Developer builds local AI retrieval system to cut token costs

A developer has created a local-first retrieval system to efficiently answer questions from a large knowledge base of markdown files. The system uses SQLite with FTS5 for fast keyword searching and exact filtering, combined with local ONNX embeddings for semantic ranking. This approach significantly reduces the token cost per answer, moving from thousands of tokens to a few hundred, by avoiding the need to process entire files. AI

IMPACT Enables more efficient and cost-effective use of LLMs for knowledge retrieval in local environments.

RANK_REASON The item describes a custom tool built by a developer to solve a specific problem, rather than a release from a major AI lab or a significant industry event.

Read on dev.to — LLM tag →

• FTS5
• ONNX
• SQLite

• infra
• product

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