Cheatsheet

Jupyter::Chatbook Cheatsheet

March 14, 2026

Quick reference for the Raku package "Jupyter::Chatbook". (raku.land, GitHub.)

0) Preliminary steps

Follow the instructions in the README of "Jupyter::Chatbook":

For installation and setup problems see the issues (both open and closed) of package's GitHub repository. (For example, this comment.)

1) New LLM persona initialization

A) Create persona with `#%chat` or `%%chat` (and immediately send first message)

#%chat assistant1, name=ChatGPT model=gpt-4.1-mini prompt="You are a concise technical assistant."
Say hi and ask what I am working on.

# Hi! What are you working on?

Remark: For all "Jupyter::Chatbook" magic specs both prefixes %% and #% can be used.

Remark: For the prompt argument the following delimiter pairs can be used: '...', "...", «...», {...}, ⎡...⎦.

B) Create persona with `#%chat <id> prompt` (create only)

#%chat assistant2 prompt, conf=ChatGPT, model=gpt-4.1-mini
You are a code reviewer focused on correctness and edge cases.

# Chat object created with ID : assistant2.

You can use prompt specs from "LLM::Prompts", for example:

#%chat yoda prompt
@Yoda

# Chat object created with ID : yoda.
 Expanded prompt:
 ⎡You are Yoda. 
 Respond to ALL inputs in the voice of Yoda from Star Wars. 
 Be sure to ALWAYS use his distinctive style and syntax. Vary sentence length.⎦

The Raku package "LLM::Prompts" (GitHub link) provides a collection of prompts and an implementation of a prompt-expansion Domain Specific Language (DSL).

2) Notebook-wide chat with an LLM persona

Continue an existing chat object

Render the answer as Markdown:

#%chat assistant1 > markdown
Give me a 5-step implementation plan for adding authentication to a FastAPI app. VERY CONCISE.

Magic cell parameter values can be assigned using the equal sign ("="):

#%chat assistant1 > markdown
Now rewrite step 2 with test-first details.

Default chat object (`NONE`)

#%chat
Does vegetarian sushi exist?

# Yes, vegetarian sushi definitely exists! It's a popular option for those who avoid fish or meat. Instead of raw fish, vegetarian sushi typically includes ingredients like:
 
 - Avocado
 - Cucumber
 - Carrots
 - Pickled radish (takuan)
 - Asparagus
 - Sweet potato
 - Mushrooms (like shiitake)
 - Tofu or tamago (Japanese omelette)
 - Seaweed salad
 
 These ingredients are rolled in sushi rice and nori seaweed, just like traditional sushi. Vegetarian sushi can be found at many sushi restaurants and sushi bars, and it's also easy to make at home.

Using the prompt-expansion DSL to modify the previous chat-cell result:

#%chat
!HaikuStyled>^

# Rice, seaweed embrace,  
 Avocado, crisp and bright,  
 Vegetarian.

3) Management of personas (`#%chat <id> meta`)

Query one persona

#%chat assistant1 meta
prompt

# "You are a concise technical assistant."

#%chat assistant1 meta
say

# Chat: assistant1
# ⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺
# Prompts: You are a concise technical assistant.
# ⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺
# role : user
# content : Say hi and ask what I am working on.
# timestamp : 2026-03-14T09:23:01.989418-04:00
# ⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺
# role : assistant
# content : Hi! What are you working on?
# timestamp : 2026-03-14T09:23:03.222902-04:00
# ⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺
# role : user
# content : Give me a 5-step implementation plan for adding authentication to a FastAPI app. VERY CONCISE.
# timestamp : 2026-03-14T09:23:03.400597-04:00
# ⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺
# role : assistant
# content : 1. Install `fastapi` and `python-jose` for JWT handling.  
# 2. Define user model and fake user database.  
# 3. Create OAuth2 password flow with `OAuth2PasswordBearer`.  
# 4. Implement token creation and verification functions.  
# 5. Protect routes using dependency injection for authentication.
# timestamp : 2026-03-14T09:23:05.106661-04:00
# ⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺
# role : user
# content : Now rewrite step 2 with test-first details.
# timestamp : 2026-03-14T09:23:05.158446-04:00
# ⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺⸺
# role : assistant
# content : 2. Write tests to verify user data retrieval and password verification; then define user model and fake user database accordingly.
# timestamp : 2026-03-14T09:23:06.901396-04:00

