Use the OpenPipe SDK as a drop-in replacement for the generic OpenAI package. Calls sent through the OpenPipe SDK will be recorded by default for later training. You’ll use this same SDK to call your own fine-tuned models once they’re deployed.

Find the SDK at https://pypi.org/project/openpipe/

Installation

pip install openpipe

Simple Integration

Add OPENPIPE_API_KEY to your environment variables.

export OPENPIPE_API_KEY=opk-<your-api-key>
# Or you can set it in your code, see "Complete Example" below

Replace this line

from openai import OpenAI

with this one

from openpipe import OpenAI

Adding Searchable Metadata Tags

OpenPipe follows OpenAI’s concept of metadata tagging for requests. You can use metadata tags in the Request Logs view to narrow down the data your model will train on. We recommend assigning a unique metadata tag to each of your prompts. These tags will help you find all the input/output pairs associated with a certain prompt and fine-tune a model to replace it.

Here’s how you can use the tagging feature:

Complete Example

from openpipe import OpenAI
import os

client = OpenAI(
    # defaults to os.environ.get("OPENAI_API_KEY")
    api_key="My API Key",
    openpipe={
        # defaults to os.environ.get("OPENPIPE_API_KEY")
        "api_key": "My OpenPipe API Key",
        # optional, defaults to process.env["OPENPIPE_BASE_URL"] or https://api.openpipe.ai/api/v1 if not set
        "base_url": "My URL",
    }
)

completion = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "system", "content": "count to 10"}],
    metadata={"prompt_id": "counting", "any_key": "any_value"},
)

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Should I Wait to Enable Logging?

We recommend keeping request logging turned on from the beginning. If you change your prompt you can just set a new prompt_id metadata tag so you can select just the latest version when you’re ready to create a dataset.

Quick StartLogging Requests