Setup LLM Connection Endpoint

tip

This is particularly helpful if you wish to:

• Enable no-code evaluation workflows for non-technical users
• Simulate conversations to be evaluated in a click of a button
• Red-team LLM applications

You can also setup an LLM endpoint that accepts a POST request over HTTPS to enable users to run evaluations directly on the platform without having to code, and start an evaluation through a click of a button instead. At a high level, you would have to provide Confident AI with the mappings to test case parameters such as the actual_output, retrieval_context, etc., and at evaluation time Confident AI will use the dataset and metrics settings you've specified for your experiment to unit test your LLM application.

note

If you're setting this up for red-teaming, all you need to do is return the actual_output of your model, and nothing else such as retrieval_context (although you're free to return additional data).

Create an LLM Endpoint

In order for Confident AI to reach your LLM application, you'll need to expose your LLM in a RESTFUL API endpoint that is accessible over the internet. These are the hard rules you MUST follow when setting up your endpoint:

1. Accepts a POST request over HTTPS.
2. Returns a JSON response and MUST contain the actual_output value somewhere in the returned Json object. Whether or not to supply a retrieval_context or tools_called value in your returned Json is optional, and this depends on whether the metrics you have enabled for your experiment requires these parameters.

caution

When Confident AI calls your LLM endpoint, it does a POST request with a data structure of this type:

{
    "input": "..."
}

This input will be used to unit test your LLM application, and any JSON response returned will be parsed and used to deduce what the remaining test case parameters (i.e. actual_output) values are.

So, it is imperative that your LLM endpoint:

1. Parses the incoming data to extract this input value to carry out generations.
2. Returns the actual_output and any other LLMTestCase parameters in the JSON response with their correct respective type.

For those that want a recap of what types each test case parameter is of, visit the test cases section.

Connect Your LLM Endpoint

Now that you have your endpoint up and running, all that's left is to tell Confident AI how to reach it.

tip

You can setup your LLM connection in Project Settings > LLM Connection. There is also a button for you to ping your LLM endpoint to sanity check that you've setup everything correctly.

You'll have to provide the:

1. HTTPS endpoint you've setup.
2. The json key path to the mandaatory actual_output parameter, and the optional retrieval_context , and tools_called parameters. The json key path is a list of strings.

In order for evaluation to work, you MUST set the json key path for the actual_output parameter. Remember, the actual_output of your LLM application is always required for evaluation, while the retrieval_context and tools_called parameters are optional depending on the metrics you've enabled.

note

The json key path tells Confident AI where to look in your Json response for the respective test case parameter values.

For instance, if you set the key path of the actual_output parameter to ["response", "actual output"], the correct Json response to return from your LLM endpoint is as follows:

{
    "response": {
        "actual output": "must be a string!"
    }
}

That's not to say you can't include other things to return in your Json response, but the key path will determine the variables Confident AI will be using to populate LLMTestCases at evaluation time.

info

If you're wondering why expected_output, context, and expected_tools is not required in setting up your json key path, it is because it is expected that these variables are static, just like the input, and should therefore come from your dataset instead.