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https://github.com/antononcube/raku-jupyter-chatbook

Jupyter Raku Kernel that produces LLM-aware notebooks.
https://github.com/antononcube/raku-jupyter-chatbook

chatbook jupyter-notebook llm prompt-engineering raku rakulang

Last synced: 3 months ago
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Jupyter Raku Kernel that produces LLM-aware notebooks.

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README 

        
# Jupyter::Chatbook



[![MacOS](https://github.com/antononcube/Raku-Jupyter-Chatbook/actions/workflows/macos.yml/badge.svg)](https://github.com/antononcube/Raku-Jupyter-Chatbook/actions/workflows/macos.yml)

[![Linux](https://github.com/antononcube/Raku-Jupyter-Chatbook/actions/workflows/linux.yml/badge.svg)](https://github.com/antononcube/Raku-Jupyter-Chatbook/actions/workflows/linux.yml)

[![https://raku.land/zef:antononcube/Jupyter::Chatbook](https://raku.land/zef:antononcube/Jupyter::Chatbook/badges/version)](https://raku.land/zef:antononcube/Jupyter::Chatbook)



## In brief



This Raku package is a fork of Brian Duggan's 

["Jupyter::Kernel"](https://github.com/bduggan/raku-jupyter-kernel), [BDp1].



Here are the top opening statements of the README of "Jupyter::Kernel":



> "Jupyter::Kernel" is a pure Raku implementation of a Raku kernel for Jupyter clients¹.



> Jupyter notebooks provide a web-based (or console-based)

Read Eval Print Loop (REPL) for running code and serializing input and output.



It is desirable to include the interaction with Large Language Models (LLMs) into 

the "typical" REPL systems or workflows. Having LLM-aware and LLM-chat-endowed 

notebooks -- **chatbooks** -- can really speed up the:

- Writing and preparation of documents on variety of subjects

- Derivation of useful programming code

- Adoption of programming languages by newcomers



This repository is mostly for experimental work, but it aims to be *always* very

useful for interacting with LLMs via Raku.



**Remark:** The reason to have a separate package -- a fork of

["Jupyter::Chatbook"](https://github.com/bduggan/raku-jupyter-kernel) --

is because:

- I plan to introduce 4-6 new package dependencies

- I expect to do a fair amount of UX experimental implementations and refactoring



-------



## Installation and setup



From ["Zef ecosystem"](https://raku.land):



```

zef install Jupyter::Chatbook

```



From GitHub:



```

zef install https://github.com/antononcube/Raku-Jupyter-Chatbook.git

```



### macOS specific



If using macOS and [ZMQ](https://zeromq.org) is installed with [homebrew](https://formulae.brew.sh/formula/zeromq), 

then it might be necessary to copy the ZMQ library "libzmq.5.dylib" into a directory where `zef` can find it.



For example, see the GitHub Actions workflow file ["macos.yml"](https://github.com/antononcube/Raku-Jupyter-Chatbook/blob/master/.github/workflows/macos.yml). 



-------



## Jupyter kernel configuration



**Remark:** The instructions in this section follow the instructions in

["Jupyter::Chatbook"](https://github.com/bduggan/raku-jupyter-kernel).

The "main" change is using `jupyter-chatbook.raku` instead of `jupyter-kernel.raku`. 



### Server Configuration



To generate a configuration directory, and to install a kernel

config file and icons into the default location:



```

jupyter-chatbook.raku --generate-config

```



* Use `--location=XXX` to specify another location.

* Use `--force` to override an existing configuration.



### Logging



By default a log file `jupyter.log` will be written in the

current directory.  An option `--logfile=XXX` argument can be

added to the argv argument of the server configuration file

(located at `$(jupyter --data)/kernels/raku/kernel.json`)

to change this.



### Client configuration



The jupyter documentation describes the client configuration.

To start, you can generate files for the notebook or

console clients like this:



```

jupyter notebook --generate-config

jupyter console --generate-config

```



Some suggested configuration changes for the console client:



* set `kernel_is_complete_timeout` to a high number.  Otherwise,

  if the kernel takes more than 1 second to respond, then from

  then on, the console client uses internal (non-Raku) heuristics

  to guess when a block of code is complete.



* set `highlighting_style` to `vim`.  This avoids having dark blue

  on a black background in the console client.



### Running



Start the web UI with:



```

jupyter-notebook

Then select New -> Raku.

