dotdotgod Query Uses Local AI Embeddings to Match Questions to Relevant Docs

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dotdotgod's query tool lets software agents find relevant documentation passages by semantic meaning rather than keyword or filename matching, bridging gaps when a user's language differs from document terminology. The tool searches Markdown files under a docs/ directory, applying exclusion policies that omit active plans and archived records from its default retrieval scope. Documents are split along their heading hierarchy into fragments of up to 1,600 characters, each tagged with its file path and heading context to aid interpretation. Retrieval runs entirely on-device using the Xenova/multilingual-e5-small model via Hugging Face Transformers, meaning document content is never sent to an external API. Similarity between a question and all document passages is measured using cosine distance on 384-dimensional vectors, with results ranked and capped by a configurable file-level limit.

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