Part 4/11:
David highlights that each message becomes a sizable data object (~42KB), with the embedding comprised of 1,500 floating-point values. While this isn’t the most efficient method for large volumes of data, it’s suitable for prototyping. He mentions that chunking — grouping multiple messages into a single memory segment — will be necessary as conversations grow.
The Core Loop and Data Management
The process runs in an infinite loop (while true in Python). The key steps are:
User Input: Captured from the console.
Embedding Generation: The input is vectorized via OpenAI’s model.
Logging: Data is stored in a JSON format with unique UUIDs to avoid conflicts, ensuring each message’s traceability.