Qpossible is a Streamlit application designed to streamline database analysis and provide insights using large language models (LLMs). It supports multiple database types and enables efficient interaction with SQL databases through natural language queries.
- Database Analysis: Use LLMs to analyze database content and structures, and suggest various types of analysis that can be performed on the data.
- SQL Query Generator: Transform natural language queries into SQL commands, making it easier for non-technical users to interact with databases.
- SQL Query Executor: Execute SQL queries directly within the app and retrieve results instantly.
- Data Plotting: Generate charts and graphs from query results for easy data interpretation.
- SQL Chat: Engage in a conversational interface with the database to retrieve information through dialogues.
- Multi-Database Support: Qpossible currently supports both SQLite and PostgreSQL databases.
- LLM Integrations: Seamlessly connect with LLM providers like Ollama, Groq, and OpenAI for enhanced data analysis.
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Clone the repository:
git clone https://github.com/admi386/Qpossible.git cd Qpossible -
Install dependencies: pip install -r requirements.txt
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Launch the application: streamlit run app.py
https://qpossible.streamlit.app
This project is licensed under the Apache License 2.0