OpenAI hero Openlayer integrates with OpenAI in two different ways:
  • If you are building an AI system with OpenAI LLMs and want to evaluate it, you can use the SDKs to make Openlayer part of your workflow.
  • Some tests on Openlayer are based on a score produced by an LLM evaluator. You can set any of OpenAI’s LLMs as the LLM evaluator for these tests.
This integration guide explores each of these paths.

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Evaluating OpenAI LLMs

You can set up Openlayer tests to evaluate your OpenAI LLMs in development and monitoring.

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Development

In development mode, Openlayer becomes a step in your CI/CD pipeline, and your tests get automatically evaluated after being triggered by some events. Openlayer tests often rely on your AI system’s outputs on a validation dataset. As discussed in the Configuring output generation guide, you have two options:
  1. either provide a way for Openlayer to run your AI system on your datasets, or
  2. before pushing, generate the model outputs yourself and push them alongside your artifacts.
For AI systems built with OpenAI LLMs, if you are not computing your system’s outputs yourself, you must provide your API credentials. To do so, navigate to “Settings” > “Workspace secrets,” and add the OPENAI_API_KEY secret. OpenAI secret
For Azure OpenAI, add the AZURE_OPENAI_API_KEY, and AZURE_OPENAI_ENDPOINT secrets instead.
If you don’t add the required OpenAI API key, you’ll encounter a “Missing API key” error when Openlayer tries to run your AI system to get its outputs: OpenAI missing secret
You can use one of the OpenAI templates to check out how a sample project fully set up with Openlayer looks like. We have templates in Python, and TypeScript.

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Monitoring

To use the monitoring mode, you must set up a way to publish the requests your AI system receives to the Openlayer platform. This process is streamlined for OpenAI LLMs. To set it up, you must follow the steps in the code snippet below: 
# 1. Set the environment variables
import os
import openai

os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY_HERE"
os.environ["OPENLAYER_API_KEY"] = "YOUR_OPENLAYER_API_KEY_HERE"
os.environ["OPENLAYER_INFERENCE_PIPELINE_ID"] = "YOUR_OPENLAYER_INFERENCE_PIPELINE_ID_HERE"

# 2. Import the `trace_openai` function and wrap the OpenAI client with it
from openlayer.lib import trace_openai

openai_client = trace_openai(openai.OpenAI())

# 3. From now on, every chat completion/completion call with
# the `openai_client` is traced and published to Openlayer. E.g.,
completion = openai_client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[
        {"role": "user", "content": "How are you doing today?"},
    ]
)

See full Python example

See full TypeScript example

For Azure OpenAI, check out this code example instead.
Once the code is set up, all your OpenAI calls are automatically published to Openlayer, along with metadata, such as latency, number of tokens, cost estimate, and more. If you navigate to the “Requests” page of your Openlayer inference pipeline, you can see the traces for each request. OpenAI trace
If the OpenAI LLM call is just one of the steps of your AI system, you can use the code snippets above together with tracing. In this case, your OpenAI LLM calls get added as a step of a larger trace. You can enhance traces with metadata using update_current_trace(user_id="123", inferenceId="custom_id") and update_current_step(model="gpt-4", tokens=150) for rich observability and request correlation. Refer to the Tracing guide for details.
After your AI system requests are continuously published and logged by Openlayer, you can create tests that run at a regular cadence on top of them. Refer to the Monitoring overview, for details on Openlayer’s monitoring mode, to the Publishing data guide, for more information on setting it up, or to the Tracing guide, to understand how to trace more complex systems.

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OpenAI LLM evaluator

Some tests on Openlayer rely on scores produced by an LLM evaluator. For example, tests that use Ragas metrics and the custom LLM evaluator test. You can use any of OpenAI’s LLMs as the underlying LLM evaluator for these tests. You can change the default LLM evaluator for a project in the project settings page. To do so, navigate to “Settings” > Select your project in the left sidebar > click on “Metrics” to go to the metric settings page. Under “LLM evaluator,” choose the OpenAI LLM you want to use. Furthermore, make sure to add your OPENAI_API_KEY as a workspace secret. LLM evaluator with OpenAI