VMind

Not only automatic, but also intelligent. An Intelligent data Visualization System, based on LLM.

Stars: 142

Visit

VMind is an open-source solution for intelligent visualization, providing an intelligent chart component based on LLM by VisActor. It allows users to create chart narrative works with natural language interaction, edit charts through dialogue, and export narratives as videos or GIFs. The tool is easy to use, scalable, supports various chart types, and offers one-click export functionality. Users can customize chart styles, specify themes, and aggregate data using LLM models. VMind aims to enhance efficiency in creating data visualization works through dialogue-based editing and natural language interaction.

README:

VMind

Not just automatic, but also fantastic.Open-source solution for intelligent visualization.

Introduction • Demo • Tutorial • API• OpenApi

English | 简体中文 | 日本語

Introduction

@visactor/vmind is an intelligent chart component based on LLM provided by VisActor, including dialog-based chart generation and editing capabilities. It provides a natural language interaction interface, allowing you to easily create chart narrative works with @visactor/VMind with just one sentence, and edit them through continuous dialogue, greatly improving your efficiency in creating data visualization works.

The main features of @visactor/vmind include:

Easy to use: Just provide the data you want to display and a sentence describing the information you want to display, and @visactor/vmind will automatically generate the chart for you. Based on the existing chart, describe the modifications you want to make to the chart in one sentence, and @visactor/VMind will help you achieve the desired effect.
Strong scalability: The components of @visactor/VMind can be easily extended and customized, and new functions and features can be added as needed. By default, the OpenAI GPT model is used, and you can easily replace it with any LLM service.
Easy narrative: Based on the powerful chart narrative ability of @visactor/vchart, @visactor/VMind supports the generation of various types of charts, including line charts, bar charts, pie charts, etc., and can also generate dynamic bar charts and other dynamic charts, making it easy for you to narrate data. More chart types are being added. You can also use the dialog-based editing function to easily modify chart styles and animation effects, making it easy for you to create narratives.
One-click export: @visactor/VMind comes with a chart export module, and you can export the created chart narrative as a video or GIF for display.

Development Guide

Docs Page

Enter the VMind repository and execute:

# Install dependencies
$ rush update
# Start the demo page
$ rush docs

Start the Development Page

Enter the VMind repository and execute:

# Install dependencies
$ rush update
# Start the VMind development page
$ rush vmind

You can start the vmind development page. You need to set your LLM service URL and API key to use it normally. You can modify the headers when calling the LLM in packages/vmind/__tests__/browser/src/pages/DataInput.tsx. You can create a new .env.local file in the packages/vmind folder and write in it:

VITE_SKYLARK_URL="Your service url of skylark model"
VITE_GPT_URL="Your service url of gpt model"
VITE_SKYLARK_KEY="Your api-key of skylark model"
VITE_GPT_KEY="Your api-key of gpt model"
VITE_PROXY_CONFIG="Your Vite proxy config for forwarding requests. Must be in JSON string format and is optional. Example: {"proxy": {"/v1": {"target": "https://api.openai.com/","changeOrigin": true},"/openapi": {"target": "https://api.openai.com/","changeOrigin": true}}}"

These configurations will be automatically loaded when starting the development environment.

Project Structure

__tests__: Playground for development
src/common: Common data processing, chart recommendation methods, chart generation pipelines
src/gpt: Code related to gpt intelligent chart generation
src/skylark: Code related to skylark intelligent chart generation
src/chart-to-video: Code related to exporting videos, GIFs

Instructions for use

📦 Installation

# npm
$ npm install @visactor/vmind

# yarn
$ yarn add @visactor/vmind

📊 Usage Example

Intelligent Chart Generation

First, we need to install VMind in the project:

# Install with npm

npm install @visactor/vmind

# Install with yarn

yarn add @visactor/vmind

Next, import VMind at the top of the JavaScript file

import VMind from '@visactor/vmind';

VMind currently supports OpenAI GPT-3.5, GPT-4 models and skylark-pro series models. Users can specify the model type to be called when initializing the VMind object, and pass in the URL of the LLM service. Next, we initialize a VMind instance and pass in the model type and model url:

import { Model } from '@visactor/vmind';

const vmind = new VMind({
  url: LLM_SERVICE_URL, //URL of the LLM service
  model: Model.SKYLARK, //Currently supports gpt-3.5, gpt-4, skylark pro models. The specified model will be called in subsequent chart generation
  headers: {
    'api-key': LLM_API_KEY
  } //headers will be used directly as the request header in the LLM request. You can put the model api key in the header
});

Here is a list of supported models:

//models that VMind support
//more models are under development
export enum Model {
  GPT3_5 = 'gpt-3.5-turbo',
  GPT4 = 'gpt-4',
  SKYLARK = 'skylark-pro',
  SKYLARK2 = 'skylark2-pro-4k'
}

VMind supports datasets in both CSV and JSON formats.

