Build a Chatbot to Interact with SQL & Tabular Databases Using LLMs 🚀

In this video, together we will go through all the steps necessary to design a ChatBot APP to interact with SQL and Tabular Databases using natural language, SQL LLM agents, and GPT 3.5. We will design a Chatbot that can: 1. Chat with SQL DB that we create from SQL files 2. Chat with SQL DB that we create from CSV and XLSX files 3. Chat with SQL DB that we create by uploading documents while using the chatbot 4. RAG with Tabular data Moreover, in this video, I will show you why RAG is not a good option for interacting with your databases. The code is available on the Github repository. 🚀 GitHub Repositories: Advanced Q&A and RAG series: https://github.com/Farzad-R/Advanced-QA-and-RAG-Series LLM-Zero-To-Hundred Series: https://github.com/Farzad-R/LLM-Zero-to-Hundred 00:00 Intro 00:41 Roadmap (Q&A vs RAG) 05:59 Resources of the first project 09:35 Project schema walk-through 13:19 Test your GPT and Embedding models (Notebook) 14:44 How to load environment variables (.env file) 17:26 Step 1.1: Create the SQL database from .sqlfile 19:00 Step 1.2: Create the SQL database from CSV and XLSX files 20:22 Step 1.3: Create the VectorDB from CSV and XLSX files 21:25 Step 2: Test your SQL database (Notebook) 22:15 Step 3: Step-by-step guide for Q&A with the SQL database created from SQL file (Notebook) 31:15 Step 4: Q&A with the SQL database created from CSV and XLSX files (Notebook) 36:15 Step 5: RAG with SQL databases and Tabular Data (Notebook) 42:34 ChatBot GUI brief backend walk-through 47:00 UI walk-through 48:25 Demo: Q&A with SQL DB created from .sql file 50:36 Demo: Q&A with SQL DB created from CSV and XLSX files 53:03 Demo: RAG with VectorDB created from CSV and XLSX files 54:45 Demo: Q&A with SQL DB created from uploaded CSV and XLSX files 56:55 Keynotes Langchain SQL Agent: https://python.langchain.com/docs/use_cases/sql/ Frameworks: #langchain , #openai, gradio, SQLite #chatbot #rag #llm #agent #python #gpt