```



You can also use it in the console like this:



```

jupyter-console --kernel=raku

```



Or make a handy shell alias:



```

alias iraku='jupyter-console --kernel=raku'

```



### macOS specific



Consider copying the 

[RakuChatbook kernel specifications](./resources) 

in the directory "~/Library/Jupyter/kernels/raku". That way IDEs like 

[Visual Studio Code](https://code.visualstudio.com)

would find the "RakuChatbook" kernel "quicker" or "more directly." 



------



## LLM and DeepL API keys



The default API keys for the chat cells, LLM functions, chat objects, and DeepL cells are taken from 

the Operating System (OS) environmental variables 

`OPENAI_API_KEY`, `PALM_API_KEY`, `GEMINI_API_KEY`, `MISTRAL_API_KEY`, `DEEPL_AUTH_KEY`. 



The api keys can also be specified using LLM evaluator and configuration options and objects; 

see [AA3, AAp2, AAv4].



**Remark:** `PALM_API_KEY` works for both PaLM and Gemini.



-------



## Using LLMs in chatbooks



There are four ways to use LLMs in a chatbook:



1. [LLM functions](https://github.com/antononcube/Raku-Jupyter-Chatbook/blob/master/eg/Chatbook-LLM-functions-and-chat-objects.ipynb), [AA3, AAp2]

2. [LLM chat objects](https://github.com/antononcube/Raku-Jupyter-Chatbook/blob/master/eg/Chatbook-LLM-functions-and-chat-objects.ipynb), [AA4, AAp2]

3. [Code cells with magics](https://github.com/antononcube/Raku-Jupyter-Chatbook/blob/master/eg/Chatbook-LLM-cells.ipynb)

   accessing LLMs, like, OpenAI's, [AAp3], or PaLM's, [AAp4]

4. [Notebook-wide chats](https://github.com/antononcube/Raku-Jupyter-Chatbook/blob/master/eg/Chatbook-LLM-chats.ipynb) 

   that are distributed over multiple code cells with chat-magic specs



The sections below briefly describe each of these ways and have links to notebooks with

more detailed examples.



-------



## LLM functions and chat objects



LLM functions as described in [AA3] are best utilized via a certain REPL tool or environment.

Notebooks are the perfect media for LLM functions workflows. 

Here is an example of a code cell that defines an LLM function:



```perl6

use LLM::Functions;



my &fcp = llm-function({"What is the population of the country $_ ?"});

```



Here is another cell that can be evaluated multiple times using different country names:



```perl6

.map({ &fcp($_) })

```



For more examples of LLM functions and LLM chat objects see the notebook 

["Chatbook-LLM-functions-and-chat-objects.ipynb"](https://github.com/antononcube/Raku-Jupyter-Chatbook/blob/master/eg/Chatbook-LLM-functions-and-chat-objects.ipynb).



**Remark:** 

Chatbooks load in their initialization phase the packages

["LLM::Functions"](https://github.com/antononcube/Raku-LLM-Functions), [AAp2], and

["LLM::Prompts"](https://github.com/antononcube/Raku-LLM-Prompts), [AAp10].

"LLM::Prompts" provides a prompt expansion DSL that allows specifications like:



```

#% chat

@Yoda How many students did you train? #Translated|German

```



See the movie ["Jupyter Chatbook multi cell LLM chats teaser (Raku)"](https://www.youtube.com/watch?v=wNpIGUAwZB8), [AAv5].



**Remark:**

Also, in the initialization phase are loaded the packages

["Clipboard"](https://github.com/antononcube/Raku-Clipboard), [AAp5],

["Data::Translators"](https://github.com/antononcube/Raku-Data-Translators), [AAp6],

["Data::TypeSystem"](https://github.com/antononcube/Raku-Data-Translators), [AAp7],

["Text::Plot"](https://github.com/antononcube/Raku-Text-Plot), [AAp8],

and

["Text::SubParsers"](https://github.com/antononcube/Raku-Text-SubParsers), [AAp9],

that can be used to post-process LLM outputs.



-------



## LLM cells



The LLMs of OpenAI (ChatGPT, DALL-E) and Google (PaLM, Gemini) can be interacted with using "dedicated" notebook cells.



Here is an example of a code cell with PaLM magic spec:



```

%% gemini, max-tokens=600

Generate a horror story about a little girl lost in the forest and getting possessed.

