Chatin is a browser extension that brings the power of Large Language Models (LLMs) to MindMeister, making problem analysis easier and more intuitive. Understanding a problem isn't just about recognizing a gap; it's about exploring that gap from what you initially perceive to what you eventually aim to solve. This journey, known as 'problem scoping,' involves hypothesizing, testing, and evolving your understanding based on observations.
Traditional methods like literature reviews are thorough but time-consuming. Chatin offers a swift alternative, leveraging LLMs to provide immediate, relevant insights directly into your MindMeister maps. It's designed for those initial stages of problem-solving where quick, informed guidance can pivot your direction towards more effective solutions.
With Chatin, enhance your problem-solving process by integrating the speed of LLMs with the visual organization of MindMeister. Dive into problem analysis with an innovative tool that guides you from your first hunch to your final strategy.
Díaz, O., Garmendia, X., Contell, J. P., & Pereira, J. (2023, May). Inquiry Frameworks for Research Question Scoping in DSR: A Realization for ChatGPT. In International Conference on Design Science Research in Information Systems and Technology (pp. 299-313). Cham: Springer Nature Switzerland.
Purpose: Chatin is a browser extension designed to guide the problem analysis in MindMeister. By harnessing the power of Large Language Models (LLMs), Chatin aims to explore the landscape of your initial problem perception and offer different insights and help for understand and conceptualize your problem.
How It Works: Chatin adds a mind map template in MindMeister to start conceptualizing your problem. After defining your context, Chatin introduces the dynamic intelligence of LLMs to enrich your problem analysis by initiating a exploration of the domain through prompting. It transforms the initial, often vague, problem perceptions into clearly scoped challenges. Through an interactive process, Chatin helps you explore and refine your problem to maximize the chances of getting the a usefull solution for it! Although it lacks the veracity of literature review, it provides other benefits such as:
- Efficiency: By integrating LLM insights directly into your MindMeister workflow, Chatin significantly reduces the time needed for literature reviews and data gathering.
- Clarity: It clarifies the problem scoping process, helping you move from a broad understanding of issues to pinpointing specific actionable problems.
- Enhanced Problem Solving: With the support of LLMs, you gain access to a wide range of knowledge and perspectives, enhancing creativity and depth in problem-solving.
You can follow these steps to get Chatin up and running
Ensure your computer and you meet the minimum requirements for running Chatin.
- Google Chrome browser
- Mindmeister account. The free version allows you to create 3 maps at most.
- LLM API key. Chatin allows you to interact with GPT and Claude, therefore, in order to use them ensure you have an OpenAI or Anthropic API key
- Open your web browser and navigate to the Chatin extension page on the [Browser Extension Store/Marketplace].
- Click the "Add to Browser" or "Install" button to begin the installation process.
- Once the installation is complete, you will see a Chatin icon appear in your browser’s toolbar. This indicates that Chatin is successfully installed.
Open Chatin's options panel. You can do it by right click in Chatin's browser’s toolbar icon.
In this page, you have to complete the following information:
- Grant access to MindMeister
- Select your Large Language Model and include your API key
- Establish the number of answers you want to retrieve by each prompting
Finally, Check if the Chatin template appears in MindMeister.
You can create a Chatin mind map clicking on its template. Once you click there, an empty Chatin map is displayed:
First, you have to complete your problem space. To this aim, you have to complete the following nodes:
- Perceived Problem: Indicate which is the problem you perceived in the practice.
- Practice: Indicate which is the setting or context you want to address. An example can be “combating climate change”
- Activity: Indicate one concrete task from the practice you want to research. An example of an activity within the practice of combating climate change could be “reduce the use of cars”.
- Person [optional]: People that engage in the practice in which they perceive the problem
- Optional variables: You can add extra variables adding new nodes in the Context node.
Once the Problem Space is defined, you can start the analysis clicking on the "NEW" button in the Problem Analysis node. At the start of the analysis you will see that a new yellow/question node is created to start the analysis. Remember that you can refine or extend the problem space when you want during the problem analysis.
We distinguish two kinds of functionalities: Divergent functionalities (those that help to create new nodes) and Convergence functionalities (those that help to decide which node select).
Chatin offers different prompts to explore the problem and analyze the cause and consequences of the problem. First, Chatin starts identifying causes using the "5 why strategy" through prompting and creating question nodes that deep into the cause of the problems. Question nodes are displayed with the yellow color and they contain the prompt that is going to be send to the LLM (This prompts can be modified by the user changing the content of the node). To perform the question, the user has to click on the node and then select the 'ask question' label that is going to appear (alternatively, the question can also be done by clicking the magnifier icon). After waiting few seconds, new green nodes are generated by the LLM that answer the question.
Moreover, if you click on the node's note, a description of the answer is displayed.
Now, here starts the iterative process. For the identified problems new question nodes can be generated to deep into the problem. To this aim, when you select an interesting cause you can select it and create a new prompt to continue asking.
Then, you can repeat the above process to create more questions based on the cause until finding the problem you want to address.
During this process you have also the possibility to add your own problems or questions. To this aim, Chatin provides the "add own cause" and "add own question" options:
Remember that you can also modify the question nodes:
You can complement Chatin Mind map with implicit knowledge. It provides the opportunity to attach PDFs in the question nodes to ask question based on these papers. This helps to get better results associated with the topic.
Note that nodes created asking from a PDF have a green light color. In addition to this, if you click on the node's note it includes the excerpt that justifies the creation of the node.
