Tutorial on using pre-trained OpenAI language models

 Under construction -- no ready to be used yet

In this tutorial you will learn how to use language models (via the OpenAI API) from your Analytica model, and also learn about language models and how they work.

Adding the OpenAI API library to your model

( Downloading the library) ( Create new model. Add library to model ) ( Getting and entering the OpenAI API key )

The OpenAI API Library needs to be integrated into your model for you to access its functionality. Here's a step-by-step guide on how to do it:

1. Download the OpenAI API Library: The first step is to download the OpenAI library. Make sure to save it in a location you can easily access.
2. Create a New Model: After the download is complete, open Analytica and create a new model. This will serve as your workspace.
3. Add the Library to the Model: Locate the "File" button on the top-left corner of the Analytica interface, click on it, and select the option "Add Module".
4. Select the Library File: In the file selection dialog that appears, navigate to where you saved the OpenAI library file and select it.
5. Embed the Library: Once you've selected the library file, a new dialog will open up. Choose the "Embed a copy" option. This action embeds a copy of the library directly into your model.
6. Save Your API Key: Lastly, open the embedded OpenAI API library module. Locate the section where you can save your API key. Input your OpenAI API key into the designated environment variable to finalize the setup.

What OpenAI models are available to use?

Base Models:

• ada
• babbage
• curie
• davinci
• gpt-3.5-turbo
• whisper-1

Code Search Models:

• ada-code-search-code
• ada-code-search-text
• babbage-code-search-code
• babbage-code-search-text
• code-search-ada-code-001
• code-search-ada-text-001
• code-search-babbage-code-001
• code-search-babbage-text-001

Search Document Models:

• ada-search-document
• babbage-search-document
• curie-search-document
• davinci-search-document
• text-search-ada-doc-001
• text-search-babbage-doc-001
• text-search-curie-doc-001
• text-search-davinci-doc-001

Search Query Models:

• ada-search-query
• babbage-search-query
• curie-search-query
• davinci-search-query
• text-search-ada-query-001
• text-search-babbage-query-001
• text-search-curie-query-001
• text-search-davinci-query-001

Similarity Models:

• ada-similarity
• babbage-similarity
• curie-similarity
• davinci-similarity
• text-similarity-ada-001
• text-similarity-babbage-001
• text-similarity-curie-001
• text-similarity-davinci-001

Instruction Models:

• curie-instruct-beta
• davinci-instruct-beta

Edit Models:

• code-davinci-edit-001
• text-davinci-edit-001

Text Models:

• text-ada-001
• text-babbage-001
• text-curie-001
• text-davinci-001
• text-davinci-002
• text-davinci-003
• text-embedding-ada-002

Turbo Models:

• gpt-3.5-turbo-0301
• gpt-3.5-turbo-0613
• gpt-3.5-turbo-16k
• gpt-3.5-turbo-16k-0613

Generating text completions

This step is all about constructing a model capable of generating text sequences, with the specific characteristics of these sequences being determined by the parameters you define. Here's how you do it:

1. Create an Index Node for Completions: Begin by constructing an Index node and name it "Completion number". This node plays an important role in your model, as it dictates the number of different text completions your model will generate.
2. Set Up a Variable Node for Prompt Generation and Define the Command: Next, establish a variable node and title it "Example of prompt generation". Inside this node, you'll input the following command:

Prompt_completion("In the night sky, ", Completion_index:Completion_number, max_tokens:100)

This command instructs the model to create text that continues from the prompt "There once was a boy". The parameter "Completion_number" controls the quantity of unique completions the model will generate. The max_tokens:100 limits each text output to approximately 100 tokens in length.

This configuration provides a playground for you to experiment with your model, modify the parameters, and observe how changes affect the text generation. Through this interactive learning process, you'll gain deeper insights into how to navigate your model effectively to generate the desired results.

Word likelihoods

(tutorial about next token probabilities. logits and perplexity, etc.)

In-context learning

(What is in-context learning?) (Some examples: Translation, ....)

Achieving the desired task with a pre-trained language model can be quite challenging. While these models possess vast knowledge, they might struggle to comprehend the specific task's format unless tuned or conditioned.

Therefore, it is common to write prompts that tell the language model what the format of the task they are supposed to be accomplishing. This method is called in-context learning. In the case where the provided prompt contains several examples of the task that the model is supposed to accomplish, it is known as “few-shot learning,” since the model is learning perform the task based on a few examples.

Creating a typical prompt often has two essential parts:

1. Instructions: This component serves as a set of guidelines, instructing the model on how to approach and accomplish the given task. For certain models like OpenAI's text-davinci-003, which are fine-tuned to follow specific instructions, the instruction string becomes even more crucial.
2. Demonstrations: These demonstration strings provide concrete examples of successfully completing the task at hand. By presenting the model with these instances, it gains valuable insights to adapt and generalize its understanding.

For example, suppose we want to design a prompt for the task of translating words from English to Chinese. A prompt for this task could look like the following:

Translate English to Chinese.

dog -> 狗

apple -> 苹果

coffee -> 咖啡

supermarket -> 超市

squirrel ->

Given this prompt, most large language models should answer with the correct answer: "松鼠"

Creating classifiers using in-context learning

(Classifier Task 1)

(Download the training / test set) (Write the n-shot prompt) (Evaluate performance on several test cases)

Similarity embeddings

(Uses) (Computing similarities) (Indexing a collection of docs) (Adding similar snippet to LLM prompts)