```



For more examples see the notebook 

["Chatbook-LLM-cells.ipynb"](https://github.com/antononcube/Raku-Jupyter-Chatbook/blob/master/eg/Chatbook-LLM-cells.ipynb).



------



## Notebook-wide chats



Chatbooks have the ability to maintain LLM conversations over multiple notebook cells.

A chatbook can have more than one LLM conversations.

"Under the hood" each chatbook maintains a database of chat objects.

Chat cells are used to give messages to those chat objects.



For example, here is a chat cell with which a new 

["Email writer"](https://developers.generativeai.google/prompts/email-writer) 

chat object is made, and that new chat object has the identifier "em12":  



```

%% chat-em12, prompt = «Given a topic, write emails in a concise, professional manner»

Write a vacation email.

```



Here is a chat cell in which another message is given to the chat object with identifier "em12":



```

%% chat-em12

Rewrite with manager's name being Jane Doe, and start- and end dates being 8/20 and 9/5.

```



In this chat cell a new chat object is created:



```

%% chat snowman, prompt = ⎡Pretend you are a friendly snowman. Stay in character for every response you give me. Keep your responses short.⎦

Hi!

```



And here is a chat cell that sends another message to the "snowman" chat object:



```

%% chat snowman

Who build you? Where?

```



**Remark:** Specifying a chat object identifier is not required. I.e. only the magic spec `%% chat` can be used.

The "default" chat object ID identifier is "NONE".



**Remark:** The magic keyword "chat" can be separated from the identifier of the chat object with

the symbols "-", "_", ":", or with any number of (horizontal) white spaces.



For more examples see the notebook 

["Chatbook-LLM-chats.ipynb"](https://github.com/antononcube/Raku-Jupyter-Chatbook/blob/master/eg/Chatbook-LLM-chats.ipynb).

For a quick demo see the movie

["Jupyter Chatbook multi cell LLM chats teaser (Raku)"](https://www.youtube.com/watch?v=wNpIGUAwZB8), [AAv5].



Here is a flowchart that summarizes the way chatbooks create and utilize LLM chat objects:



```mermaid

flowchart LR

    OpenAI{{OpenAI}}

    PaLM{{PaLM}}

    LLMFunc[[LLM::Functions]]

    LLMProm[[LLM::Prompts]]

    CODB[(Chat objects)]

    PDB[(Prompts)]

    CCell[/Chat cell/]

    CRCell[/Chat result cell/]

    CIDQ{Chat ID
specified?}

    CIDEQ{Chat ID
exists in DB?}

    RECO[Retrieve existing
chat object]

    COEval[Message
evaluation]

    PromParse[Prompt
DSL spec parsing]

    KPFQ{Known
prompts
found?}

    PromExp[Prompt
expansion]

    CNCO[Create new
chat object]

    CIDNone["Assume chat ID
is 'NONE'"] 

    subgraph Chatbook frontend    

        CCell

        CRCell

    end

    subgraph Chatbook backend

        CIDQ

        CIDEQ

        CIDNone

        RECO

        CNCO

        CODB

    end

    subgraph Prompt processing

        PDB

        LLMProm

        PromParse

        KPFQ

        PromExp 

    end

    subgraph LLM interaction

      COEval

      LLMFunc

      PaLM

      OpenAI

    end

    CCell --> CIDQ

    CIDQ --> |yes| CIDEQ

    CIDEQ --> |yes| RECO

    RECO --> PromParse

    COEval --> CRCell

    CIDEQ -.- CODB

    CIDEQ --> |no| CNCO

    LLMFunc -.- CNCO -.- CODB

    CNCO --> PromParse --> KPFQ

    KPFQ --> |yes| PromExp

    KPFQ --> |no| COEval

    PromParse -.- LLMProm 

    PromExp -.- LLMProm

    PromExp --> COEval 

    LLMProm -.- PDB

    CIDQ --> |no| CIDNone

    CIDNone --> CIDEQ

    COEval -.- LLMFunc

    LLMFunc <-.-> OpenAI

    LLMFunc <-.-> PaLM

```



------



## Chat meta cells



Each chatbook session has a Hash of chat objects.

Chatbooks can have chat meta cells that allow the access of the chat object "database" as whole, 

or its individual objects.  



