Integrating large language models (LLMs) into applications is complicated and heavily dependent on the specific model provider you use.
The AI SDK standardizes integrating artificial intelligence (AI) models across supported providers. This enables developers to focus on building great AI applications, not waste time on technical details.
For example, here’s how you can generate text with various models using the AI SDK:
Get access to our most powerful models with a few lines of code.
1 completion = client.chat.completions.create (
2 model = "text-davinci-003",
3 prompt = "Once upon a time",
4 max_tokens = 100
5 )
6 Connected ...,
7 Response: "Once upon a time, there was a unicorn"import OceanAI from 'oceanai'
const oceanai = new OceanAI()
async function main(){
const assistant = await oceanai.createAssistant(
model : 'davinci',
assistant : 'chat' ,
language : 'en',
})
main()Build AI assistants within your own applications that can leverage models, tools, and knowledge to do complex, multi-step tasks.
To effectively leverage the AI SDK, it helps to familiarize yourself with the following concepts:
Generative artificial intelligence refers to models that predict and generate various types of outputs (such as text, images, or audio) based on what’s statistically likely, pulling from patterns they’ve learned from their training data. For example:
A large language model (LLM) is a subset of generative models focused primarily on text. An LLM takes a sequence of words as input and aims to predict the most likely sequence to follow. It assigns probabilities to potential next sequences and then selects one. The model continues to generate sequences until it meets a specified stopping criterion.
LLMs learn by training on massive collections of written text, which means they will be better suited to some use cases than others. For example, a model trained on GitHub data would understand the probabilities of sequences in source code particularly well.
However, it's crucial to understand LLMs' limitations. When asked about less known or absent information, like the birthday of a personal relative, LLMs might "hallucinate" or make up information. It's essential to consider how well-represented the information you need is in the model.
An embedding model is used to convert complex data (like words or images) into a dense vector (a list of numbers) representation, known as an embedding. Unlike generative models, embedding models do not generate new text or data. Instead, they provide representations of semantic and syntactic relationships between entities that can be used as input for other models or other natural language processing tasks.
In the next section, you will learn about the difference between models providers and models, and which ones are available in the AI SDK.