This week, I tackled the Search and Retrieval portion of our Local Context Store. If capturing data cleanly is about keeping our memory system from turning into a messy Downloads folder, searching is about giving the AI the exact file it needs the moment it asks. My task was to build the retrieval layer, the critical bridge between our stored SQLite records and the prompt window of a Large Language Model (LLM).
The goal was simple but vital: when a user asks the AI a question, the system needs to seamlessly dive into its local memory, find the most relevant past context, and package it up so the LLM can reason with it.
Finding the Right Needles
To make this happen, I implemented a suite of search functions, such as search_context_items and search_context_by_type. Under the hood, these use SQLite's LIKE operator to perform case-insensitive keyword searches across both the title and content of our stored memory. This ensures that if the system is looking for "sqlite", it will reliably find records mentioning "SQLite" regardless of how it was capitalized, and whether the keyword is hiding in the headline or deep in the body text.
I also added the ability to filter these searches by strict context types. If the AI is trying to remember a technical choice, we can restrict the search solely to config_decision records, entirely ignoring irrelevant device_log or learning_record entries.