During the problem analysis you can select a problem to address. A problem can be selected clicking on the black tick.
Once you click on a problem, it is coloured in red and now, instead of searching for causes you can search for consequences in order to find goodness criteria. See next figure. Now the question is differente but the mechanisms is the same as with the causes exploration.
If you ask the question, in the consequence mode the LLM will return the established number of answer + the previous causes of the addressed problem (see next figure).
Once you find an interesting consequence, you can add it as a Goodness Criteria to your problem space clicking on the black tick.
When you have the Goodness Criteria in the problem space you can explore how to measure it for future evaluations.
Chatin provides different functionalities to help you selecting a candidate problem.
Chatin provides the opportunity to aggregate different nodes when there are a lot of options to simplify the map. You can click on a question node and select the compact option. Then, Chatin will ask you to select the number of clusters you want. Finally, once the number of nodes is selected, Chatin will replace the current nodes with the number of asked nodes.
The groups are colored in dark green and collect the previous nodes. You can check which nodes are gathered in each group checking the node note.
Chatin provides you the opportunity to consult the question in Consensus in order to retrive associated literature. You can select a question node and select the Consensus option and the question will be asked in Consensus. You will be redirected to a page like this:
It is also possible to use consensus in answer nodes. In this case, the question will ask for solutions for the given problem in the literature.
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You can ask support in the following mail: xabier.garmendiad@ehu.eus
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Here there is a video demo: https://youtu.be/lGInf5cqZ7Q?si=8Ks48PHibMiFXli9
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Here you can find some map examples created with Chatin: https://docs.google.com/document/d/1liPEni_1E7nwpLqcZN4sE_DnGSYGupJ7rqfIQaxQcYw/edit?usp=sharing
- OpenAI. GPT4 and GPT3.5
- Anthropic. Claude2.0
Chatin is a browser extension and it is developed with web development technologies: JavaScript, Node and Gulp. It is developed over the last Manifest version (Manifest version 3): https://developer.chrome.com/docs/extensions/develop/migrate/what-is-mv3?hl=es-419
The web extension follows the standard web browser extension architecture with content script modules to manage the web content and background scripts to manage the local storage, mindmeister and LLM communications.
To develop from the source code, you will need to install node and gulp to build the extension. This are the required versions
- Node v12.22
- Gulp v4
Chatin makes use of differente APIs:
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MindMeister API. Chatin uses this API to manage the maps: https://developers.mindmeister.com/
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LangChain. Chatin uses different LLM's API through the LangChain JS library: https://js.langchain.com/docs/get_started/introduction
The artifact code is organized as follows:
These are the main components:
- images. This folder contains the images within the browser extension.
- pages. This folder contains the html files of the extension.
- resources. This folder contains mind map templates and the pdf.js library to process pdfs.
- scripts. This is the main component, it contains the scripts to make the extension work. Based on a web extension architecture this is the main classes:
- Content Script: content_script.js, this is the script that is executed when MindMeister is accessed
- Service worker: background.js, this script initializes all the background scripts to enable the communications
- Options file: options.js, this is the script executed when the option page is opened
- The rest of scripts are organized in the following folders.
- chatin. This folder contains the scripts that are executed in MindMeister.
- HomePageManager is executed in MindMeister home page to add the button to create a Chatin map.
- MindmapManager is executed when opening a MindMeister map and it handles Chatin's functionality. This is the most important file.
- The rest of the files in this folder are classes imported in the two above classes and define the Chatin model (e.g., node names, question templates, prompt styles...)
- llm. The main class in this folder is LLMManager, which is executed at the background to establish the communications with Langchain for asking questions to a LLM and with the local storage to manage the variables from the Options page.
- mindmeister. This folder contains the scripts to communicate with MindMeister API functionalities
- utils. This folder contains helper functions which are small pieces of reusable code designed to perform a specific task that supports larger code structures.
- llm. The main class in this folder is LLMManager, which is executed at the background to establish the communications with Langchain for asking questions to a LLM and with the local storage to manage the variables from the Options page.
To compile the project, you only need to execute the following lines, but make sure you have the node and gulp versions specified in the Development Enviroment section.
$ npm install
$ gulp default
Run $ gulp --watch and load the dist-directory into chrome.
There are two kinds of entryfiles that create bundles.
- All js-files in the root of the
./app/scriptsdirectory - All css-,scss- and less-files in the root of the
./app/stylesdirectory
$ gulp
| Option | Description |
|---|---|
--watch |
Starts a livereload server and watches all assets. To reload the extension on change include livereload.js in your bundle. |
--production |
Minifies all assets |
--verbose |
Log additional data to the console. |
--vendor |
Compile the extension for different vendors (chrome, firefox, opera, edge) Default: chrome |
--sourcemaps |
Force the creation of sourcemaps. Default: !production |
Zips your dist directory and saves it in the packages directory.
$ gulp pack --vendor=firefox
Increments version number of manifest.json and package.json,
commits the change to git and adds a git tag.
$ gulp patch // => 0.0.X
or
$ gulp feature // => 0.X.0
or
$ gulp release // => X.0.0
The build tool also defines a variable named process.env.NODE_ENV in your scripts. It will be set to development unless you use the --production option.
To contribute please contact xabier.garmendiad@ehu.eus.
This project has been developed in a research setting. The aim is to explore the benefits of AI for problem analysis so future plans seek to evaluate the artifact in real settings