Quickstart - Bespoke Labs

Install Curator

Curator needs Python 3.10 or later.

pip install bespokelabs-curator

Set an API key

This example uses an OpenAI model, so set your OpenAI key.

export OPENAI_API_KEY=<your-api-key>

Send a prompt

Create an LLM object with a model name, then call it with a prompt.

from bespokelabs import curator

llm = curator.LLM(model_name="gpt-4o-mini")
poem = llm("Write a poem about the importance of data in AI.")
print(poem.dataset.to_pandas())
# Output:
#                                             response
# 0  In the realm where silence once held sway,  \n...

The call returns a CuratorResponse. Its dataset attribute holds the results as a Hugging Face Dataset, so you can call to_pandas() on it. To get several responses, pass a list of prompts. Curator sends them in parallel.

from bespokelabs import curator

llm = curator.LLM(model_name="gpt-4o-mini")
poems = llm([
    "Write a poem about the importance of data in AI.",
    "Write a haiku about the importance of data in AI.",
])
print(poems.dataset.to_pandas())
# Output:
#                                             response
# 0  In the realm where silence once held sway,  \n...
# 1  Silent streams of truth,  \nData shapes the le...

Curator caches every response. If you run the same code again, Curator reads the responses from the cache and does not call the model. See Caching and recovery.

Next steps

Read Key concepts to learn how to write your own prompt and parse methods.
Read Structured output to get typed results from the model.
Turn on the Curator Viewer to watch responses while Curator generates them.