Local embeddings with llamadart: how llamas share secrets

This article explores the implementation of local embeddings within 'Llama Village,' a virtual world powered by local AI using the llamadart framework. The author details how the game utilizes EmbeddingGemma to compare dialogue with stored facts, enabling characters to share information dynamically. By mapping text to numeric vectors and calculating cosine similarity, the system identifies potential knowledge transfers. However, the author highlights a critical design challenge: similarity scores alone can be misleading, as they may fail to distinguish between a confession and a denial. To address this, the piece proposes integrating a 'DecisionEngine' to evaluate the intent behind dialogue, ensuring that state changes are based on accurate interpretations rather than just keyword or vector proximity. The article serves as a technical walkthrough for developers looking to implement robust, local AI-driven knowledge systems in their own applications, emphasizing the importance of separating text representation from logical decision-making.