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What would you do

Reddit r/LocalLLaMA / 3/15/2026

💬 OpinionDeveloper Stack & InfrastructureIdeas & Deep AnalysisTools & Practical Usage
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Key Points

  • The post discusses fact extraction from conversations using SQLite FTS5 and the limitations of keyword search in capturing semantic connections.
  • It highlights examples like 'I hate cold weather' or 'where should I vacation' that require semantic understanding beyond simple keywords.
  • It compares using a vector-based memory system versus querying a base language model (e.g., base-en-v1.5) and notes latency trade-offs.
  • It weighs building regex patterns against letting the LLM handle extraction, noting results vary with complexity and LLM parameters.
  • Overall, it emphasizes that the best approach depends on the specific complexity and configuration of the LLM powering the system.

So working with fact extraction from conversations been doing it so far with SQlight and FTS5. The main issue I keep running into is keyword searching, misses semantic connections such as I hate cold weather or where should I vacation it can’t pick out all the useful parts. Is using a vector system for memory better or is the latency trade-off worse than just using an in group language model like the base-en-v1.5. Also building reggex patterns versus just letting the LLM handle It itself has been a battle of latency and confusion for me because I get tossed results on both sides. It honestly depends on the complexity and parameters of the LLM powering it.

submitted by /u/Mastertechz
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