Querypls is a web application that provides an interactive chat interface, simplifying SQL query generation. Users can effortlessly enter SQL queries and receive corresponding results. The application harnesses the capabilities of the language models from Hugging Face to generate SQL queries based on user input.
💬 Interactive chat interface for easy communication.
🔍 Enter SQL queries and receive query results as responses.
🤖 Utilizes language models from Hugging Face for advanced query generation (Querypls-prompt2sql).
💻 User-friendly interface for seamless interaction.
🔒 Secure Google Authentication for OAuth2 integration.
🔄 Chat history recording for easy reference.
Querypls received a shoutout from 🦜 🔗 Langchain on their Twitter, reaching over 60,000 impressions. Additionally, it was featured under the Community Favorite Projects section on 🦜 🔗 Langchain's blog, leading to a significant increase in stars for this repository and a growing user base. The project was also highlighted in a YouTube video, and it also caught the attention of Backdrop, expressing their interest and liking in an email, inviting the project to be a part of their hackathon.
| 🔗 Langhchain Twitter Post | 🔗 Langhcain Blog Post |
|---|---|
 |
 |
| 🎥 YouTube Video | Backdrop Hackathon Invitation |
 |
 |
A big thank you to Langchain for their support and recognition!
-
Clone the repository:
git clone https://github.com/samadpls/Querypls.git
-
Navigate to the project directory:
cd Querypls -
Install dependencies:
pip install -r requirements.txt
-
Create a
.envfile based on.env_exampleand set the necessary variables. -
Run the application:
streamlit run src/app.py
-
Open the provided link in your browser to use Querypls.
This project is licensed under the MIT License. See the LICENSE file for details.
Note
Querypls, while powered by a 7B model of Satablility AI LLM Model, is currently limited in providing optimal responses for complex queries involving PLSQL or intricate scenarios with multiple table joins.
Made with 🤍 by samadpls