Improving Local Techdocs for Your AI Coding Agent
The article discusses enhancing technical documentation for AI coding agents through a structured approach. It outlines a two-step classification process for pages, followed by embedding and building a knowledge graph. The goal is to filter out non-content pages and focus on useful information for AI applications.
- ▪The process begins with a rule-based classification to identify legal and navigation pages.
- ▪Pages that cannot be classified by rules are sent to a local LLM for further classification.
- ▪After classification, pages are embedded using a local sentence transformer model to facilitate faster processing.
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This is the third post in the series about making technical documentation available for use in your AI agent or knowledge base, based on our work on Morsel, a knowledge base that improves itself using AI agents. In the first post I described how we crawl documentation sites, clean the page content, and generate descriptions for images. In the second post I shared practical gotchas we ran into when crawling complete techdocs. Here I want to describe what we do afterwards to structure the crawled documentation further and make it available in a more useful form - for example, for your local AI coding agent. At a high level, we classify pages, embed them with a local model, and then build a knowledge graph that combines explicit hyperlinks with semantic similarity edges.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Philip Heltweg.
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