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https://github.com/rgbkrk/chatlab

โšก๏ธ๐Ÿงช Fast LLM Tool Calling Experimentation, big and smol
https://github.com/rgbkrk/chatlab

chatbot chatgpt hacktoberfest interpreter jupyter jupyter-lab jupyter-notebooks noteable openai

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โšก๏ธ๐Ÿงช Fast LLM Tool Calling Experimentation, big and smol

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README 

        
# ChatLab



**Chat Experiments, Simplified**



๐Ÿ’ฌ๐Ÿ”ฌ



ChatLab is a Python package that makes it easy to experiment with OpenAI's chat models. It provides a simple interface for chatting with the models and a way to register functions that can be called from the chat model.



Best yet, it's interactive in the notebook!



## Notebooks to get started with



* [Learning the Basics](./notebooks/basics.ipynb)

* [Recommend and Visualize Color Palettes](./notebooks/color-picker.ipynb)

* [Introduction to the Function Registry](./notebooks/function-registry.ipynb)

* [Creating Knowledge Graphs with Pydantic](./notebooks/knowledge-graph.ipynb)

* [Direct Parallel Function Calling](./notebooks/parallel-function-calling.ipynb)

* [Let the Model do some Data Science](./notebooks/the-data-science-helper.ipynb)



## Introduction



```python

import chatlab

import random



def flip_a_coin():

    '''Returns heads or tails'''

    return random.choice(['heads', 'tails'])



chat = chatlab.Chat()

chat.register(flip_a_coin)



await chat("Please flip a coin for me")

```



ย ๐‘“ย  Ran `flip_a_coin`







Input:



```json

{}

```



Output:



```json

"tails"

```



```markdown

It landed on tails!

```



In the notebook, text will stream into a Markdown output and function inputs and outputs are a nice collapsible display, like with ChatGPT Plugins.



TODO: Include GIF/mp4 of this in action



### Installation



```bash

pip install chatlab

```



### Configuration



You'll need to set your `OPENAI_API_KEY` environment variable. You can find your API key on your [OpenAI account page](https://platform.openai.com/account/api-keys). I recommend setting it in an `.env` file when working locally.



On hosted notebook environments, set it in your Secrets to keep it safe from prying LLM eyes.



## What can `Chat`s enable _you_ to do?



๐Ÿ’ฌ



Where `Chat`s take it next level is with _Chat Functions_. You can



-   declare a function

-   register the function in your `Chat`

-   watch as Chat Models call your functions!



You may recall this kind of behavior from ChatGPT Plugins. Now, you can take this even further with your own custom code.



As an example, let's give the large language models the ability to tell time.



```python

from datetime import datetime

from pytz import timezone, all_timezones, utc

from typing import Optional

from pydantic import BaseModel



def what_time(tz: Optional[str] = None):

    '''Current time, defaulting to UTC'''

    if tz is None:

        pass

    elif tz in all_timezones:

        tz = timezone(tz)

    else:

        return 'Invalid timezone'



    return datetime.now(tz).strftime('%I:%M %p')



class WhatTime(BaseModel):

    tz: Optional[str] = None

```



Let's break this down.



`what_time` is the function we're going to provide access to. Its docstring forms the `description` for the model while the schema comes from the pydantic `BaseModel` called `WhatTime`.



```python

import chatlab



chat = chatlab.Chat()



# Register our function

chat.register(what_time, WhatTime)

```



After that, we can call `chat` with direct strings (which are turned into user messages) or using simple message makers from `chatlab` named `user` and `system`.



```python

await chat("What time is it?")

```



ย ๐‘“ย  Ran `what_time`







Input:



```json

{}

```



Output:



```json

"11:19 AM"

```



```markdown

The current time is 11:19 AM.

```



## Interface



The `chatlab` package exports



### `Chat`



The `Chat` class is the main way to chat using OpenAI's models. It keeps a history of your chat in `Chat.messages`.



#### `Chat.submit`



`submit` is how you send all the currently built up messages over to OpenAI. Markdown output will display responses from the `assistant`.



```python

await chat.submit('What would a parent who says "I have to play zone defense" mean? ')

# Markdown response inline

chat.messages

```



```js

[{'role': 'user',

  'content': 'What does a parent of three kids mean by "I have to play zone defense"?'},

 {'role': 'assistant',

  'content': 'When a parent of three kids says "I have to play zone defense," it means that they...

```



#### `Chat.register`



You can register functions with `Chat.register` to make them available to the chat model. The function's docstring becomes the description of the function while the schema is derived from the `pydantic.BaseModel` passed in.



```python

from pydantic import BaseModel



class WhatTime(BaseModel):

    tz: Optional[str] = None



def what_time(tz: Optional[str] = None):

    '''Current time, defaulting to UTC'''

    if tz is None:

        pass

    elif tz in all_timezones:

        tz = timezone(tz)

    else:

        return 'Invalid timezone'



    return datetime.now(tz).strftime('%I:%M %p')



chat.register(what_time, WhatTime)

```



#### `Chat.messages`



The raw messages sent and received to OpenAI. If you hit a token limit, you can remove old messages from the list to make room for more.



```python

chat.messages = chat.messages[-100:]

```



### Messaging



#### `human`/`user`



These functions create a message from the user to the chat model.



```python

from chatlab import human



human("How are you?")

```



```json

{ "role": "user", "content": "How are you?" }

```



#### `narrate`/`system`



`system` messages, also called `narrate` in `chatlab`, allow you to steer the model in a direction. You can use these to provide context without being seen by the user. One common use is to include it as initial context for the conversation.



```python

from chatlab import narrate



narrate("You are a large bird")

```



```json

{ "role": "system", "content": "You are a large bird" }

```



## Development



This project uses poetry for dependency management. To get started, clone the repo and run



```bash

poetry install -E dev -E test

```



We use `ruff` and `mypy`.



## Contributing



Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.