You Need Better Knowledge Graphs for Your Graph RAG

RAG (Retrieval-Augmented Generation) has become the hype of Generative AI applications, so are knowledge graphs. You see lots of graph-based LLM apps out there and you're probably building one too. However, how you construct knowledge graphs determines the quality of your LLM-based application. Solely relying on GPT-4 for extracting entities and relationships without thorough evaluation will give you the garbage-in-garbage-out effect.

To get prepared for Data Day Texas 2024, I built a Graph RAG AI assistant using Diffbot API for both web scraping and knowledge graph construction. You'll see how I built it while monitoring the results throughout the video. Diffbot offers transparency in the information retrieval process and benchmarks for evaluating the accuracy of the information retrieved.

Diffbot's APIs are free to use, including the Natural Language API that was used in the video:
http://app.diffbot.com

Note: This video is independently produced and is not sponsored by Diffbot, Neo4j, or Streamlit.

Here's the link to my Github repo for this project:
https://github.com/leannchen86/graph-...

0:00 Intro
0:53 Step 1. Web Scraping with Diffbot API
1:37 Step 2. Construct knowledge graph with Diffbot Graph Transformer (Langchain)
3:31 Step 3. Customize Diffbot Graph Transformer
3:41 Step 4. Import Diffbot Knowledge Graph into Neo4j Database
5:03 Step 5. What Entity/Relationship Extraction Looks Like By GPT-4
5:41 Step 6. Meet My Graph RAG AI Assistant
7:08 Outro


#knowledgegraph #generativeai #llm #aichatbot

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