Fast-track RAG: Chat with SQL Databases using Few-Shot Learning and Gemini | Streamlit | LangChain

About this lesson

In this video tutorial, I'll guide you through the development of a RAG application designed to chat with SQL databases, eliminating the need for consistently coding complex SQL queries. Employing few-shot learning, I instruct the Large Language Model (LLM) to adapt to specific database schemas through a limited set of examples. Gemini-Pro, a language model from Google, is utilized for this purpose. The front end of the application is developed using Streamlit, providing an intuitive interface for users. 💡In this video, we cover: 🔍 SQL Query Generation: The application is capable of generating SQL queries based on user input. 🧠 Few-shot learning techniques are employed to learn the LLM for the specific database schema. 🔄 Schema Adaptation: The application dynamically adapts to changes in the database schema. 🖥️ The front end of the application is developed using Streamlit. 🔥 Don't forget to 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲, 𝐬𝐦𝐚𝐬𝐡 the 𝗹𝗶𝗸𝗲 𝐛𝐮𝐭𝐭𝐨𝐧, and 𝐭𝐮𝐫𝐧 𝐨𝐧 the 𝐧𝐨𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐛𝐞𝐥𝐥🔔 for more 𝗲𝘅𝗰𝗶𝘁𝗶𝗻𝗴 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 and 𝘁𝘂𝘁𝗼𝗿𝗶𝗮𝗹𝘀. 🚀 Timestamps: 0:00 Introduction 1:55 Demo 2:55 Project Workflow Explanation 4:50 Database Overview 5:40 Installation Procedures 6:40 Database Connection 8:29 Loading LLM for SQL Query Generation 9:58 Utilizing Few-Shot Learning 14:03 Integrating LLM for Natural Responses 16:26 Creating Utils File 21:21 Streamlit App Design 26:30 App Testing 29:28 Conclusion Links: 💻 GitHub repo for code: https://github.com/Eduardovasquezn/rag-sql-reader ☕️ Buy me a coffee... or an iced tea: https://www.buymeacoffee.com/eduardov 👔 LinkedIn: https://www.linkedin.com/in/eduardo-vasquez-n/ #LLM #RAGApplication #SQLDatabase #FewShotLearning #LangChain #Gemini #Streamlit #SQLQueryGeneration #SchemaAdaptation #FrontEndDevelopment #Tutorial #DatabaseProgramming #AI #GenerativeAI #DataScience #Python

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