cosette

Install

pip install cosette

Getting started

OpenAI’s Python SDK will automatically be installed with Cosette, if you don’t already have it.

from cosette import *

Cosette only exports the symbols that are needed to use the library, so you can use import * to import them. Alternatively, just use:

import cosette

…and then add the prefix cosette. to any usages of the module.

Cosette provides models, which is a list of models currently available from the SDK.

models

('gpt-4o',
 'gpt-4-turbo',
 'gpt-4',
 'gpt-4-32k',
 'gpt-3.5-turbo',
 'gpt-3.5-turbo-instruct')

For these examples, we’ll use GPT-4o.

model = models[0]

Chat

The main interface to Cosette is the Chat class, which provides a stateful interface to the models:

chat = Chat(model, sp="""You are a helpful and concise assistant.""")
chat("I'm Jeremy")

Hi Jeremy! How can I assist you today?

r = chat("What's my name?")
r

Your name is Jeremy. How can I assist you further?

As you see above, displaying the results of a call in a notebook shows just the message contents, with the other details hidden behind a collapsible section. Alternatively you can print the details:

print(r)

ChatCompletion(id='chatcmpl-9R8Z1c76TFqYFYjyON08CbkAmjerN', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Your name is Jeremy. How can I assist you further?', role='assistant', function_call=None, tool_calls=None))], created=1716254803, model='gpt-4o-2024-05-13', object='chat.completion', system_fingerprint='fp_729ea513f7', usage=In: 43; Out: 12; Total: 55)

You can use stream=True to stream the results as soon as they arrive (although you will only see the gradual generation if you execute the notebook yourself, of course!)

for o in chat("What's your name?", stream=True): print(o, end='')

I don't have a personal name, but you can call me Assistant. How can I help you today, Jeremy?

Tool use

Tool use lets the model use external tools.

We use docments to make defining Python functions as ergonomic as possible. Each parameter (and the return value) should have a type, and a docments comment with the description of what it is. As an example we’ll write a simple function that adds numbers together, and will tell us when it’s being called:

def sums(
    a:int,  # First thing to sum
    b:int=1 # Second thing to sum
) -> int: # The sum of the inputs
    "Adds a + b."
    print(f"Finding the sum of {a} and {b}")
    return a + b

Sometimes the model will say something like “according to the sums tool the answer is” – generally we’d rather it just tells the user the answer, so we can use a system prompt to help with this:

sp = "Never mention what tools you use."

We’ll get the model to add up some long numbers:

a,b = 604542,6458932
pr = f"What is {a}+{b}?"
pr

'What is 604542+6458932?'

To use tools, pass a list of them to Chat:

chat = Chat(model, sp=sp, tools=[sums])

Now when we call that with our prompt, the model doesn’t return the answer, but instead returns a tool_use message, which means we have to call the named tool with the provided parameters:

r = chat(pr)
r

Finding the sum of 604542 and 6458932

• id: chatcmpl-9R8Z2JNenseQyQoseIs8XNImmy2Bo
• choices: [Choice(finish_reason=‘tool_calls’, index=0, logprobs=None, message=ChatCompletionMessage(content=None, role=‘assistant’, function_call=None, tool_calls=[ChatCompletionMessageToolCall(id=‘call_HV4yaZEY1OYK1zYouAcVwfZK’, function=Function(arguments=‘{“a”:604542,“b”:6458932}’, name=‘sums’), type=‘function’)]))]
• created: 1716254804
• model: gpt-4o-2024-05-13
• object: chat.completion
• system_fingerprint: fp_729ea513f7
• usage: CompletionUsage(completion_tokens=21, prompt_tokens=96, total_tokens=117)

Cosette handles all that for us – we just have to pass along the message, and it all happens automatically:

chat()

The sum of 604542 and 6458932 is 7063474.

You can see how many tokens have been used at any time by checking the use property.

chat.use

In: 224; Out: 39; Total: 263

Tool loop

We can do everything needed to use tools in a single step, by using Chat.toolloop. This can even call multiple tools as needed solve a problem. For example, let’s define a tool to handle multiplication:

def mults(
    a:int,  # First thing to multiply
    b:int=1 # Second thing to multiply
) -> int: # The product of the inputs
    "Multiplies a * b."
    print(f"Finding the product of {a} and {b}")
    return a * b

Now with a single call we can calculate (a+b)*2 – by passing show_trace we can see each response from the model in the process:

chat = Chat(model, sp=sp, tools=[sums,mults])
pr = f'Calculate ({a}+{b})*2'
pr

'Calculate (604542+6458932)*2'

def pchoice(r): print(r.choices[0])

r = chat.toolloop(pr, trace_func=pchoice)

Finding the sum of 604542 and 6458932
Finding the product of 2 and 1
Choice(finish_reason='tool_calls', index=0, logprobs=None, message=ChatCompletionMessage(content=None, role='assistant', function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_OfypQBQoAuIUksucevaxwH5Z', function=Function(arguments='{"a": 604542, "b": 6458932}', name='sums'), type='function'), ChatCompletionMessageToolCall(id='call_yKAL5o96cDef83OFJhDB21MM', function=Function(arguments='{"a": 2}', name='mults'), type='function')]))
Finding the product of 7063474 and 2
Choice(finish_reason='tool_calls', index=0, logprobs=None, message=ChatCompletionMessage(content=None, role='assistant', function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_Ffye7Tf65CjVjwwx8Sp8031i', function=Function(arguments='{"a":7063474,"b":2}', name='mults'), type='function')]))
Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='The result of \\((604542 + 6458932) \\times 2\\) is 14,126,948.', role='assistant', function_call=None, tool_calls=None))

OpenAI uses special tags for math equations, which we can replace using wrap_latex:

wrap_latex(contents(r))

The result of $(604542 + 6458932) \times 2$ is 14,126,948.

Images

As everyone knows, when testing image APIs you have to use a cute puppy.

fn = Path('samples/puppy.jpg')
display.Image(filename=fn, width=200)

<img src="index_files/figure-commonmark/cell-21-output-1.jpeg" width="200" />

We create a Chat object as before:

chat = Chat(model)

Claudia expects images as a list of bytes, so we read in the file:

img = fn.read_bytes()

Prompts to Claudia can be lists, containing text, images, or both, eg:

chat([img, "In brief, what color flowers are in this image?"])

The flowers in the image are purple.

The image is included as input tokens.

chat.use

In: 273; Out: 8; Total: 281

Alternatively, Cosette supports creating a multi-stage chat with separate image and text prompts. For instance, you can pass just the image as the initial prompt (in which case the model will make some general comments about what it sees), and then follow up with questions in additional prompts:

chat = Chat(model)
chat(img)

What an adorable puppy! This puppy has a white and light brown coat and is lying on green grass next to some purple flowers. Puppies like this are commonly seen from breeds such as Cavalier King Charles Spaniels, though without more context, it’s difficult to identify the breed precisely. It looks very playful and cute!

chat('What direction is the puppy facing?')

The puppy is facing slightly to the right of the camera, with its head turned towards the viewer. Its body is positioned in such a way that suggests it is laying down or resting on the grass.

chat('What color is it?')

The puppy has a predominantly white coat with light brown patches, particularly around its ears and eyes. This coloration is commonly seen in certain breeds, such as the Cavalier King Charles Spaniel.

Note that the image is passed in again for every input in the dialog, so that number of input tokens increases quickly with this kind of chat.

chat.use

In: 995; Out: 141; Total: 1136