litlytics
🔥 LitLytics - an affordable, simple analytics platform that leverages LLMs to automate data analysis
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LitLytics is an affordable analytics platform leveraging LLMs for automated data analysis. It simplifies analytics for teams without data scientists, generates custom pipelines, and allows customization. Cost-efficient with low data processing costs. Scalable and flexible, works with CSV, PDF, and plain text data formats.
README:
LitLytics is an affordable, simple analytics platform that leverages LLMs to automate data analysis. It was designed to help teams without dedicated data scientists gain insights from their data.
- No Data Science Expertise Required: LitLytics simplifies the entire analytics process, making it accessible to anyone.
- Automatic Pipeline Generation: Describe your analytics task in plain language, and LitLytics will generate a custom pipeline for you.
- Customizable Pipelines: You can review, update, or modify each step in the analytics pipeline to suit your specific needs.
- Cost-Efficient: Leveraging modern LLMs allows LitLytics to keep the cost of processing data incredibly low — typically fractions of a cent per document.
- Scalable & Flexible: Works with various data formats including CSV, PDF, and plain text.
Watch the demo video for more detailed intro.
Make sure you have Docker installed.
Then, start LitLytics from image by running following command:
docker run -d -p 3000:3000 ghcr.io/yamalight/litlytics:latest
This will launch the platform inside a docker container, and you will be able to interact with it in your browser: http://localhost:3000.
Make sure you have Bun (>=1.1.0) installed.
Clone this repository:
git clone https://github.com/yamalight/litlytics.git
cd litlytics
Install dependencies:
bun install
And finally start the LitLytics platform:
bun run dev
This will launch the platform, and you will be able to interact with it in your browser: http://localhost:5173.
POST /api/execute
endpoint executes pipeline using given LLM provider and model.
The body should be a JSON object with the following fields:
- provider: The language model provider you wish to use.
-
model (
LLMModel
): The specific model to use, based on the selected provider. -
key (
string
): The API key to authenticate with the specified provider. -
pipeline (
Pipeline
): The configuration for the processing pipeline.
Example request:
{
"provider": "openai",
"model": "gpt-4o-mini",
"key": "sk-your-api-key",
"pipeline": {
// your pipeline configuration
}
}
A response will include new pipeline config that includes results of the task execution.
Contributions are welcome! If you’d like to contribute to LitLytics, please fork the repository and submit a pull request with your changes.
- Fork the repo
- Create your feature branch (
git checkout -b feature/YourFeature
) - Commit your changes (
git commit -m 'Add YourFeature'
) - Push to the branch (
git push origin feature/YourFeature
) - Open a pull request
This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0). This license ensures that the software remains free and open, even when used as part of a network service. If you modify or distribute the project (including deploying it as a service), you must also make your changes available under the same license.
If your use case requires a proprietary license (for example, you do not wish to open-source your modifications or need a more flexible licensing arrangement), we offer commercial and enterprise licenses. Please contact us to discuss licensing options tailored to your needs.
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