# Bool::True

Query all personas

#%chat all
keys

# NONE
 assistant1
 assistant2
 ce
 gc
 html
 latex
 raku
 yoda

#%chat all
gist

# {NONE => LLM::Functions::Chat(chat-id = NONE, llm-evaluator.conf.name = chatgpt, messages.elems = 4, last.message = ${:content("Rice, seaweed embrace,  \nAvocado, crisp and bright,  \nVegetarian."), :role("assistant"), :timestamp(DateTime.new(2026,3,14,9,23,10.770353078842163,:timezone(-14400)))}), assistant1 => LLM::Functions::Chat(chat-id = assistant1, llm-evaluator.conf.name = ChatGPT, messages.elems = 6, last.message = ${:content("2. Write tests to verify user data retrieval and password verification; then define user model and fake user database accordingly."), :role("assistant"), :timestamp(DateTime.new(2026,3,14,9,23,6.901396036148071,:timezone(-14400)))}), assistant2 => LLM::Functions::Chat(chat-id = assistant2, llm-evaluator.conf.name = chatgpt, messages.elems = 0), ce => LLM::Functions::Chat(chat-id = ce, llm-evaluator.conf.name = chatgpt, messages.elems = 0), gc => LLM::Functions::Chat(chat-id = gc, llm-evaluator.conf.name = chatgpt, messages.elems = 0), html => LLM::Functions::Chat(chat-id = html, llm-evaluator.conf.name = chatgpt, messages.elems = 0), latex => LLM::Functions::Chat(chat-id = latex, llm-evaluator.conf.name = chatgpt, messages.elems = 0), raku => LLM::Functions::Chat(chat-id = raku, llm-evaluator.conf.name = chatgpt, messages.elems = 0), yoda => LLM::Functions::Chat(chat-id = yoda, llm-evaluator.conf.name = chatgpt, messages.elems = 0)}

Delete one persona

#%chat assistant1 meta
delete

# Deleted: assistant1
 Gist: LLM::Functions::Chat(chat-id = assistant1, llm-evaluator.conf.name = ChatGPT, messages.elems = 6, last.message = ${:content("2. Write tests to verify user data retrieval and password verification; then define user model and fake user database accordingly."), :role("assistant"), :timestamp(DateTime.new(2026,3,14,9,23,6.901396036148071,:timezone(-14400)))})

Clear message history of one persona (keep persona)

#%chat assistant2 meta
clear

# Cleared messages of: assistant2
 Gist: LLM::Functions::Chat(chat-id = assistant2, llm-evaluator.conf.name = chatgpt, messages.elems = 0)

Delete all personas

#%chat all
drop

# Deleted 8 chat objects with names NONE assistant2 ce gc html latex raku yoda.

#%chat <id>|all meta command aliases / synonyms:

delete or drop
keys or names
clear or empty

4) Regular chat cells vs direct LLM-provider cells

Regular chat cells (`#%chat`)

Stateful across cells (conversation memory stored in chat objects).
Persona-oriented via identifier + optional prompt.
Backend chosen with conf (default: ChatGPT).

Direct provider cells (`#%openai`, `%%gemini`, `%%llama`, `%%dalle`)

Direct single-call access to provider APIs.
Useful for explicit provider/model control.
Do not use chat-object memory managed by #%chat.

Remark: For all "Jupyter::Chatbook" magic specs both prefixes %% and #% can be used.

Examples

OpenAI's (ChatGPT) models:

#%openai > markdown, model=gpt-4.1-mini
Write a regex for US ZIP+4.

Google's (Gemini) models:

#%gemini > markdown, model=gemini-2.5-flash
Explain async/await in Python using three point each with less than 10 words.

Access llamafile, locally run models:

#%llama > markdown 
Give me three Linux troubleshooting tips. VERY CONCISE.

Remark: In order to run the magic cell above you have to run a llamafile program/model on your computer. (For example, ./google_gemma-3-12b-it-Q4_K_M.llamafile.)

Access Ollama models:

#%chat ollama > markdown, conf=Ollama
Give me three Linux troubleshooting tips. VERY CONCISE.

Remark: In order to run the magic cell above you have to run an Ollama app on your computer.

Create images using DALL-E:

#%dalle, model=dall-e-3, size=landscape
A dark-mode digital painting of a lighthouse in stormy weather.

5) DALL-E interaction management

For a detailed discussion of the DALL-E interaction in Raku and magic cell parameter descriptions see "Day 21 – Using DALL-E models in Raku".

Image generation:

#%dalle, model=dall-e-3, size=landscape, style=vivid
A dark-mode digital painting of a lighthouse in stormy weather.

