crystalai 0.28.0

Python client library for the Crystal Computing AI API

Crystal Computing AI Python Library

The Crystal Computing AI Python library provides convenient access to the Crystal Computing AI API from applications written in the Python language. It includes a pre-defined set of classes for API resources that initialize themselves dynamically from API responses which makes it compatible with a wide range of versions of the Crystal Computing AI API.

You can find usage examples for the Crystal Computing AI Python library in our API reference and the Crystal Computing AI Cookbook.

Credit

This library is forked from the OpenAI Python Library which is forked from the Stripe Python Library.

Installation

To start, ensure you have Python 3.7.1 or newer. If you just want to use the package, run:

pip install --upgrade crystalai

After you have installed the package, import it at the top of a file:

import crystalai

To install this package from source to make modifications to it, run the following command from the root of the repository:

python setup.py install

Optional dependencies

Install dependencies for crystalai.embeddings_utils:

pip install crystalai[embeddings]

Install support for Weights & Biases which can be used for fine-tuning:

pip install crystalai[wandb]

Data libraries like numpy and pandas are not installed by default due to their size. They’re needed for some functionality of this library, but generally not for talking to the API. If you encounter a MissingDependencyError, install them with:

pip install crystalai[datalib]

Usage

The library needs to be configured with your Crystal Computing AI account's private API key which is available on our developer platform. Either set it as the CRYSTALAI_API_KEY environment variable before using the library:

export CRYSTALAI_API_KEY='crystal_...'

Or set crystalai.api_key to its value:

crystalai.api_key = "crystal_..."

Examples of how to use this library to accomplish various tasks can be found in the Crystal Computing AI Cookbook. It contains code examples for: classification using fine-tuning, clustering, code search, customizing embeddings, question answering from a corpus of documents. recommendations, visualization of embeddings, and more.

Most endpoints support a request_timeout param. This param takes a Union[float, Tuple[float, float]] and will raise an openai.error.Timeout error if the request exceeds that time in seconds (See: https://requests.readthedocs.io/en/latest/user/quickstart/#timeouts).

Chat completions

Chat models such as gpt-3.5-turbo and gpt-4 can be called using the chat completions endpoint.

completion = crystalai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hello world"}])
print(completion.choices[0].message.content)

You can learn more in our chat completions guide.

Completions

Text models such as babbage-002 or davinci-002 (and our legacy completions models) can be called using the completions endpoint.

completion = crystalai.completion.create(model="davinci-002", prompt="Hello world")
print(completion.choices[0].text)

You can learn more in our completions guide.

Embeddings

Embeddings are designed to measure the similarity or relevance between text strings. To get an embedding for a text string, you can use following:

text_string = "sample text"

model_id = "text-embedding-ada-002"

embedding = crystalai.Embedding.create(input=text_string, model=model_id)['data'][0]['embedding']

You can learn more in our embeddings guide.

Fine-tuning

Fine-tuning a model on training data can both improve the results (by giving the model more examples to learn from) and lower the cost/latency of API calls by reducing the need to include training examples in prompts.

# Create a fine-tuning job with an already uploaded file
crystalai.FineTuningJob.create(training_file="file-abc123", model="gpt-3.5-turbo")

# List 10 fine-tuning jobs
crystalai.FineTuningJob.list(limit=10)

# Retrieve the state of a fine-tune
crystalai.FineTuningJob.retrieve("ft-abc123")

# Cancel a job
crystalai.FineTuningJob.cancel("ft-abc123")

# List up to 10 events from a fine-tuning job
crystalai.FineTuningJob.list_events(id="ft-abc123", limit=10)

# Delete a fine-tuned model (must be an owner of the org the model was created in)
crystalai.Model.delete("ft:gpt-3.5-turbo:acemeco:suffix:abc123")

You can learn more in our fine-tuning guide.

To log the training results from fine-tuning to Weights & Biases use:

crystalai wandb sync

For more information, read the wandb documentation on Weights & Biases.

Moderation

Crystal Computing AI provides a free Moderation endpoint that can be used to check whether content complies with the Crystal Computing AI content policy.

moderation_resp = crystalai.Moderation.create(input="Here is some perfectly innocuous text that follows all Crystal Computing AI content policies.")

You can learn more in our moderation guide.

Async API

Async support is available in the API by prepending a to a network-bound method:

async def create_chat_completion():
    chat_completion_resp = await crystalai.ChatCompletion.acreate(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hello world"}])

To make async requests more efficient, you can pass in your own aiohttp.ClientSession, but you must manually close the client session at the end of your program/event loop:

from aiohttp import ClientSession
crystalai.aiosession.set(ClientSession())

# At the end of your program, close the http session
await crystalai.aiosession.get().close()

Command-line interface

This library additionally provides an crystalai command-line utility which makes it easy to interact with the API from your terminal. Run crystalai api -h for usage.

# list models
crystalai api models.list

# create a chat completion (gpt-3.5-turbo, gpt-4, etc.)
crystalai api chat_completions.create -m gpt-3.5-turbo -g user "Hello world"

# create a completion (text-davinci-003, text-davinci-002, ada, babbage, curie, davinci, etc.)
crystalai api completions.create -m ada -p "Hello world"

# generate images via DALL·E API
crystalai api image.create -p "two dogs playing chess, cartoon" -n 1

# using crystalai through a proxy
crystalai --proxy=http://proxy.com api models.list