OpenAI Invokers

This package contains invokers for different OpenAI compatible invocations.

The following invokers are provided:

Native OpenAI interface

This set of invokers implement the native OpenAI client. That client takes an endpoint and a model name, and call the API on that.

OpenAIChatInvoker : An invoker that called the chat completion API. Input is a chat history and the response is the LLMs completion of that chat.

OpenAIChatJsonInvoker : Similar to the OpenAIChatInvoker, this invoker calls the chat completion API, but here the parameter json_object is given as the response format. This should force the LLM to respond with a clean JSON object.

OpenAIImageInvoker : An invoker to call the OpenAI image generation API. Input would be a JSON encoded object of configuration parameters, output is a JSON encoded object containing the properties on the generated image.

Parameters

The native invokers expect the following parameters in the meta.yaml, or their representative environment variable.

OpenAI on Azure interface

These invokers follow the same pattern as their native counterparts, with the difference that they create a client that talks to an OpenAI on Azure endpoint with slightly different configuration of the connection.

AzureOpenAIChatInvoker: A chat completion invoker that uses an OpenAI on Azure client with the appropriate attributes.

AzureOpenAIChatJsonInvoker: The chat completion invoker for OpenAI on Azure, that returns a JSON encoded result object.

AzureOpenAIImageInvoker: The OpenAI on Azure invoker to generate images.

Parameters

Chat Completion

The chat completion API is meant to take chat history into account. The dialogue that was had so far is sent as content parameter to this invoker. This is done by sending the content in the form of a YAML encoded list of objects. See the following example:

- role: assistant
  content: Hey hello! I am a friendly chatbot and I am here to help. What can I help you with?
- role: user
  content: |
    Can you please tell me how to make Apple Pie?
    My grandmother always used to make these and I have never had a better one since she passed away.
    So I guess I am looking for a traditional approach.
    Please give me a bullet list of ingredients, in metric measures, followed by a step-by-step
    approach to making it.

In this example it is clear that the order of the dialogue is important. Also, the use of the | character after content enables us to easily create a multi-line string.

The role can be either "system", for a system prompt, or "assistant" or "user", depending on who made the uttering.

When the content that is passed to the invoker cannot be parsed as a YAML encoded string, a faux chat history is created, for which it is set that the role is "user" and the full content string that is passed is the content for that single chat object.

When using a Jinja template, referring to {{ chat_history }} will in fact render a YAML representation of the dialogue so far.

Image parameters

The generation parameters object should contain the following properties:

These parameters can be set in the meta.yaml for the image invoker. The content that gets passed to the invoker will be used as the prompt value. But, when the content that is sent to this invoker can be parsed as a JSON object, the values in these parameters can be overriden by setting them in that object.