gem-hunter
主要言語: TypeScript
Chat with your data - AI data analysis and visualization on CSV, Postgres, MySQL, Snowflake, SQLite...
💬 Chat with your data using natural language 📊
Introducing DataLine, the simplest and fastest way⚡️ to analyze and visualize your data!
Generate and export charts, tables, reports in seconds with DataLine - Your AI-driven data analysis and visualization tool 🤓
⚠️ We're looking for maintainers! If you're interested, message me on Discord ⚠️
Technical or non-technical people who want to explore data, fast. ⚡️⚡️
It also works for backend developers to speed up drafting queries and explore new DBs with ease. 😎
It's especially well-suited for businesses given its security-first 🔒 and open-source 📖 nature.
DataLine is an AI-driven data analysis and visualization tool.
It's privacy-focused, storing everything on your device. No ☁️, only ☀️!
It hides your data from the LLMs used by default, but this can be disabled if the data is not deemed sensitive.
It can connect to a variety of data sources (Postgres, Snowflake, MySQL, Azure SQL Server, Microsoft SQL Server, Excel, SQLite, CSV, sas7bdat, and more), execute queries, generate charts, and allow for copying the results to build reports quickly.
For now, we're trying to help people get insights out of their data, fast.
This is meant to enable non-technical folks to query data and aid data analysts in getting their jobs done 10x as fast.
But you can still influence the direction we go in. We're building this for you, so you have the biggest say.
With a lot more coming soon. You can still influence what we build, so if you're a user and you're down for it, we'd love to interview you! Book some time with one of us here:
There are multiple ways of setting up DataLine, simplest being using a binary executable. This allows you to download a file and run it to get started.
A more flexible option is using our hosted Docker image. This allows you to setup authentication and other features if you need them.
Head over to our releases page, and open the most recent one. There you should find a windows-exe.zip file. Download it, unzip it, and run the DataLine.exe file.
You might get a "Windows protected your PC" message, which is normal (more info -> run anyway). Finally, open http://localhost:7377/ in your browser.
Homebrew
# install dataline brew tap ramiawar/dataline brew install dataline # run dataline dataline
If you don't like Homebrew, a binary can be found in the latest release!
DataLine should then be running on port 7377 accessible from your browser: http://localhost:7377
You can use Homebrew, see the Mac section.
You may also wish to use the binary instead, to do so, follow the instructions in the Windows section, and use the dataline-linux.tar.zip file instead.
You can also use our official docker image and get started in one command. This is more suitable for business use:
docker run -p 7377:7377 -v dataline:/home/.dataline --name dataline ramiawar/dataline:latest
You can manage this as you would any other container. docker start dataline, docker stop dataline
For updating to a new version, just remove the container and rerun the command. This way the volume is persisted across updates.
docker rm dataline docker run -p 7377:7377 -v dataline:/home/.dataline --name dataline ramiawar/dataline:latest
To connect to the frontend, you can then visit: http://localhost:7377
Same as installation, just replace old exe with new exe! Your data will still be there across versions.
Homebrew (retains your data, don't worry about that!)
brew update && brew upgrade datalineIf you don't like Homebrew, a binary can be found in the latest release! Data will still be retained there as well.
If using Homebrew, same as above. Otherwise simply replace the old binary with the new one!
For updating to a new version, just remove the container and rerun the command. This way the volume is persisted across updates.
docker rm dataline docker run -p 7377:7377 -v dataline:/home/.dataline --name dataline ramiawar/dataline:latest
Feeling spicy are we? 🌶️ There are a few things you should know. DataLine is split into two parts: the backend and the frontend.
The backend is a Python FastAPI server, and the frontend is a React app. The frontend also includes our landing page, so you need to set up an env var first!
Check the backend and frontend readmes.
DataLine also supports basic auth 🔒 in self-hosted mode 🥳 in case you're hosting it and would like to secure it with a username/password.
Auth is NOT supported ❌ when running the DataLine executable.
To enable authentication on the self-hosted version, add the environment variables AUTH_USERNAME and AUTH_PASSWORD while launching the service. ✅
Inject the env vars with the docker run command as follows:
docker run -p 7377:7377 -v dataline:/home/.dataline --name dataline -e AUTH_USERNAME=admin -e AUTH_PASSWORD=admin ramiawar/dataline:latest
We plan on supporting multiple user auth in the future, but for now it supports a single user by default.
Go through the following checklist to explore DataLine's features!
See instructions file for more details.
The one thing you must configure when deploying DataLine to a custom domain is CORS allowed origins.
To do this, add the environment variable ALLOWED_ORIGINS (comma separated list of origins) to your domain name(s).
By default, it is set to http://localhost:7377,http://0.0.0.0:7377 to make it work with local Docker and local binaries.
For example, running the docker image on a remote server with IP 123.123.12.34:
docker run -p 7377:7377 -v dataline:/home/.dataline --name dataline -e ALLOWED_ORIGINS="http://123.123.12.34:7377,https://123.123.12.34:7377" ramiawar/dataline:latestWe support excel files, but they will have to conform to some structure for the time being. We also support multiple sheets - each sheet will be ingested as a separate table.
Right now, we will try to automatically detect the 'header row' and the first column based on some manual data processing (so as to keep things secure). This means that we might detect the wrong things if you have extra rows on top / logos / branding elements.
To ensure the best quality, make sure your first row is the column names, and remove any padding rows/columns from all the sheets. If any sheet fails, the import will fail.
Future improvements to this include optionally allowing LLMs to figure out what the header row is to reduce user effort.
Thanks goes to these wonderful people (emoji key):
 Rami Awar 💻 🎨 📖 🚇 📦 📝 🐛 |
 anthony2261 💻 🤔 🚇 📦 🧑🏫 🚧 🐛 |
 Walusimbi Mahad 💻 🤔 |
 Péter Gyarmati 💻 📖 |
 Joe Haddad 💻 📖 |
 maryamalki 💻 📖 |
 Filip Katušin 💻 🐛 |
This project follows the all-contributors specification. Contributions of any kind welcome!