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Your Personal Assistant in Computational BioImaging.

Project description

🦒 BioImage.IO Chatbot 🤖

📣New Preprint: arXiv 10.5281/zenodo.10032228

👇 Want to Try the Chatbot? Visit here!

Your Personal Assistant in Computational Bioimaging

Welcome to the BioImage.IO Chatbot user guide. This guide will help you get the most out of the chatbot, providing detailed information on how to interact with it and retrieve valuable insights related to computational bioimaging.


The BioImage.IO Chatbot is a versatile conversational agent designed to assist users in accessing information related to computational bioimaging. It leverages the power of Large Language Models (LLMs) and integrates user-specific data to provide contextually accurate and personalized responses. Whether you're a researcher, developer, or scientist, the chatbot is here to make your bioimaging journey smoother and more informative.

screenshot for the chatbot

The following diagram shows how the chatbot works:

Chatbot Features

The BioImage.IO Chatbot is equipped with an array of capabilities designed to enhance the bioimaging experience:

  • Contextual and Personalized Response: Interprets the context of inquiries to deliver relevant and accurate responses. Adapts interactions based on user-specific background information to provide customized advice.

  • Comprehensive Data Source Integration: Accesses a broad range of databases and documentation for bioimaging, including,, deepImageJ, ImJoy, and Details on the supported sources are maintained in the knowledge-base-manifest.yaml file.

  • Advanced Query Capabilities: Generates and executes Python scripts for detailed queries within structured databases such as CSV, JSON files, or SQL databases, facilitating complex data retrievals.

  • AI-Powered Analysis and Code Interpretation: Directly runs complex image analysis tasks using advanced AI models like Cellpose, via an embedded code interpreter.

  • Performance Enhancements with ReAct and RAG: Utilizes a Retrieval Augmented Generation system with a ReAct loop for dynamic, iterative reasoning and tool engagement, improving response quality.

  • Extension Mechanism for Developers: Allows for the development of custom extensions using ImJoy plugins or hypha services within Jupyter notebooks, enhancing flexibility and integration possibilities.

  • Vision Inspection and Hardware Control: Features a Vision Inspector extension powered by GPT-4 for visual feedback on image content and analysis outcomes, and demonstrates potential for controlling microscopy hardware in smart microscopy setups.

  • Interactive User Interface and Documentation: Offers a user-friendly interface with comprehensive support documents, ensuring easy access to its features and maximizing user engagement.

Using the Chatbot

We are providing a public chatbot service for you to try out. You can access the chatbot here.

Please note that the chatbot is still in beta and is being actively developed, we will log the message you input into the chatbot for further investigation of issues and support our development. See the Disclaimer for BioImage.IO Chatbot. If you want to to remove your chat logs, please contact us via this form.

Here you can find usage guide and more examples: Usage guide and example screenshots.

If you encounter any issues, please report them via Github.

Asking Questions

To ask the chatbot a question, type your query and send it. The chatbot will analyze your question and provide a relevant response. You can ask questions related to bioimaging, software tools, models, and more.

Personalized Responses

The chatbot uses your user profile information, such as your name, occupation, and background, to personalize its responses. This ensures that the information you receive is tailored to your specific needs.

Setup the Chatbot locally

If you want to run the chatbot server locally, you need to have an OpenAI API key. You can get one by signing up at OpenAI. Once you have your API key, you can install the chatbot package via pip and set the environment variables:

pip install bioimageio-chatbot
export OPENAI_API_KEY=sk-xxxxxxxx # Required
export BIOIMAGEIO_KNOWLEDGE_BASE_PATH=/path/to/bioimageio-knowledge-base  # Optional, default to ./bioimageio-knowledge-base 
export BIOIMAGEIO_CHAT_LOGS_PATH=./chat-logs # Optional, default to ./chat-logs

The chatbot server backend has been tested on Ubuntu and MacOS, it should work on Windows as well.

Command-line Interface

BioImage.IO Chatbot comes with a command-line interface to facilitate server management, connection to external servers, and knowledge base creation.

You can access the command-line interface by running python -m bioimageio_chatbot or the bioimageio-chatbot command.

Below are the available commands and options:

Initialize Knowledge Base

To initialize the knowledge base, use the init command:

python -m bioimageio_chatbot init

This will load the knowledge base from the location specified by the BIOIMAGEIO_KNOWLEDGE_BASE_PATH environment variable, or use the default path ./bioimageio-knowledge-base. If the knowledge base is not found, it will be downloaded from the predefined URL (by default, it uses It can be overridden with BIOIMAGEIO_KNOWLEDGE_BASE_URL).

NOTE: It may take some time to download the knowledge base depending on your internet connection. Example:

export BIOIMAGEIO_KNOWLEDGE_BASE_PATH="./my_knowledge_base"
python -m bioimageio_chatbot init

After running the init command, it will list the databases loaded into the knowledge base.

Start Server

To start your own server entirely, use the start-server command:

python -m bioimageio_chatbot start-server [--host HOST] [--port PORT] [--public-base-url PUBLIC_BASE_URL]


  • --host: The host address to run the server on (default:
  • --port: The port number to run the server on (default: 9000)
  • --public-base-url: The public base URL of the server (default:
  • --login-required: Whether to require users to log in before accessing the chatbot (default to not require login)


export OPENAI_API_KEY=sk-xxxxxxxx
export BIOIMAGEIO_KNOWLEDGE_BASE_PATH=./bioimageio-knowledge-base
export BIOIMAGEIO_CHAT_LOGS_PATH=./chat-logs
python -m bioimageio_chatbot start-server --host= --port=9000

This will create a local server, and the BioImage.IO Chatbot is available at:

Open the link in a browser, and you will see the chat interface.

