End to End Text to SQL LLM App along with Quering SQL database using langchain #artificial #python

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In this video, we dive into building a powerful Text-to-SQL application that converts natural language questions into SQL queries and interacts with an SQLite database. We leverage LangChain for generating SQL queries from text and Streamlit for creating an interactive web interface.

🌟 What You’ll Learn:

Database Setup: Learn how to create and populate an SQLite database with sample data using Python.
LangChain Integration: Discover how to use LangChain to convert natural language questions into SQL queries. We’ll cover configuration, generating SQL from text, and handling responses.
Streamlit Interface: Build a user-friendly web application using Streamlit. See how to capture user input, display generated SQL queries, and show query results.
🔧 Tools and Technologies:

LangChain: For natural language processing and SQL query generation.
Streamlit: To create a sleek and interactive web application.
SQLite: A lightweight database for managing and querying data.
Python: The programming language used for all tasks.
📜 Key Features:

Convert natural language questions into SQL queries.
Execute queries on a sample database and display results.
Simple and intuitive web interface for easy interaction.
📂 Resources:

LangChain Documentation
Streamlit Documentation
SQLite Documentation
GitHub Repository
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#LangChain #Streamlit #TextToSQL #Python #Database #AI #MachineLearning
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