January 2024 Summaries

The text discusses the implementation of semantic search using AI and embedding models. It explains how to convert any string of text into a vector and store it in a vector database, where nearby vectors mean approximately the same thing. The author demonstrates building a search engine for their side project website Braggoscope, which uses an unofficial directory of BBC Radio 4's show In Our Time. They walk through setting up a vector database, embedding model, and creating a minimal PartyKit server to manage indexing and querying. The text also touches upon the use of vector databases in retrieval-augmented generation (RAG) for AI chatbots and copilot experiences.

PartyKit AI, a selection of LLMs and a vector database integrated into the PartyKit environment, is designed to make using AI models as simple as possible. It offers various AI models such as text generation, text-to-image transformation, speech-to-text conversion, and text and image classification. The integration of AI with real-time collaboration in PartyKit allows for advanced capabilities like semantic search and Retrieval-Augmented Generation (RAG). Users can connect to third-party vector databases or use alternative Large Language Models (LLMs) beyond Llama2 and Mistral. The combination of long-lived multiplayer rooms and AI models opens up a world of possibilities for building AI agents and collaborative experiences between humans and AI.