Please note that the chatbot server may not be accessible to users outside your local network.

A user guide and technical overview can be found here.

To be able to share your chatbot service over the internet (especially for users outside your local network), you will need to expose your server publicly. Please, see Connect to Server

Connect to Server

To help you share your chatbot with users external to your local network, you can use our public BioEngine server as a proxy.

To connect to an external BioEngine server, use the connect-server command:

python -m bioimageio_chatbot connect-server [--server-url SERVER_URL]


  • --server-url: The URL of the external BioEngine server to connect to (default:
  • --login-required: Whether to require users to log in before accessing the chatbot (default to not require login)


export OPENAI_API_KEY=sk-xxxxxxxx
export BIOIMAGEIO_KNOWLEDGE_BASE_PATH=./bioimageio-knowledge-base
export BIOIMAGEIO_CHAT_LOGS_PATH=./chat-logs
python -m bioimageio_chatbot connect-server --server-url=

First, you will be asked to log in with a hypha account. Either your GitHub or Google account can be reused. Then, the following message containing a link to the chatbot will be displayed: 'The BioImage.IO Chatbot is available at:'

Leave your chatbot running to enable users inside or outside your network to access it from this URL.

User Management

If you set --login-required when running start-server or connect-server, users will be required to log in before accessing the chatbot. The chatbot will then collect the user's GitHub or Google account information and store it its logs for future analysis.

You can also provide an optional environment variable BIOIMAGEIO_AUTHORIZED_USERS_PATH for the chatbot to load a list of authorized users. The file should be a JSON file containing a list of GitHub or Google account names. For example:

    "users": [
        {"email": ""}

Create Knowledge Base

To create a new knowledge base, use the create-knowledge-base command:

python -m bioimageio_chatbot create-knowledge-base [--output-dir OUTPUT_DIR]


  • --output-dir: The directory where the knowledge base will be created (default: ./bioimageio-knowledge-base)


export OPENAI_API_KEY=sk-xxxxxxxx
export BIOIMAGEIO_KNOWLEDGE_BASE_PATH=./bioimageio-knowledge-base
python -m bioimageio_chatbot create-knowledge-base --output-dir=./bioimageio-knowledge-base

Running the BioImage.IO Chatbot in a Docker Container

Step 1: Build the Docker Image

To run the BioImage.IO Chatbot using a Docker container, follow these steps. First, build the Docker image by running the following command in your terminal:

docker build -t bioimageio-chatbot:latest .

If you prefer to use a pre-built Docker image from Docker Hub, you can pull the image using the following command:

docker pull alalulu/bioimageio-chatbot:latest

Step 2: Start the Chatbot Server

After building the Docker image, you can start the chatbot server with the following command:

docker run -e OPENAI_API_KEY=sk-xxxxxxxxxxxxx -e BIOIMAGEIO_KNOWLEDGE_BASE_PATH=/knowledge-base -p 3000:9000 -v /path/to/local/knowledge-base:/knowledge-base bioimageio-chatbot:latest python -m bioimageio_chatbot start-server --host= --port=9000 --public-base-url=http://localhost:3000

Replace the placeholders in the command with the following values:

  • sk-xxxxxxxxxxxxx: Your OpenAI API key.
  • /path/to/local/knowledge-base: The local path to your knowledge base folder.

Optionally, for improved reproducibility, you can change latest to a version tag such as v0.1.18.

Step 3: Access the Chatbot

The BioImage.IO Chatbot is now running in the Docker container. You can access it locally in your web browser by visiting:

Make sure to replace 3000 with the host port you specified in the docker run command.

Enjoy using the BioImage.IO Chatbot!

Technical Overview

Please read the technical overview for more details about the chatbot's design and implementation.

Develop Chatbot Extensions

The BioImage.IO Chatbot is designed to be extensible, allowing developers to create custom extensions to add new functionalities to the chatbot. You can create extensions to integrate new tools, databases, and services into the chatbot, making it more powerful and versatile. See the development guide for more details.

Join Us as a Community Partner

The BioImage.IO Chatbot is a community-driven project. We welcome contributions from the community to help improve the chatbot's knowledge base and make it more informative and useful to the community.

For more information, please visit the contribution guidelines.

If you are a tool developer or a database maintainer related to bioimaging, you can join us as a community partner. Please get in touch with us via Github issues.

Contact Us

If you have any questions, need assistance, or want to contribute to the chatbot's knowledge base, please do not hesitate to contact us via Github issues. Our team is here to help you get started and make valuable contributions.

Thanks for your support and helping make the BioImage.IO Chatbot more informative and useful to the community.


For detailed description of our work, please read our preprint: arXiv 10.5281/zenodo.10032227

To reproduce the use cases described in Figure 2 in the manuscript, please refer to the reproducing example usage scenarios.

Cite Us

If you use the BioImage.IO Chatbot in your research, please cite us:

Lei, W., Fuster-Barceló, C., Muñoz-Barrutia, A., & Ouyang, W. (2023). 🦒BioImage.IO Chatbot: A Personalized Assistant for BioImage Analysis Augmented by Community Knowledge Base (0.1.0). Zenodo.


We thank AI4Life consortium for its crucial support in the development of the BioImage.IO Chatbot.


AI4Life has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101057970. Views and opinions expressed are, however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

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