NodeJS & AI: Building Smarter Applications

A model in AI is a specialized program trained on massive amounts of data to recognize patterns and make predictions or decisions. These models perform specific tasks, such as understanding text, generating content, or classifying images.

Large language models are designed to understand and generate human language, and they are based on transformer architecture. The foundational paper for this architecture is 'Attention is All You Need.'

Closed models, like ChatGPT or Gemini, are accessed via API and come with a cost, whereas open-source models can be downloaded and run locally or hosted on cloud infrastructure. Examples of open-source models include Lama and DeepSeq.

RAC, which stands for Retrieve of Method Generation, helps reduce hallucination in AI models by providing relevant information to improve accuracy and enable the use of private data for more powerful models.

Embedding transforms data, particularly text, into a list of numbers to capture its semantic meaning. This positions the text in a high-dimensional space where similar meanings are closer together, facilitating retrieval tasks like similarity search.

Vectors represent the semantic meaning of text and are stored in vector databases. They allow for similarity searches to retrieve information based on how close and semantically similar the meanings are.

The retrieval techniques mentioned include keyword or term-based, semantic, and hybrid retrieval. In the example, semantic retrieval is used to match user queries with relevant information.

An agent in AI is a system that works to achieve specific goals by planning, reasoning, and using tools. Unlike simple prompt-response models, agents plan their approach, maintain context, and adapt to fulfill tasks rather than just provide static responses.

The React pattern involves defining functions (tools), setting a mission or goal, reasoning through the mission, executing actions with appropriate tools, observing the results, and synthesizing a response based on the gathered context.