cursivepy 0.1.5

No project description provided

Cursive is a universal and intuitive framework for interacting with LLMs.

It works in any JavaScript runtime and has a heavy focus on extensibility and developer experience.

highlights

✦ Extensible - You can easily hook into any part of a completion life cycle. Be it to log, cache, or modify the results.

✦ Functions - Easily describe functions that the LLM can use along with its definition, with any model (currently supporting GPT-4, GPT-3.5, Claude 2, and Claude Instant)

✦ Universal - Cursive's goal is to bridge as many capabilities between different models as possible. Ultimately, this means that with a single interface, you can allow your users to choose any model.

✦ Informative - Cursive comes with built-in token usage and costs calculations, as accurate as possible.

✦ Reliable - Cursive comes with automatic retry and model expanding upon exceeding context length. Which you can always configure.

quickstart

1. Install.

poetry add cursivepy
# or
pip install cursivepy

2. Start using.

from cursive import Cursive

const cursive = Cursive()

response = cursive.ask(
    prompt='What is the meaning of life?',
)

print(response.answer)

usage

Conversation

Chaining a conversation is easy with cursive. You can pass any of the options you're used to with OpenAI's API.

res_a = cursive.ask(
    prompt='Give me a good name for a gecko.',
    model='gpt-4',
    max_tokens=16,
)

print(res_a.answer) # Zephyr

res_b = res_b.conversation.ask(
    prompt='How would you say it in Portuguese?'
)

print(res_b.answer) # Zéfiro

Streaming

Streaming is also supported, and we also keep track of the tokens for you!

result = cursive.ask(
    prompt='Count to 10',
    stream=True,
    on_token=lambda partial: print(partial['content'])
)

print(result.usage.total_tokens) # 40

Functions

You can use very easily to define and describe functions, along side with their execution code.

from cursive import cursive_function, Cursive

cursive = Cursive()

@cursive_function()
def add(a: float, b: float):
    """
    Adds two numbers.
    
    a: The first number.
    b: The second number.
    """
    return a + b

res = cursive.ask(
    prompt='What is the sum of 232 and 243?',
    functions=[sum],
)

print(answer) # The sum of 232 and 243 is 475.

The functions' result will automatically be fed into the conversation and another completion will be made. If you want to prevent this, you can add pause to your function definition.

@cursive_function(pause=True)
def create_character(name: str, age: str):
    """
    Creates a character.
    
    name: The name of the character.
    age: The age of the character.
    """
    return {
        'name': name,
        'age': age,
    }

res = cursive.ask({
    prompt='Create a character named John who is 23 years old.',
    functions=[createCharacter],
})

print(res.function_result) # { name: 'John', age: 23 }

Hooks

You can hook into any part of the completion life cycle.

cursive.on('completion:after', lambda result: print(
    result.data.cost.total,
    result.data.usage.total_tokens,
))

cursive.on('completion:error', lambda result: print(
    result.error,
))

cursive.ask({
    prompt: 'Can androids dream of electric sheep?',
})

# 0.0002185
# 113

Embedding

You can create embeddings pretty easily with cursive.

embedding = cursive.embed('This should be a document.')

This will support different types of documents and integrations pretty soon.

Reliability

Cursive comes with automatic retry with backoff upon failing completions, and model expanding upon exceeding context length -- which means that it tries again with a model with a bigger context length when it fails by running out of it.

You can configure this behavior by passing the retry and expand options to useCursive.

cursive = Cursive(
    max_retries=5, # 0 disables it completely
    expand={
        'enable': true,
        'defaults_to': 'gpt-3.5-turbo-16k',
        'model_mapping': {
            'gpt-3.5-turbo': 'gpt-3.5-turbo-16k',
            'gpt-4': 'claude-2',
        },
    },
)

roadmap

vendor support

• Anthropic
• Cohere
• Azure OpenAI models
• Huggingface
• Replicate