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dataline
Chat with your data - AI data analysis and visualization on CSV, Postgres, MySQL, Snowflake, SQLite...
Stars: 1022
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DataLine is an AI-driven data analysis and visualization tool designed for technical and non-technical users to explore data quickly. It offers privacy-focused data storage on the user's device, supports various data sources, generates charts, executes queries, and facilitates report building. The tool aims to speed up data analysis tasks for businesses and individuals by providing a user-friendly interface and natural language querying capabilities.
README:
π¬ Chat with your data using natural language π
Introducing DataLine, the simplest and fastest wayβ‘οΈ to analyze and visualize your data!
Generate and export charts, tables, reports in seconds with DataLine - Your AI-driven data analysis and visualization tool π€
- Who is this for
- What is it
- Roadmap
- Feature Support
- Getting started
- Upgrading
- Running manually
- Authentication
- Startup Quest
- Supported Databases
- Deployment
Technical or non-technical people who want to explore data, fast. β‘οΈβ‘οΈ
It also works for backend developers to speed up drafting queries and explore new DBs with ease. π
It's especially well-suited for businesses given its security-first π and open-source π nature.
DataLine is an AI-driven data analysis and visualization tool.
It's privacy-focused, storing everything on your device. No βοΈ, only βοΈ!
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.
- [x] Broad DB support: Postgres, MySQL, Snowflake, Excel, CSV, SQLite, and more
- [x] Generating and executing SQL from natural language
- [x] Ability to modify SQL results, save them, and re-run
- [x] Better support for explorative questions
- [x] Querying data files like CSV, Excel, SQLite, sas7bdat (more connection types)
- [x] Charting via natural language
- [x] Modifying chart queries and re-rendering/refreshing charts
- [ ] Dashboards and triggers
- [ ] Knowledge base and 'trainable' examples (flavor of RAG)
- [ ] More advanced charting options (bubble, stacks, etc.)
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 dataline
If 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? πΆοΈ 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 π in self-hosted mode π₯³ in case you're hosting it and would like to secure it with a username/password.
Auth is NOT supported β 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. β
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!
- [ ] Create a sample database connection
- [ ] Create a new chat and rename it
- [ ] Start asking questions about your data and getting answers
- [ ] Refresh the page and re-run some SQL queries
- [ ] Click inside an SQL query, modify it, and save your modification for later!
- [ ] Try to modify your sample DB connection and explore the connection editor page
- [ ] Try asking for a chart!
- [ ] To really challenge it, ask a question that is answered with multiple results (charts, tables, etc.) - example
- [ ] Add a profile picture
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:latest
We 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):
This project follows the all-contributors specification. Contributions of any kind welcome!
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The official Node.js client for Pinecone, written in TypeScript. This client library provides a high-level interface for interacting with the Pinecone vector database service. With this client, you can create and manage indexes, upsert and query vector data, and perform other operations related to vector search and retrieval. The client is designed to be easy to use and provides a consistent and idiomatic experience for Node.js developers. It supports all the features and functionality of the Pinecone API, making it a comprehensive solution for building vector-powered applications in Node.js.
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aiohttp-pydantic
Aiohttp pydantic is an aiohttp view to easily parse and validate requests. You define using function annotations what your methods for handling HTTP verbs expect, and Aiohttp pydantic parses the HTTP request for you, validates the data, and injects the parameters you want. It provides features like query string, request body, URL path, and HTTP headers validation, as well as Open API Specification generation.
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gcloud-aio
This repository contains shared codebase for two projects: gcloud-aio and gcloud-rest. gcloud-aio is built for Python 3's asyncio, while gcloud-rest is a threadsafe requests-based implementation. It provides clients for Google Cloud services like Auth, BigQuery, Datastore, KMS, PubSub, Storage, and Task Queue. Users can install the library using pip and refer to the documentation for usage details. Developers can contribute to the project by following the contribution guide.
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aioconsole
aioconsole is a Python package that provides asynchronous console and interfaces for asyncio. It offers asynchronous equivalents to input, print, exec, and code.interact, an interactive loop running the asynchronous Python console, customization and running of command line interfaces using argparse, stream support to serve interfaces instead of using standard streams, and the apython script to access asyncio code at runtime without modifying the sources. The package requires Python version 3.8 or higher and can be installed from PyPI or GitHub. It allows users to run Python files or modules with a modified asyncio policy, replacing the default event loop with an interactive loop. aioconsole is useful for scenarios where users need to interact with asyncio code in a console environment.
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aiosqlite
aiosqlite is a Python library that provides a friendly, async interface to SQLite databases. It replicates the standard sqlite3 module but with async versions of all the standard connection and cursor methods, along with context managers for automatically closing connections and cursors. It allows interaction with SQLite databases on the main AsyncIO event loop without blocking execution of other coroutines while waiting for queries or data fetches. The library also replicates most of the advanced features of sqlite3, such as row factories and total changes tracking.