjointed text snippets about Kafka or ClickHouse, missing the structural connection entirely. Furthermore, simple similarity algorithms suffer from "Hub-Node Distortion". If two unrelated blog posts share a broad tag like #MachineLearning, standard Jaccard similarity erroneously scores their connection at a perfect 1.0, generating severe false positives. 💡 THE SOLUTION: An Interconnected Knowledge Graph To solve this, we transform chronological, unstructured archives into a high-fidelity Knowledge Graph. Here is the exact pipeline: 1️⃣ Data Staging & Formatting: We use an automated Python script (build_kg_docs.py) to convert raw JSON records into clean, structured Markdown documents with explicit frontmatter and headers. 2️⃣ Knowledge Graph Extraction: Using a graph extraction tool (graphify) routed through an LLM, we extract entity-relation-entity triples and multi-component system hyperedges. This uncovers the core pillars of the technical writing (Go, Kafka, ClickHouse, Keycloak, etc.). 3️⃣ Adamic-Adar Similarity: We implement an Adamic-Adar / Inverse-Degree Weighted Similarity algorithm in Python. This weights rare technical concepts heavily and broad concepts lightly, eliminating false similarity matches. 4️⃣ Breadth-First Search (BFS) Retrieval: We build a custom reasoning engine (graph_rag.py) that matches query keywords to seed graph entities, performs BFS expansion to extract a relevant localized subgraph, and passes this rich context to the LLM. 5️⃣ Frontend Integration: We wire the graph to a TanStack Router frontend using a normalized slug resolver to support fuzzy matching across articles. 🎯 What This Enables By shifting from flat vector search to an interconnected Knowledge Graph, we unlock: 🌐 Interactive Navigation: Genuinely related post recommendations based on shared architectural topology rather than simple keyword collisions. 🤖 Context-Aware Chatbot: An AI assistant that truly understands the relationships between Go workers, Kafka pipelines, Redis caches, and ClickHouse databases. 🔍 Knowledge Gap Audits: A visual way to find isolated concepts and thin communities, creating a clear roadmap for future deep-dive articles. 🛠️ Key Technologies & Frameworks Mentioned: Graph Database & Extraction: Graphify, OpenAI API (gpt-5.6-luna) GraphRAG Architecture: Python, Breadth-First Search (BFS) Scale-Aware Systems: Go, Kafka, Redis, ClickHouse, Keycloak, OpenFGA Frontend: TanStack Router If you found this technical breakdown helpful, please LIKE, SHARE, and SUBSCRIBE for more deep dives into software architecture, Knowledge Graphs, and LLM orchestration! 🔔