Here is an example of a chat meta cell (that applies the method `say` to the chat object with ID "snowman"):



```

%% chat snowman meta

say

```



Here is an example of chat meta cell that creates a new chat chat object with the LLM prompt

specified in the cell

(["Guess the word"](https://developers.generativeai.google/prompts/guess-the-word)):



```

%% chat-WordGuesser prompt

We're playing a game. I'm thinking of a word, and I need to get you to guess that word. 

But I can't say the word itself. 

I'll give you clues, and you'll respond with a guess. 

Your guess should be a single word only.

```



Here is another chat object creation cell using a prompt from the package

["LLM::Prompts"](https://raku.land/zef:antononcube/LLM::Prompts), [AAp4]:



```

%% chat yoda1 prompt

@Yoda

```



Here is a table with examples of magic specs for chat meta cells and their interpretation:



| cell magic line  | cell content                         | interpretation                                                  |

|:-----------------|:-------------------------------------|:----------------------------------------------------------------|

| chat-ew12 meta   | say                                  | Give the "print out" of the chat object with ID "ew12"          |   

| chat-ew12 meta   | messages                             | Give the messages of the chat object with ID "ew12"             |   

| chat sn22 prompt | You pretend to be a melting snowman. | Create a chat object with ID "sn22" with the prompt in the cell |   

| chat meta all    | keys                                 | Show the keys of the session chat objects DB                    |   

| chat all         | keys                                 | *«same as above»*                                               |   



Here is a flowchart that summarizes the chat meta cell processing:



```mermaid

flowchart LR

    LLMFunc[[LLM::Functions]]

    CODB[(Chat objects)]

    CCell[/Chat meta cell/]

    CRCell[/Chat meta cell result/]

    CIDQ{Chat ID
specified?}

    KCOMQ{Known
chat object
method?}

    AKWQ{Keyword 'all'
specified?} 

    KCODBMQ{Known
chat objects
DB method?}

    CIDEQ{Chat ID
exists in DB?}

    RECO[Retrieve existing
chat object]

    COEval[Chat object
method
invocation]

    CODBEval[Chat objects DB
method
invocation]

    CNCO[Create new
chat object]

    CIDNone["Assume chat ID
is 'NONE'"] 

    NoCOM[/Cannot find
chat object
message/]

    CntCmd[/Cannot interpret
command
message/]

    subgraph Chatbook

        CCell

        NoCOM

        CntCmd

        CRCell

    end

    CCell --> CIDQ

    CIDQ --> |yes| CIDEQ  

    CIDEQ --> |yes| RECO

    RECO --> KCOMQ

    KCOMQ --> |yes| COEval --> CRCell

    KCOMQ --> |no| CntCmd

    CIDEQ -.- CODB

    CIDEQ --> |no| NoCOM

    LLMFunc -.- CNCO -.- CODB

    CNCO --> COEval

    CIDQ --> |no| AKWQ

    AKWQ --> |yes| KCODBMQ

    KCODBMQ --> |yes| CODBEval

    KCODBMQ --> |no| CntCmd

    CODBEval -.- CODB

    CODBEval --> CRCell

    AKWQ --> |no| CIDNone

    CIDNone --> CIDEQ

    COEval -.- LLMFunc

```



-------



## [DeepL](https://www.deepl.com) cells



Chatbooks can have [DeepL](https://www.deepl.com) cells (that use the package 

["Lingua::Translation::DeepL"](https://raku.land/zef:antononcube/Lingua::Translation::DeepL),

[AAp15].)

For example:



```

#% deepl, to-lang=German, formality=less, format=text

I told you to get the frames from the other warehouse!

```



```

Ich habe dir gesagt, du sollst die Rahmen aus dem anderen Lager holen!

```



-------



## [Mermaid-JS](https://mermaid.js.org) cells



Chatbooks can have [Mermaid-JS](https://mermaid.js.org) cells,

(that use the package ["WWW::MermaidInk"](https://raku.land/zef:antononcube/WWW::MermaidInk), [AAp11].)

