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+# SQLchat
+
+This project is a **SQL Chatbot** built with **LangChain** and **Streamlit**, designed to generate SQL queries and execute queries 
+based on database table schemas and structure. The chatbot can interact with users to understand their requirements 
+and translate them into SQL queries, leveraging relational database information provided via URI and schema definitions.
+
+## Features
+
+- **SQL Query Generator**: Automatically generates SQL queries based on user inputs and database structure.
+- **SQL Query Execution**: Automatically executes SQL queries generated by chatbot.
+- **Interactive Chat Interface**: Built with Streamlit for a user-friendly conversational experience.
+- **Database Schema Integration**: Parses table schemas from a database URI to provide accurate SQL generation capabilities.
+- **Customizable LLM Configuration**: Supports various large language models (LLMs) for generating responses.
+
+## Installation
+
+1. Clone the repository:
+
+   ```bash
+   git clone https://github.com/arthiondaena/SQLchat.git
+   cd SQLchat
+   ```
+
+2. Set up a virtual environment:
+
+   ```bash
+   python -m venv venv
+   source venv/bin/activate    # On Windows: venv\Scripts\activate
+   ```
+
+3. Install dependencies:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+   
+## Usage
+
+Run the application using Streamlit:
+
+```bash
+streamlit run app.py
+```
+
+This will launch the chatbot interface in your default web browser. The chatbot can then process user inputs and generate SQL queries based on the database schema.
+
+## Setup
+
+1. **Configure Database Connection**:
+   - Set up the `URI` configuration in the streamlit app to connect to your relational database.
+   - Ensure the database has the necessary permissions to allow schema queries.
+
+2. **Table Schemas**:
+   - The chatbot extracts table structures and schemas from the database for generating SQL queries. Make sure the database contains valid schema definitions.
+
+3. **API Key Configuration**:
+   - Provide your Groq API key for LLM integration within the script.
+
+4. **System Prompt Customization**:
+   - Adjust the instructions as per your specific SQL generation use case.
+   - The chatbot can remember upto last 4 conversations.
+
+## Features in Detail
+
+1. **SQL Query Generation**:
+   - The chatbot uses relational database schemas to intelligently generate SQL queries.
+   - Supports basic and complex queries tailored to the provided database structure.
+
+2. **Database Schema Utilization**:
+   - Extracts table information (columns, types, relationships) from the connected database.
+   - Leverages this knowledge to produce highly precise SQL queries.
+
+3. **Customizable Model Prompts**:
+   - Custom system prompts and instructions can be added to suit diverse database use cases.
+
+## Example Workflow
+1. Connect the chatbot to your database by specifying the database URI.
+2. Provide the chatbot with your SQL query requirement in plain language (e.g., "Fetch the top 10 customers by revenue").
+3. The chatbot generates and returns an accurate SQL query based on the schema.