Powering Local-First AI: Searching and Retrieving Context for Inference

A developer built the search and retrieval layer for a local-first AI context store, enabling efficient memory recall for LLM inference. The system uses SQLite with keyword search, type filtering, and importance-based ranking to surface the most relevant context. A formatting function prepares the data for the LLM, and timing measurements confirm sub-millisecond retrieval speeds.

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. 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. Finding matching text is only half the battle. Because an LLM's context window is limited and valuable, we can't just feed the model a random assortment of keyword matches. We have to guarantee that the best information surfaces first. I solved this by enforcing a strict hierarchy in the database queries: ORDER BY importance DESC, created at DESC . This means high-priority memories are always fetched before trivial notes, and recent memories are prioritized over older ones. Furthermore, I pushed the LIMIT constraint directly down to the database level. Instead of fetching hundreds of rows into Python memory just to slice off the top five, the database does the heavy lifting, returning only the most critical handful of records. Once the database returns those top records, they are still just raw data dictionaries. To be useful, they have to be translated into a language the LLM understands natively: clean, structured text. I built the prepare context for inference function to transform those raw rows into a highly readable text block. Instead of dumping raw JSON into the prompt, the AI receives cleanly formatted blocks indicating the index, type, title, and content of each memory. This structural clarity helps the LLM easily parse the context boundaries so it doesn't confuse a past project note with the user's current question. Finally, because this is a local-first system, speed is non-negotiable. I integrated a lightweight timing mechanism to measure exactly how fast these database lookups happen. Seeing the context retrieved and formatted in just a few milliseconds proved that our local SQLite approach is blazing fast, meaning we can inject memories into prompts without introducing noticeable lag to the user experience. This task proved to me that effective AI memory isn't just about storing data. It relies just as heavily on efficiently ranking, filtering, and structuring that data for inference. With this retrieval layer in place, our AI won't just passively store memories, it will actively know how to use them.