For example:



```

#% mermaid, format=svg, background=SlateGray

mindmap

**Chatbook**

    **Direct LLM access**

        OpenAI

            ChatGPT

            DALL-E

        Google

            PaLM

            Gemini

        MistralAI

        LLaMA

    **Notebook wide chats**

        Chat objects

           Named

           Anonymous

        Chat meta cells              

        Prompt DSL expansion 

    **DeepL**

    **MermaidJS**

        SVG

        PNG

    **Pre-loaded packages**

        LLM::Functions

        LLM::Prompts

        Text::SubParsers

        Data::Translators

        Data::TypeSystem

        Clipboard :ALL

        Text::Plot

        Image::Markup::Utilities

        WWW::LLaMA

        WWW::MermaidInk

        WWW::OpenAI

        WWW::PaLM

        WWW::Gemini

        Lingua::Translation::DeepL

```



------



## TODO



1. [ ] TODO Features

   1. [X] DONE Chat-meta cells (simple)

      - [X] DONE meta  

      - [X] DONE all  

      - [X] DONE prompt

   2. [X] DONE Gemini cells

   3. [X] DONE DeepL cells

   4. [ ] TODO Wolfram|Alpha cells

      - Handling cell type: result, simple, or query

   5. [ ] TODO Chat-meta cells (via LLM)

   6. [ ] TODO DSL ["ProdGDT"](https://github.com/antononcube/Raku-WWW-ProdGDT) cells

   7. [X] DONE Using pre-prepared prompts

      - This requires implementing ["LLM::Prompts"](https://github.com/antononcube/Raku-LLM-Prompts).

        - And populating it with a good number of prompts.

   8. [ ] TODO Parse Python style magics

      - See ["JupyterChatbook"](https://github.com/antononcube/Python-JupyterChatbook)

      - See ["Getopt::Long::Grammar"](https://github.com/antononcube/Raku-Getopt-Long-Grammar)

2. [ ] TODO Unit tests

   1. [X] DONE PaLM cells

   2. [X] DONE OpenAI cells

   3. [X] DONE MermaidInk cells

   4. [ ] TODO DALL-E cells

   5. [X] DONE Chat meta cells

3. [ ] TODO Documentation

   - [X] DONE LLM functions and chat objects in chatbooks

   - [X] DONE LLM cells in chatbooks

   - [X] DONE Notebook-wide chats and chat meta cells 

   - [ ] TODO All parameters of OpenAI API in Raku

   - [ ] TODO All parameters of PaLM API in Raku

   - [ ] TODO More details on prompts

   - [ ] TODO Introductory video(s)



------



## References



### Articles



[AA1] Anton Antonov,

["Literate programming via CLI"](https://rakuforprediction.wordpress.com/2023/03/06/literate-programming-via-cli/),

(2023),

[RakuForPrediction at WordPress](https://rakuforprediction.wordpress.com).



[AA2] Anton Antonov,

["Generating documents via templates and LLMs"](https://rakuforprediction.wordpress.com/2023/07/11/generating-documents-via-templates-and-llms/),

(2023),

[RakuForPrediction at WordPress](https://rakuforprediction.wordpress.com).



[AA3] Anton Antonov,

["Workflows with LLM functions"](https://rakuforprediction.wordpress.com/2023/08/01/workflows-with-llm-functions/),

(2023),

[RakuForPrediction at WordPress](https://rakuforprediction.wordpress.com).



[AA4] Anton Antonov,

["Number guessing games: PaLM vs ChatGPT"](https://rakuforprediction.wordpress.com/2023/08/06/number-guessing-games-palm-vs-chatgpt/),

(2023),

[RakuForPrediction at WordPress](https://rakuforprediction.wordpress.com).



[SW1] Stephen Wolfram,

["Introducing Chat Notebooks: Integrating LLMs into the Notebook Paradigm"](https://writings.stephenwolfram.com/2023/06/introducing-chat-notebooks-integrating-llms-into-the-notebook-paradigm/),

(2023),

[writings.stephenwolfram.com](https://writings.stephenwolfram.com).



### Packages



[AAp1] Anton Antonov,

[Jupyter::Chatbook Raku package](https://github.com/antononcube/Raku-Jupyter-Chatbook),

(2023),

[GitHub/antononcube](https://github.com/antononcube).



[AAp2] Anton Antonov,

[LLM::Functions Raku package](https://github.com/antononcube/Raku-LLM-Functions),

(2023),

[GitHub/antononcube](https://github.com/antononcube).



[AAp3] Anton Antonov,

[WWW::OpenAI Raku package](https://github.com/antononcube/Raku-WWW-OpenAI),

(2023),

[GitHub/antononcube](https://github.com/antononcube).



