Text Models (LLM)

Overview

The AI/ML API provides access to text-based models, also known as Large Language Models (LLMs), and allows you to interact with them through natural language (that's why a third common name for such models is chat models). These models can be applied to various tasks, enabling the creation of diverse applications using our API. For example, text models can be used to:

• Create a system that searches your photos using text prompts.

• Act as a psychological supporter.

• Play games with you through natural language.

• Assist you with coding.

• Perform a security assessment (pentests) on servers for vulnerabilities.

• Write documentation for your services.

• Serve as a grammar corrector for multiple languages with deep context understanding.

• And much more.

We support numerous text models. You can view the complete list here.

Text Model Specific Capabilities

There are several capabilities of text models that are worth mentioning separately.

Completion allows the model to analyze a given text fragment and predict how it might continue based on the probabilities of the next possible tokens or characters. Chat Completion extends this functionality, enabling a simulated dialogue between the user and the model based on predefined roles (e.g., "strict language teacher" and "student"). A detailed description and examples can be found here.

An evolution of chat completion includes Assistants (preconfigured conversational agents with specific roles) and Threads (a mechanism for maintaining conversation history for context). Examples of these capabilities can be found here.

Function Calling allows a chat model to invoke external programmatic tools (e.g., a function you have written) while generating a response. A detailed description and examples are available here.

Quick Code Example

We will call the gpt-4o model using the Python programming language and the OpenAI SDK.

If you need a more detailed explanation of how to call a model's API in code, check out our QUICKSTART section.

%pip install openai
import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.aimlapi.com/v1",

    # Insert your AIML API Key in the quotation marks instead of <YOUR_API_KEY>.
    api_key="<YOUR_API_KEY>",  
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "system",
            "content": "You are an AI assistant who knows everything.",
        },
        {
            "role": "user",
            "content": "Tell me, why is the sky blue?"
        },
    ],
)

message = response.choices[0].message.content

print(f"Assistant: {message}")

By running this code example, we received the following response from the chat model:

Assistant: The sky appears blue due to a phenomenon called Rayleigh scattering. When sunlight enters Earth's atmosphere, it collides with gas molecules and small particles. Sunlight is made up of different colors, each with different wavelengths. Blue light has a shorter wavelength and is scattered in all directions by the gas molecules in the atmosphere more than other colors with longer wavelengths, such as red or yellow.
As a result, when you look up at the sky during the day, you see this scattered blue light being dispersed in all directions, making the sky appear blue to our eyes. During sunrise and sunset, the sun's light passes through a greater thickness of Earth's atmosphere, scattering the shorter blue wavelengths out of your line of sight and leaving the longer wavelengths, like red and orange, more dominant, which is why the sky often turns those colors at those times.

Next Steps

• Learn what a completion is and understand why modern models are often referred to as chat models.