https://github.com/jra333/snowflake-chat
This application allows you to query your Snowflake database using natural language. It converts your questions into SQL, executes them, and returns the results in an easy-to-understand format.
https://github.com/jra333/snowflake-chat
Last synced: 6 months ago
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This application allows you to query your Snowflake database using natural language. It converts your questions into SQL, executes them, and returns the results in an easy-to-understand format.
- Host: GitHub
- URL: https://github.com/jra333/snowflake-chat
- Owner: jra333
- Created: 2025-04-01T03:22:08.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-04-01T04:14:02.000Z (6 months ago)
- Last Synced: 2025-04-01T04:26:52.944Z (6 months ago)
- Language: Python
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Snowflake Chat (RAG Agent)
A local Streamlit application that allows you to query Snowflake data using natural language with the power of LangGraph agents.
[Click for streamlit live demo](https://snow-rag-chat.streamlit.app/) - Requires DB credentials and API key
## Features
- Natural language to SQL translation
- Interactive chat interface
- Query results in tabular format
- CSV and Excel export functionality
- LangGraph agent-based processing
## Setup Instructions
### Prerequisites
- Python 3.8+
- Snowflake account with appropriate credentials
- OpenAI API key
### Installation
1. Clone this repository:
```bash
git clone
cd snowflake-rag
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Create a `.env` file in the root directory with your credentials:
```
# OpenAI API Key
OPENAI_API_KEY=your_openai_api_key
# Snowflake Connection (optional, can also be entered in the UI)
SNOWFLAKE_ACCOUNT=your_account
SNOWFLAKE_USER=your_username
SNOWFLAKE_PASSWORD=your_password
SNOWFLAKE_WAREHOUSE=your_warehouse
SNOWFLAKE_DATABASE=your_database
SNOWFLAKE_SCHEMA=your_schema
```
### Running the Application
1. Start the Streamlit app:
```bash
streamlit run app/app.py
```
2. Open your browser and navigate to http://localhost:8501
3. If you didn't set up Snowflake credentials in the `.env` file, you'll need to enter them in the sidebar.
4. Enter your OpenAI API key in the sidebar if not already provided in the `.env` file.
5. Select a table from the dropdown and start asking questions!
## Usage
1. Connect to your Snowflake database using the connection form in the sidebar
2. Select a table to query from the dropdown menu
3. Enter a natural language question in the chat input, such as:
- "How many rows are in this table?"
- "What are the top 5 entries sorted by date?"
- "Show me records where the value is greater than 100"
4. View the results in the table view
5. Download results as CSV or Excel using the provided buttons
## Technology Stack
- Streamlit - UI framework
- LangChain - LLM framework
- LangGraph - Agent orchestration
- Snowflake Snowpark - Database connectivity
- OpenAI GPT - Natural language processing
## Examples of Natural Language Queries
- "How many records are in this table?"
- "What are the top 5 customers by order value?"
- "Show me sales data from last month"
- "What is the average transaction amount by product category?"
- "Find all records where the status is 'pending'"
## Security Considerations
This application includes several security features:
- Prevention of DROP, DELETE, and UPDATE operations
- Result set size limitations
- Input validation
- Secure credential handling
## Troubleshooting
- If you encounter connection issues, ensure your Snowflake credentials are correctly configured
- For SQL generation errors, try rephrasing your question to be more specific
- If export fails, check if your result set is within the size limitations
## Development
This application is built with:
- Streamlit
- LangChain
- Snowflake Snowpark Python
- LangGraph
- OpenAI's language models (GPT-4o-mini by default)