JupyterChatbook 0.0.10

Custom Jupyter magics for interacting with LLMs.

JupyterChatbook

Python package of a Jupyter extension that facilitates the interaction with Large Language Models (LLMs).

Remark: The chatbook LLM cells use the packages "openai", [OAIp2], and "google-generativeai", [GAIp1].

Remark: The API keys for the LLM cells are taken from the Operating System (OS) environmental variables OPENAI_API_KEY and PALM_API_KEY.

Remark: The results of the LLM cells are automatically copied to the clipboard using the package "pyperclip", [ASp1].

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Installation

Install from GitHub

pip install -e git+https://github.com/antononcube/Python-JupyterChatbook.git#egg=Python-JupyterChatbook

From PyPi

pip install JupyterChatbook

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Demonstration notebooks (chatbooks)

Notebook	Description
Chatbooks-cells-demo.ipynb	How to do multi-cell (notebook-wide) chats?
Chatbook-LLM-cells.ipynb	How to "directly message" LLMs services?
DALL-E-cells-demo.ipynb	How to generate images with DALL-E?
Echoed-chats.ipynb	How to see the LLM interaction execution steps?

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

%%chat --chat_id 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 --chat_id 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 -i 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 -i 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".

For more examples see the notebook "Chatbook-cells-demo.ipynb".

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

flowchart LR
    OpenAI{{OpenAI}}
    PaLM{{PaLM}}
    LLMFunc[[LLMFunctions]]
    LLMProm[[LLMPrompts]]
    CODB[(Chat objects)]
    PDB[(Prompts)]
    CCell[/Chat cell/]
    CRCell[/Chat result cell/]
    CIDQ{Chat ID<br/>specified?}
    CIDEQ{Chat ID<br/>exists in DB?}
    RECO[Retrieve existing<br/>chat object]
    COEval[Message<br/>evaluation]
    PromParse[Prompt<br/>DSL spec parsing]
    KPFQ{Known<br/>prompts<br/>found?}
    PromExp[Prompt<br/>expansion]
    CNCO[Create new<br/>chat object]
    CIDNone["Assume chat ID<br/>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

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Chat meta cells

TBD...

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DALL-E access

See the notebook "DALL-E-cells-demo.ipynb"

Here is a screenshot:

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Implementation details

The design of this package -- and corresponding envisioned workflows with it -- follow those of the Raku package "Jupyter::Chatbook", [AAp3].

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TODO

• TODO Implementation
  • DONE PalM chat cell
  • TODO Using "pyperclip"
    • DONE Basic
      • %%chatgpt
      • %%dalle
      • %%palm
      • %%chat
    • TODO Switching on/off copying to the clipboard
      • DONE Per cell
        • With the argument --copy_to_clipboard.
      • TODO Global
        • Can be done via the chat meta cell, but maybe a more elegant, bureaucratic solution exists.
  • DONE Formatted output: asis, html, markdown
    • %%chatgpt
    • %%palm
    • %%chat
  • DONE DALL-E image variations cell
    • Combined image variations and edits with %%dalle.
  • TODO Mermaid-JS cell
  • TODO ProdGDT cell
  • MAYBE DeepL cell
    • See "deepl-python"
• TODO Documentation
  • TODO Multi-cell LLM chats movie (teaser)
  • TODO Multi-cell LLM chats movie (comprehensive)
  • TODO LLM service cells movie (short)
  • TODO Code generation

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References

Packages

[AAp1] Anton Antonov, LLMFunctionObjects Python package, (2023), Python-packages at GitHub/antononcube.

[AAp2] Anton Antonov, LLMPrompts Python package, (2023), Python-packages at GitHub/antononcube.

[AAp3] Anton Antonov, Jupyter::Chatbook Raku package, (2023), GitHub/antononcube.

[ASp1] Al Sweigart, pyperclip (Python package), (2013-2021), PyPI.org/AlSweigart.

[GAIp1] Google AI, google-generativeai (Google Generative AI Python Client), (2023), PyPI.org/google-ai.

[OAIp1] OpenAI, openai (OpenAI Python Library), (2020-2023), PyPI.org.

Videos

[AAv1] Anton Antonov, "Jupyter Chatbook multi cell LLM chats teaser (Raku)", (2023), YouTube/@AAA4Prediction.

[AAv2] Anton Antonov, "Jupyter Chatbook multi cell LLM chats teaser (Python)", (2023), YouTube/@AAA4Prediction.