It embeds essay passages into vectors stored in a Postgres database with pgvector, then retrieves relevant passages based on semantic similarity to user queries. The chat interface feeds retrieved passages into GPT-3.5-turbo to generate contextual answers, combining vector-based retrieval with LLM generation. All code, data, and embeddings are open-source.