Here we use a DALL-E meta cell to see how many images were generated in a notebook session:

#% dalle meta
elems

# 3

Here we export the second image -- using the index 1 -- into a file named "stormy-weather-lighthouse-2.png”:

#% dalle export, index=1
stormy-weather-lighthouse-2.png

# stormy-weather-lighthouse-2.png

Here we show all generated images:

#% dalle meta
show

Here we export all images (into file names with the prefix "cheatsheet"):

#% dalle export, index=all, prefix=cheatsheet

6) LLM provider access facilitation

API keys can be passed inline (api-key) or through environment variables.

Notebook-session environment setup

%*ENV<OPENAI_API_KEY> = "YOUR_OPENAI_KEY";
%*ENV<GEMINI_API_KEY> = "YOUR_GEMINI_KEY";
%*ENV<OLLAMA_API_KEY> = "YOUR_OLLAMA_KEY";

Ollama-specific defaults:

OLLAMA_HOST (default host fallback is http://localhost:11434)
OLLAMA_MODEL (default model if model=... not given)

The magic cells take as argument base-url. This allows to use LLMs that have ChatGPT compatible APIs. The argument base_url is a synonym of host for magic cell #%ollama.

7) Notebook/chatbook session initialization with custom code + personas JSON

Initialization runs when the extension is loaded.

A) Custom Raku init code

Env var override: RAKU_CHATBOOK_INIT_FILE
If not set, first existing file is used in this order:

~/.config/raku-chatbook/init.py
~/.config/init.raku

Use this for imports/helpers you always want in chatbook sessions.

B) Pre-load personas from JSON

Env var override: RAKU_CHATBOOK_LLM_PERSONAS_CONF
If not set, first existing file is used in this order:

~/.config/raku-chatbook/llm-personas.json
~/.config/llm-personas.json

The supported JSON shape is an array of dictionaries:

[
  {
    "chat-id": "raku",
    "conf": "ChatGPT",
    "prompt": "@CodeWriterX|Raku",
    "model": "gpt-4.1-mini",
    "max_tokens": 8192,
    "temperature": 0.4
  }
]

Recognized persona spec fields include:

chat-id
prompt
conf (or configuration)
model, max-tokens, temperature, base-url
api-key
evaluator-args (object)

Verify pre-loaded personas:

#%chat all
keys

# ollama

Cheatsheet

Jupyter::Chatbook Cheatsheet

0) Preliminary steps

1) New LLM persona initialization

A) Create persona with `#%chat` or `%%chat` (and immediately send first message)

B) Create persona with `#%chat <id> prompt` (create only)

2) Notebook-wide chat with an LLM persona

Continue an existing chat object

Default chat object (`NONE`)

3) Management of personas (`#%chat <id> meta`)

Query one persona

Query all personas

Delete one persona

Clear message history of one persona (keep persona)

Delete all personas

4) Regular chat cells vs direct LLM-provider cells

Regular chat cells (`#%chat`)

Direct provider cells (`#%openai`, `%%gemini`, `%%llama`, `%%dalle`)

Examples

5) DALL-E interaction management

6) LLM provider access facilitation

Notebook-session environment setup

7) Notebook/chatbook session initialization with custom code + personas JSON

A) Custom Raku init code

B) Pre-load personas from JSON

Jupyter::Chatbook v0.3.9

Authors

License

Dependencies

Test Dependencies

Provides

Documentation

Jupyter::Chatbook Cheatsheet

0) Preliminary steps

1) New LLM persona initialization

A) Create persona with #%chat or %%chat (and immediately send first message)

B) Create persona with #%chat <id> prompt (create only)

2) Notebook-wide chat with an LLM persona

Continue an existing chat object

Default chat object (NONE)

3) Management of personas (#%chat <id> meta)

Query one persona

Query all personas

Delete one persona

Clear message history of one persona (keep persona)

Delete all personas

4) Regular chat cells vs direct LLM-provider cells

Regular chat cells (#%chat)

Direct provider cells (#%openai, %%gemini, %%llama, %%dalle)

Examples

5) DALL-E interaction management

6) LLM provider access facilitation

Notebook-session environment setup

7) Notebook/chatbook session initialization with custom code + personas JSON

A) Custom Raku init code

B) Pre-load personas from JSON

Jupyter::Chatbook v0.3.9

Authors

License

Dependencies

Test Dependencies

Provides

Documentation

A) Create persona with `#%chat` or `%%chat` (and immediately send first message)

B) Create persona with `#%chat <id> prompt` (create only)

Default chat object (`NONE`)

3) Management of personas (`#%chat <id> meta`)

Regular chat cells (`#%chat`)

Direct provider cells (`#%openai`, `%%gemini`, `%%llama`, `%%dalle`)