Package: lida
LIDA: Automatic Generation of Visualizations and Infographics using Large Language ModelsLIDA is a library for generating data visualizations and data-faithful infographics. LIDA is grammar agnostic (will work with any programming language and visualization libraries e.g. matplotlib, seaborn, altair, d3 etc) and works with multiple large language model providers (OpenAI, Azure OpenAI, PaLM, Cohere, Huggingface). Details on the components of LIDA are described in thepaper hereand in this tutorialnotebook. See the project pageherefor updates!.Note on Code Execution:To create visualizations, LIDAgeneratesandexecutescode.
Ensure that you run LIDA in a secure environment.FeaturesLIDA treatsvisualizations as codeand provides a clean api for generating, executing, editing, explaining, evaluating and repairing visualization code.Data SummarizationGoal GenerationVisualization GenerationVisualization EditingVisualization ExplanationVisualization Evaluation and RepairVisualization RecommendationInfographic Generation (beta) # pip install lida[infographics]fromlidaimportManager,llmlida=Manager(text_gen=llm("openai"))# palm, cohere ..summary=lida.summarize("data/cars.csv")goals=lida.goals(summary,n=2)# exploratory data analysischarts=lida.visualize(summary=summary,goal=goals[0])# exploratory data analysisGetting StartedSetup and verify that your python environment ispython 3.10or higher (preferably, useConda). Install the library via pip.pipinstalllidaOnce requirements are met, setup your api key. Learn more about setting up keys for other LLM providershere.exportOPENAI_API_KEY=<yourkey>Alternatively you can install the library in dev model by cloning this repo and runningpip install -e .in the repository root.Web API and UILIDA comes with an optional bundled ui and web api that you can explore by running the following command:lidaui--port=8080--docsThen navigate tohttp://localhost:8080/in your browser. To view the web api specification, add the--docsoption to the cli command, and navigate tohttp://localhost:8080/api/docsin your browser.The fastest and recommended way to get started after installation will be to try out the web ui above or run thetutorial notebook.Building the Web API and UI with DockerThe LIDA web api and ui can be setup using docker and the command below (ensure that you have docker installed, and you have set yourOPENAI_API_KEYenvironment variable).dockercomposeupData SummarizationGiven a dataset, generate a compact summary of the data.fromlidaimportManagerlida=Manager()summary=lida.summarize("data/cars.json")# generate data summaryGoal GenerationGenerate a set of visualization goals given a data summary.goals=lida.goals(summary,n=5,persona="ceo with aerodynamics background")# generate goalsAdd apersonaparameter to generate goals based on that persona.Visualization GenerationGenerate, refine, execute and filter visualization code given a data summary and visualization goal. Note that LIDA representsvisualizations as code.# generate charts (generate and execute visualization code)charts=lida.visualize(summary=summary,goal=goals[0],library="matplotlib")# seaborn, ggplot ..Visualization EditingGiven a visualization, edit the visualization using natural language.# modify chart using natural languageinstructions=["convert this to a bar chart","change the color to red","change y axes label to Fuel Efficiency","translate the title to french"]edited_charts=lida.edit(code=code,summary=summary,instructions=instructions,library=library,textgen_config=textgen_config)Visualization ExplanationGiven a visualization, generate a natural language explanation of the visualization code (accessibility, data transformations applied, visualization code)# generate explanation for chartexplanation=lida.explain(code=charts[0].code,summary=summary)Visualization Evaluation and RepairGiven a visualization, evaluate to find repair instructions (which may be human authored, or generated), repair the visualization.evaluations=lida.evaluate(code=code,goal=goals[i],library=library)Visualization RecommendationGiven a dataset, generate a set of recommended visualizations.recommendations=lida.recommend(code=code,summary=summary,n=2,textgen_config=textgen_config)Infographic Generation [WIP]Given a visualization, generate a data-faithful infographic. This methods should be considered experimental, and uses stable diffusion models from thepeacassolibrary. You will need to runpip install lida[infographics]to install the required dependencies.infographics=lida.infographics(visualization=charts[0].raster,n=3,style_prompt="line art")Using LIDA with Locally Hosted LLMs (HuggingFace)LIDA uses thellmxlibrary as its interface for text generation. llmx supports multiple local models including HuggingFace models. You can use the huggingface models directly (assuming you have a gpu) or connect to an openai compatible local model endpoint e.g. using the excellentvllmlibrary.Using HuggingFace Models Directly!pip3install--upgradellmx==0.0.17a0# Restart the colab sessionfromlidaimportManagerfromllmximportllmtext_gen=llm(provider="hf",model="uukuguy/speechless-llama2-hermes-orca-platypus-13b",device_map="auto")lida=Manager(text_gen=text_gen)# now you can call lida methods as above e.g.sumamry=lida.summarize("data/cars.csv")# ....Using an OpenAI Compatible Endpoint e.g.vllm serverfromlidaimportManager,TextGenerationConfig,llmmodel_name="uukuguy/speechless-llama2-hermes-orca-platypus-13b"model_details=[{'name':model_name,'max_tokens':2596,'model':{'provider':'openai','parameters':{'model':model_name}}}]# assuming your vllm endpoint is running on localhost:8000text_gen=llm(provider="openai",api_base="http://localhost:8000/v1",api_key="EMPTY",models=model_details)lida=Manager(text_gen=text_gen)Important Notes / Caveats / FAQsLIDA generates and executes code based on provided input. Ensure that you run LIDA in a secure environment with appropriate permissions.LIDA currently works best with datasets that have a small number of columns (<= 10). This is mainly due to the limited context size for most models. For larger datasets, consider preprocessing your dataset to use a subset of the columns.LIDA assumes the dataset exists and is in a format that can be loaded into a pandas dataframe. For example, a csv file, or a json file with a list of objects. In practices the right dataset may need to be curated and preprocessed to ensure that it is suitable for the task at hand.Smaller LLMs (e.g., OSS LLMs on Huggingface) have limited instruction following capabilities and may not work well with LIDA. LIDA works best with larger LLMs (e.g., OpenAI GPT 3.5, GPT 4).How reliable is the LIDA approach? The LIDApaperdescribes experiments that evaluate the reliability of LIDA using a visualization error rate metric. With the current version of prompts, data summarization techniques, preprocessing/postprocessing logic and LLMs, LIDA has an error rate of < 3.5% on over 2200 visualizations generated (compared to a baseline of over 10% error rate). This area is work in progress.Can I build my own apps with LIDA? Yes! You can either use the python api directly in your app or setup a web api endpoint and use the web api in your app. See theweb apisection for more details.How is LIDA related to OpenAI Code Interpreter: LIDA shares several similarities with code interpreter in the sense that both involve writing and executing code to address user intent. LIDA differs in its focus on visualization, providing a modular api for developer reuse and providing evaluation metrics on the visualization use case.Naturally, some of the limitations above could be addressed by a much welcomed PR.Community Examples Built with LIDALIDA + Streamlit:lida-streamlit,Documentation and CitationA short paper describing LIDA (Accepted at ACL 2023 Conference) is availablehere.@inproceedings{dibia2023lida,title="{LIDA}: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models",author="Dibia, Victor",booktitle="Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",month=jul,year="2023",address="Toronto, Canada",publisher="Association for Computational Linguistics",url="https://aclanthology.org/2023.acl-demo.11",doi="10.18653/v1/2023.acl-demo.11",pages="113--126",}LIDA builds on insights in automatic generation of visualization from an earlier paper -Data2Vis: Automatic Generation of Data Visualizations Using Sequence to Sequence Recurrent Neural Networks.