To use CSV data in subsequent processes, you need to call the data processing method to extract field information and convert it into a structured dataset. VMind provides a rule-based method parseCSVData to obtain field information:

// Pass in the CSV string to obtain the fieldInfo and the JSON-structured dataset
const { fieldInfo, dataset } = vmind.parseCSVData(csv);

We can also call the getFieldInfo method to obtain the fieldInfo by passing in a JSON-formatted dataset:

// Pass in a JSON-formatted dataset to obtain the fieldInfo
const dataset=[
{
"Product name": "Coke",
"region": "south",
"Sales": 2350
},
{
"Product name": "Coke",
"region": "east",
"Sales": 1027
},
{
"Product name": "Coke",
"region": "west",
"Sales": 1027
},
{
"Product name": "Coke",
"region": "north",
"Sales": 1027
}
]
const fieldInfo = vmind.getFieldInfo(dataset);

We want to show "the changes in sales rankings of various car brands". Call the generateChart method and pass the data and display content description directly to VMind:

const userPrompt = 'show me the changes in sales rankings of various car brand';
//Call the chart generation interface to get spec and chart animation duration
const { spec, time } = await vmind.generateChart(userPrompt, fieldInfo, dataset);

In this way, we get the VChart spec of the corresponding dynamic chart. We can render the chart based on this spec:

import VChart from '@visactor/vchart';

<body>
<!-- Prepare a DOM with size (width and height) for vchart, of course you can also specify it in the spec configuration -->
<div id="chart" style="width: 600px;height:400px;"></div>
</body>

// Create a vchart instance
const vchart = new VChart(spec, { dom: 'chart' });
// Draw
vchart.renderAsync();

Thanks to the capabilities of the large language model, users can describe more requirements in natural language and "customize" dishes. Users can specify different theme styles (currently only gpt chart generation supports this feature). For example, users can specify to generate a tech-style chart:

//userPrompt can be in both Chinese and English
//Specify to generate a tech-style chart
const userPrompt = 'show me the changes in sales rankings of various car brand,tech style';
const { spec, time } = await vmind.generateChart(userPrompt, fieldInfo, dataset);

You can also specify the chart type, field mapping, etc. supported by VMind. For example:

//Specify to generate a line chart, with car manufacturers as the x-axis
const userPrompt =
  'show me the changes in sales rankings of various car brands,tech style.Using a line chart, Manufacturer makes the x-axis';
const { spec, time } = await vmind.generateChart(userPrompt, fieldInfo, dataset);

Customizing LLM Request Method

Pass parameters when initializing the VMind object:

import VMind from '@visactor/vmind';
const vmind = new VMind(openAIKey:string, params:{
url?: string;//URL of the LLM service
/** gpt request header, which has higher priority */
headers?: Record<string, string> ;//request headers
method?: string;//request method POST GET
model?: string;//model name
max_tokens?: number;
temperature?: number;//recommended to set to 0
})

Specify your LLM service url in url (default is https://api.openai.com/v1/chat/completions) In subsequent calls, VMind will use the parameters in params to request the LLM service url.

Data Aggregation

📢 Note: The data aggregation function only supports GPT series models, more models will come soon.

When using the chart library to draw bar charts, line charts, etc., if the data is not aggregated, it will affect the visualization effect. At the same time, because no filtering and sorting of fields has been done, some visualization intentions cannot be met, for example: show me the top 10 departments with the most cost, show me the sales of various products in the north, etc.

VMind supports intelligent data aggregation since version 1.2.2. This function uses the data input by the user as a data table, uses a LLM to generate SQL queries according to the user's command, queries data from the data table, and uses GROUP BY and SQL aggregation methods to group, aggregate, sort, and filter data. Supported SQL statements include: SELECT, GROUP BY, WHERE, HAVING, ORDER BY, LIMIT. Supported aggregation methods are: MAX(), MIN(), SUM(), COUNT(), AVG(). Complex SQL operations such as subqueries, JOIN, and conditional statements are not supported.

