FAQ.md

Frequently Ased Questions (FAQ)

How should I edit an aiconfig file?

Editing a configshould be done either programmatically via SDK or via the UI (workbooks):

• Programmatic editing.

• Edit with a workbook editor: this is similar to editing an ipynb file as a notebook (most people never touch the json ipynb directly)

You should only edit the aiconfig by hand for minor modifications, like tweaking a prompt string or updating some metadata.

Does this support custom endpoints?

Out of the box, AIConfig already supports all OpenAI GPT* models, Google’s PaLM model and any “textgeneration” model on Hugging Face (like Mistral). See Supported Models for more details.

Additionally, you can install aiconfig extensions for additional models (see question below).

Is OpenAI function calling supported?

Yes. This example goes through how to do it.

We are also working on adding support for the Assistants API.

How can I use aiconfig with my own model endpoint?

Model support is implemented as “ModelParser”s in the AIConfig SDK, and the idea is that anyone, including you, can define a ModelParser (and even publish it as an extension package).

All that’s needed to use a model with AIConfig is a ModelParser that knows

• how to serialize data from a model into the aiconfig format
• how to deserialize data from an aiconfig into the type the model expects
• how to run inference for model.

For more details, see Extensibility.

When should I store outputs in an aiconfig?

The AIConfigRuntime object is used to interact with an aiconfig programmatically (see SDK usage guide). As you run prompts, this object keeps track of the outputs returned from the model.

You can choose to serialize these outputs back into the aiconfig by using the config.save(include_outputs=True) API. This can be useful for preserving context -- think of it like session state.

For example, you can use aiconfig to create a chatbot, and use the same format to save the chat history so it can be resumed for the next session.

You can also choose to save outputs to a different file than the original config -- config.save("history.aiconfig.json", include_outputs=True).

Why should I use aiconfig instead of things like configurator?

It helps to have a standardized format specifically for storing generative AI prompts, inference results, model parameters and arbitrary metadata, as opposed to a general-purpose configuration schema.

With that standardization, you just need a layer that knows how to serialize/deserialize from that format into whatever the inference endpoints require.

This looks similar to ipynb for Jupyter notebooks

We believe that notebooks are a perfect iteration environment for generative AI -- they are flexible, multi-modal, and collaborative.

The multi-modality and flexibility offered by notebooks and ipynb offers a good interaction model for generative AI. The aiconfig file format is extensible like ipynb, and AI Workbook editor allows rapid iteration in a notebook-like IDE.

AI Workbooks are to AIConfig what Jupyter notebooks are to ipynb

There are 2 areas where we are going beyond what notebooks offer:

1. aiconfig is more source-control friendly than ipynb. ipynb stores binary data (images, etc.) by encoding it in the file, while aiconfig recommends using file URI references instead.
2. aiconfig can be imported and connected to application code using the AIConfig SDK.