Developer Builds Local SQLite Retrieval Layer to Feed Relevant Memory to LLMs

A developer working on a local-first AI system has implemented a search and retrieval layer that connects a SQLite-based memory store to a large language model's prompt window. The system uses keyword search functions with SQLite's LIKE operator to perform case-insensitive lookups across stored memory titles and content, with support for filtering by context type. Results are ranked by importance and recency using database-level ordering and limits, ensuring only the most relevant records are fetched without loading unnecessary data into memory. A dedicated function then formats the raw database records into structured text blocks that help the LLM clearly distinguish between different memory entries. Timing tests showed the entire retrieval and formatting process completes in milliseconds, confirming the local approach adds no noticeable delay to the user experience.