helixml / chat-widget

LLMs

This section provides an overview of Large Language Models (LLMs) and how they are integrated within the chat widget.

What are LLMs?

LLMs are a type of artificial intelligence (AI) that are trained on massive amounts of text data. They can understand and generate human-like text, making them suitable for a wide range of applications, including chatbots, translation, and content creation.

Reference: https://en.wikipedia.org/wiki/Large_language_model

LLM Models:

The chat widget uses LLMs provided through APIs. Here are some popular LLM models:

1. GPT-3: Developed by OpenAI, GPT-3 is a powerful LLM with a vast vocabulary and ability to perform various language tasks, including generating creative text formats, translating languages, and writing different kinds of creative content.

2. GPT-4: The latest iteration of GPT-3, GPT-4 offers enhanced capabilities and improved performance across various language tasks.

Reference: https://openai.com/

3. LaMDA: Developed by Google, LaMDA is a conversational AI chatbot that can engage in open-ended dialogue on a wide range of topics.

Reference: https://ai.google.com/

Accessing LLMs Through APIs:

To integrate LLMs into the chat widget, we use APIs provided by the LLM providers. These APIs allow us to send requests to the LLM and receive generated responses.

Examples:

GPT-3 API: https://beta.openai.com/docs/api-reference/introduction

LaMDA API: https://developers.google.com/workspace/reference/rest

Code Example (GPT-3 API):

import openai
          
          openai.api_key = "YOUR_API_KEY"
          
          response = openai.Completion.create(
            engine="text-davinci-003",
            prompt="Write a short story about a cat who loves to travel.",
            max_tokens=100,
            temperature=0.7,
          )
          
          print(response.choices[0].text)

Reference: https://beta.openai.com/docs/api-reference/completions

Considerations when using LLMs:

  • Bias: LLMs are trained on massive datasets, and these datasets might contain biases. It is important to be aware of potential biases and take steps to mitigate them.

  • Cost: Using LLMs can be costly, especially for large-scale applications. Consider the cost implications before integrating LLMs into your project.

  • Privacy: LLMs might process sensitive information, so it’s crucial to ensure data privacy and security.

  • Ethical Implications: The use of LLMs raises ethical considerations, such as the potential for misuse or the impact on human creativity.

Future of LLMs:

LLMs are a rapidly evolving field. New models with improved capabilities and features are being developed constantly. The future of LLMs promises even more exciting applications and advancements in natural language processing.