genie-flow-invoker-openai 0.6.10
Genie Flow Invoker OpenAI on Azure
Requires Python: >=3.11.0

# 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.

| parameter            | env variable               | default | description                                                          |
|----------------------|----------------------------|---------|----------------------------------------------------------------------|
| `api_key`            | `OPENAI_API_KEY`           |         | API key to use                                                       |
| `base_url`           | `OPENAI_BASE_URL`          |         | base URL to address the API                                          |
| `model`              | `OPENAI_MODEL`             |         | name of the model to use                                             |
| `backoff_max_time`   | `OPENAI_BACKOFF_MAX_TIME`  | 61      | maximal seconds to wait between retries                              |
| `backoff_max_tries`  | `OPENAI_MAX_BACKOFF_TRIES` | 15      | maximal number of retries                                            |
| `generation`         |                            |         | NB: for image generator only! an object with the parameters to pass  |

## 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
| parameter           | env variable                      | default | description                                                         |
|---------------------|-----------------------------------|---------|---------------------------------------------------------------------|
| `api_key`           | `AZURE_OPENAI_API_KEY`            |         | API key to use                                                      |
| `api_version`       | `AZURE_OPENAI_API_VERSION`        |         | version of the API to use                                           |
| `endpoint`          | `AZURE_OPENAI_ENDPOINT`           |         | endpoint to address the API                                         |
| `deployment_name`   | `AZURE_OPENAI_DEPLOYMENT_NAME`    |         | name of the deployment to use                                       |
| `backoff_max_time`  | `AZURE_OPENAI_BACKOFF_MAX_TIME`   | 61      | maximal seconds to wait between retries                             |
| `backoff_max_tries` | `AZURE_OPENAI_MAX_BACKOFF_TRIES`  | 15      | maximal number of retries                                           |
| `generation`        |                                   |         | NB: for image generator only! an object with the parameters to pass |


## 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:

```yaml
- 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:

| parameter | default   | description                                                   |
|-----------|-----------|---------------------------------------------------------------|
| `prompt`  |           | prompt to use to generate the model                           |
| `size`    | 1024x1024 | the size of the generated image                               |
| `quality` | standard  | the quality of the generated image, can be "standard" or "hd" |
| `n`       | 1         | the number of images to generate in one invocation            |

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.