[AAp4] Anton Antonov,

[WWW::PaLM Raku package](https://github.com/antononcube/Raku-WWW-PaLM),

(2023),

[GitHub/antononcube](https://github.com/antononcube).



[AAp5] Anton Antonov,

[Clipboard Raku package](https://github.com/antononcube/Raku-Clipboard),

(2023),

[GitHub/antononcube](https://github.com/antononcube).



[AAp6] Anton Antonov,

[Data::Translators Raku package](https://github.com/antononcube/Raku-Data-Translators),

(2023),

[GitHub/antononcube](https://github.com/antononcube).



[AAp7] Anton Antonov,

[Data::TypeSystem Raku package](https://github.com/antononcube/Raku-Data-TypeSystem),

(2023),

[GitHub/antononcube](https://github.com/antononcube).



[AAp8] Anton Antonov,

[Text::Plot Raku package](https://github.com/antononcube/Raku-Text-Plot),

(2022),

[GitHub/antononcube](https://github.com/antononcube).



[AAp9] Anton Antonov,

[Text::SubParsers Raku package](https://github.com/antononcube/Raku-Text-SubParsers),

(2023),

[GitHub/antononcube](https://github.com/antononcube).



[AAp10] Anton Antonov,

[LLM::Prompts Raku package](https://github.com/antononcube/Raku-LLM-Prompts),

(2023),

[GitHub/antononcube](https://github.com/antononcube).



[AAp11] Anton Antonov,

[WWW::MermaidInk Raku package](https://github.com/antononcube/Raku-WWW-MermaidInk),

(2023),

[GitHub/antononcube](https://github.com/antononcube).



[AAp12] Anton Antonov,

[WWW::MistralAI Raku package](https://github.com/antononcube/Raku-WWW-MistralAI),

(2023),

[GitHub/antononcube](https://github.com/antononcube).



[AAp13] Anton Antonov,

[WWW::LLaMA Raku package](https://github.com/antononcube/Raku-WWW-LLaMA),

(2024),

[GitHub/antononcube](https://github.com/antononcube).



[AAp14] Anton Antonov,

[WWW::Gemini Raku package](https://github.com/antononcube/Raku-WWW-Gemini),

(2024),

[GitHub/antononcube](https://github.com/antononcube).



[AAp15] Anton Antonov,

[Lingua::Translation::DeepL Raku package](https://github.com/antononcube/Raku-Lingua-Translation-DeepL),

(2022),

[GitHub/antononcube](https://github.com/antononcube).



[BDp1] Brian Duggan,

[Jupyter:Kernel Raku package](https://github.com/bduggan/raku-jupyter-kernel),

(2017-2023),

[GitHub/bduggan](https://github.com/bduggan).



### Videos



[AAv1] Anton Antonov,

["Raku Literate Programming via command line pipelines"](https://www.youtube.com/watch?v=2UjAdQaKof8),

(2023),

[YouTube/@AAA4Prediction](https://www.youtube.com/@AAA4prediction).



[AAv2] Anton Antonov,

["Racoons playing with pearls and onions"](https://www.youtube.com/watch?v=zlkoNZK8MpU)

(2023),

[YouTube/@AAA4Prediction](https://www.youtube.com/@AAA4prediction).



[AAv3] Anton Antonov,

["Streamlining ChatGPT code generation and narration workflows (Raku)"](https://www.youtube.com/watch?v=mI-oWLz5dYY)

(2023),

[YouTube/@AAA4Prediction](https://www.youtube.com/@AAA4prediction).



[AAv4] Anton Antonov,

["Jupyter Chatbook LLM cells demo (Raku)"](https://www.youtube.com/watch?v=cICgnzYmQZg)

(2023),

[YouTube/@AAA4Prediction](https://www.youtube.com/@AAA4prediction).



[AAv5] Anton Antonov,

["Jupyter Chatbook multi cell LLM chats teaser (Raku)"](https://www.youtube.com/watch?v=wNpIGUAwZB8)

(2023),

[YouTube/@AAA4Prediction](https://www.youtube.com/@AAA4prediction).



------



## *Footnotes*



¹ Jupyter clients are user interfaces to interact with an interpreter kernel like "Jupyter::Chatbook".

Jupyter [Lab | Notebook | Console | QtConsole ] are the jupyter maintained clients.

More info in the [jupyter documentations site](https://jupyter.org/documentation).