GraphRAG Pipeline Tutorial: Extract Entities & Build Knowledge Graphs скачать в хорошем качестве

GraphRAG Pipeline Tutorial: Extract Entities & Build Knowledge Graphs

Welcome to Total Technology Zone github link: https://github.com/ronidas39/neo4j-gr... telegram: https://t.me/ttyoutubediscussion Build Knowledge Graphs with GraphRAG Pipeline | Neo4j + Python + LLM | Tutorial 3 🚀 *Learn how to build a scalable Knowledge Graph ingestion pipeline using Neo4j's GraphRAG SDK!* In this comprehensive tutorial, I'll show you how to use the *GraphRAG Pipeline class* to build production-ready knowledge graphs. We'll create a modular pipeline with three key components: Text Splitter, Entity Extractor, and Knowledge Graph Writer — all working together seamlessly. --- 🎯 What You'll Learn ✅ How to use the GraphRAG Pipeline class for scalable knowledge graph creation ✅ Building modular components that connect sequentially ✅ Handling large documents with Text Splitter to avoid rate limits & context window issues ✅ Entity extraction using LLM (GPT-4o-mini) ✅ Writing extracted entities and relationships back to Neo4j ✅ Exporting your knowledge graph to JSON --- 📚 Prerequisites Python 3.8+ Neo4j Database (AuraDB or local instance) OpenAI API Key Basic understanding of Knowledge Graphs (watch Tutorial 1 & 2 first!) --- ⏱️ TIMESTAMPS 0:00 - Introduction & Tutorial Overview 0:22 - Recap: Schema Extraction, Entity Extraction & Neo4j Write-back 0:46 - The Problem: Handling Large Documents (Rate Limits & Context Window) 1:25 - Solution: Text Splitter for Chunking Large Text 1:52 - Main Objective: Building a Scalable Pipeline 2:10 - Pipeline Architecture: Components as Individual Units 2:48 - How Components Connect: Output → Input Flow 4:07 - Importing Pipeline Class from neo4j-graphrag 5:00 - Creating Custom Components & Data Models 6:35 - Understanding Component Input/Output Flow 8:00 - Building the Run Pipeline Function 9:09 - Connecting Components with pipeline.connect() 11:33 - Testing the Basic Pipeline 12:02 - Understanding Pipeline Output Structure 14:06 - Real Use Case: Building the Knowledge Graph Pipeline 15:01 - Component 1: Text Splitter Setup 16:00 - Importing TextChunks & Neo4jGraph Types 18:06 - Reading Large Documents from Files 21:30 - Using FixedSizeTextSplitter (chunk_size & chunk_overlap) 25:04 - Component 2: LLM Entity Relation Extractor 26:27 - Configuring OpenAI LLM (GPT-4o-mini) 30:00 - Testing Text Splitting & Entity Extraction 31:02 - Debugging: Fixing max_tokens & response_format errors 33:44 - Viewing Extracted Nodes & Relationships 34:29 - Component 3: Knowledge Graph Writer 39:42 - Setting up Neo4j Driver Connection 41:35 - Creating the Neo4j Writer Instance 45:03 - Connecting All Three Components 47:04 - Building Graph Data Structure (Nodes & Relationships) 51:40 - Exporting Knowledge Graph to JSON 53:04 - Connecting to Neo4j AuraDB 55:07 - Running the Complete Pipeline 58:42 - Fixing SSL Certificate Error (neo4j+s://) 1:01:04 - Debugging KG Writer Issues 1:03:34 - SUCCESS! Visualizing the Knowledge Graph in Neo4j 1:05:40 - Final Thoughts & What's Next 1:07:04 - Tutorial Series Recap 1:08:05 - Please Like, Subscribe & Share! --- 🛠️ Tech Stack *Neo4j GraphRAG SDK* (Experimental Pipeline Module) *Python* (asyncio) *OpenAI GPT-4o-mini* (LLM for Entity Extraction) *Neo4j AuraDB* (Cloud Graph Database) --- 📦 Key Imports ```python from neo4j_graphrag.experimental.pipeline import Pipeline from neo4j_graphrag.experimental.components.text_splitter import FixedSizeTextSplitter from neo4j_graphrag.experimental.components.entity_relation_extractor import LLMEntityRelationExtractor from neo4j_graphrag.experimental.components.kg_writer import Neo4jWriter from neo4j_graphrag.llm import OpenAILLM from neo4j import GraphDatabase ``` --- 🔗 Related Tutorials 📌 *Tutorial 1:* Schema Extraction with Neo4j GraphRAG 📌 *Tutorial 2:* Entity Extraction & Writing to Neo4j 📌 *Tutorial 3:* Building Scalable Pipelines (This Video!) --- 💡 Pro Tips from This Tutorial 1. *Use Text Splitter* for large documents to avoid rate limits and context window errors 2. *chunk_size=8000* and *chunk_overlap=100* work well for most use cases 3. *Always annotate return types* in your component's `run()` method 4. *Use `neo4j+s://`* for SSL connections to AuraDB (not `neo4j://`) 5. *Pipeline components run sequentially* — output of one becomes input of the next --- 🙏 Support the Channel If this tutorial helped you, please: 👍 *LIKE* this video 🔔 *SUBSCRIBE* for weekly tutorials 💬 *COMMENT* your questions below 📤 *SHARE* with your network Your support helps me create more in-depth, quality content! --- 📧 Connect with Me 🌐 Channel: *Total Technology Zonne* 👤 Instructor: *Roni Das* --- #Neo4j #GraphRAG #KnowledgeGraph #Python #LLM #RAG #AI #MachineLearning #Tutorial #OpenAI #GPT4 #GraphDatabase #DataScience #NLP