Use the dataQuery function of the VMind object to aggregate data. This method has three parameters:

userInput: user input. You can use the same input as generateChart
fieldInfo: Dataset field information. The same as generateChart, it can be obtained by parseCSVData, or built by the user.
dataset: Dataset. The same as generateChart, it can be obtained by parseCSVData, or built by the user.

const { fieldInfo, dataset } = await vmind?.dataQuery(userInput, fieldInfo, dataset);

The fieldInfo and dataset returned by this method are the field information and dataset after data aggregation, which can be used for chart generation. By default, the generateChart function will perform a data aggregation using the same user input before generating the chart. You can disable data aggregation by passing in the fourth parameter:

const userInput = 'show me the changes in sales rankings of various car brand';
const { spec, time } = await vmind.generateChart(userInput, fieldInfo, dataset, false); //pass false as the forth parameter to disable data aggregation before generating a chart.

Dialog-based editing

Under development, stay tuned

Effect display

Dynamic bar chart

Bar chart

Pie chart

Contribution

If you would like to contribute, please read the Code of Conduct and Guide first。

Small streams converge to make great rivers and seas!

License

MIT License

For Tasks:

Click tags to check more tools for each tasks

create dynamic charts customize chart styles export chart narratives aggregate data intelligently interact with charts using natural language

For Jobs:

data analyst data scientist business intelligence analyst visualization specialist ai engineer

Alternative AI tools for VMind

Similar Open Source Tools

VMind

github

: 142

LazyLLM

LazyLLM is a low-code development tool for building complex AI applications with multiple agents. It assists developers in building AI applications at a low cost and continuously optimizing their performance. The tool provides a convenient workflow for application development and offers standard processes and tools for various stages of application development. Users can quickly prototype applications with LazyLLM, analyze bad cases with scenario task data, and iteratively optimize key components to enhance the overall application performance. LazyLLM aims to simplify the AI application development process and provide flexibility for both beginners and experts to create high-quality applications.

github

: 870

web-llm

WebLLM is a modular and customizable javascript package that directly brings language model chats directly onto web browsers with hardware acceleration. Everything runs inside the browser with no server support and is accelerated with WebGPU. WebLLM is fully compatible with OpenAI API. That is, you can use the same OpenAI API on any open source models locally, with functionalities including json-mode, function-calling, streaming, etc. We can bring a lot of fun opportunities to build AI assistants for everyone and enable privacy while enjoying GPU acceleration.

github

: 13.1k

weave

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

github

: 685

instructor-js

Instructor is a Typescript library for structured extraction in Typescript, powered by llms, designed for simplicity, transparency, and control. It stands out for its simplicity, transparency, and user-centric design. Whether you're a seasoned developer or just starting out, you'll find Instructor's approach intuitive and steerable.

github

: 299

lerobot

LeRobot is a state-of-the-art AI library for real-world robotics in PyTorch. It aims to provide models, datasets, and tools to lower the barrier to entry to robotics, focusing on imitation learning and reinforcement learning. LeRobot offers pretrained models, datasets with human-collected demonstrations, and simulation environments. It plans to support real-world robotics on affordable and capable robots. The library hosts pretrained models and datasets on the Hugging Face community page.

github

: 6.8k

AnyGPT

AnyGPT is a unified multimodal language model that utilizes discrete representations for processing various modalities like speech, text, images, and music. It aligns the modalities for intermodal conversions and text processing. AnyInstruct dataset is constructed for generative models. The model proposes a generative training scheme using Next Token Prediction task for training on a Large Language Model (LLM). It aims to compress vast multimodal data on the internet into a single model for emerging capabilities. The tool supports tasks like text-to-image, image captioning, ASR, TTS, text-to-music, and music captioning.

github

: 730

storm

STORM is a LLM system that writes Wikipedia-like articles from scratch based on Internet search. While the system cannot produce publication-ready articles that often require a significant number of edits, experienced Wikipedia editors have found it helpful in their pre-writing stage. **Try out our [live research preview](https://storm.genie.stanford.edu/) to see how STORM can help your knowledge exploration journey and please provide feedback to help us improve the system 🙏!**

github

: 12.1k

cognee

Cognee is an open-source framework designed for creating self-improving deterministic outputs for Large Language Models (LLMs) using graphs, LLMs, and vector retrieval. It provides a platform for AI engineers to enhance their models and generate more accurate results. Users can leverage Cognee to add new information, utilize LLMs for knowledge creation, and query the system for relevant knowledge. The tool supports various LLM providers and offers flexibility in adding different data types, such as text files or directories. Cognee aims to streamline the process of working with LLMs and improving AI models for better performance and efficiency.

