In this video, together we will go through all the steps to construct a #knowledgegraph from Tabular Datasets and design a ChatBot APP to interact with the Knowledge Graph using natural language. For this purpose, we will use Knowledge Graph LLM agents and the GPT model. We will design a Chatbot that can:

1. Chat with Graph DB using an improved LLM agent
2. Chat with Graph DB using a simple LLM agent
3. RAG with Graph DB

Moreover, in this video, I will show you the second RAG approach for interacting with Tabular data but this time, using the knowledge graph. The code is available on the Github repository.

GitHub Repositories:
Advanced Q&A and RAG series: https://github.com/FarzadR/Advanced...
LLMZeroToHundred Series: https://github.com/FarzadR/LLMZero...

00:00:00 Intro (Presentation)
00:00:17 Table of Contents (Presentation)
00:01:55 Why Knowledge Graph? (Presentation)
09:35 Project schema walkthrough (Presentation)
00:06:14 LLM Model Matters (Presentation)
00:07:26 Series schema (RAG vs Q&A) (Presentation)
00:08:05 Knowledge Graph Fundamentals (Presentation)
00:10:29 How to Construct Knowledge Graph (Presentation)
00:14:12 ChatBot Schema walkthrough (Presentation)
00:16:02 Knowledge Graph Agent schema walkthrough (Presentation)
00:18:00 Second RAG approach for tabular data (Presentation)
00:18:24 Knowledge Graph for Movie Dataset (Presentation)
00:21:41 Knowledge Graph for Microsoft medical chatbot (Presentation)
00:22:52 ChatBot demo
00:23:36 Graph database installation and configuration
00:32:27 Code structure walkthrough
00:33:25 Verify your OpenAI and Neo4j connection
00:34:39 Download the Movide dataset and generate synthetic data
00:37:15 Construct the knowledge graph from the Movie dataset
00:45:50 Creating and populating the Vector Index in Graph Database
00:51:23 Q&A with GraphDB populated with Knowledge Graph of the Tabular Data (designing the simple and improved agent)
01:07:47 RAG with GraphDB
01:13:22 Testing the ChatBot
01:17:10 Microsoft Medical Chatbot walkthrough
01:22:52 Ending notes

Frameworks: #langchain , #openai, gradio, #neo4j,
#chatbot #rag #llm #agent #python #gpt