github

: 854

llama_index

LlamaIndex is a data framework for building LLM applications. It provides tools for ingesting, structuring, and querying data, as well as integrating with LLMs and other tools. LlamaIndex is designed to be easy to use for both beginner and advanced users, and it provides a comprehensive set of features for building LLM applications.

github

: 36.1k

AutoNode

AutoNode is a self-operating computer system designed to automate web interactions and data extraction processes. It leverages advanced technologies like OCR (Optical Character Recognition), YOLO (You Only Look Once) models for object detection, and a custom site-graph to navigate and interact with web pages programmatically. Users can define objectives, create site-graphs, and utilize AutoNode via API to automate tasks on websites. The tool also supports training custom YOLO models for object detection and OCR for text recognition on web pages. AutoNode can be used for tasks such as extracting product details, automating web interactions, and more.

github

: 116

Bard-API

The Bard API is a Python package that returns responses from Google Bard through the value of a cookie. It is an unofficial API that operates through reverse-engineering, utilizing cookie values to interact with Google Bard for users struggling with frequent authentication problems or unable to authenticate via Google Authentication. The Bard API is not a free service, but rather a tool provided to assist developers with testing certain functionalities due to the delayed development and release of Google Bard's API. It has been designed with a lightweight structure that can easily adapt to the emergence of an official API. Therefore, using it for any other purposes is strongly discouraged. If you have access to a reliable official PaLM-2 API or Google Generative AI API, replace the provided response with the corresponding official code. Check out https://github.com/dsdanielpark/Bard-API/issues/262.

github

: 5.4k

tensorrtllm_backend

The TensorRT-LLM Backend is a Triton backend designed to serve TensorRT-LLM models with Triton Inference Server. It supports features like inflight batching, paged attention, and more. Users can access the backend through pre-built Docker containers or build it using scripts provided in the repository. The backend can be used to create models for tasks like tokenizing, inferencing, de-tokenizing, ensemble modeling, and more. Users can interact with the backend using provided client scripts and query the server for metrics related to request handling, memory usage, KV cache blocks, and more. Testing for the backend can be done following the instructions in the 'ci/README.md' file.

github

: 664

probsem

ProbSem is a repository that provides a framework to leverage large language models (LLMs) for assigning context-conditional probability distributions over queried strings. It supports OpenAI engines and HuggingFace CausalLM models, and is flexible for research applications in linguistics, cognitive science, program synthesis, and NLP. Users can define prompts, contexts, and queries to derive probability distributions over possible completions, enabling tasks like cloze completion, multiple-choice QA, semantic parsing, and code completion. The repository offers CLI and API interfaces for evaluation, with options to customize models, normalize scores, and adjust temperature for probability distributions.

github

: 72

rosa

ROSA is an AI Agent designed to interact with ROS-based robotics systems using natural language queries. It can generate system reports, read and parse ROS log files, adapt to new robots, and run various ROS commands using natural language. The tool is versatile for robotics research and development, providing an easy way to interact with robots and the ROS environment.

github

: 256

ActionWeaver

ActionWeaver is an AI application framework designed for simplicity, relying on OpenAI and Pydantic. It supports both OpenAI API and Azure OpenAI service. The framework allows for function calling as a core feature, extensibility to integrate any Python code, function orchestration for building complex call hierarchies, and telemetry and observability integration. Users can easily install ActionWeaver using pip and leverage its capabilities to create, invoke, and orchestrate actions with the language model. The framework also provides structured extraction using Pydantic models and allows for exception handling customization. Contributions to the project are welcome, and users are encouraged to cite ActionWeaver if found useful.

github

: 296

For similar tasks

VMind

github

: 142

For similar jobs

sweep

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

github

: 7.1k

teams-ai

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

github

: 390

ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

github

: 159

classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

github

: 572

chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

github

: 27.7k

BricksLLM

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

github

: 897

uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

: 951

griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.0k