Feat/agent rag - #389
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Minimal grounded question-answering agent over the documentation corpus. - ingest.py resolves the cross-site Jinja macros, maps each page to its canonical docs.mat3ra.com URL, and chunks on H2 boundaries with a breadcrumb prefix (534 pages -> 2554 chunks). - agent.py runs in-process BM25 retrieval behind a search_docs tool and an agentic loop against claude-opus-4-6 on Vertex AI. The system prompt restricts answers to retrieved content and citations to URLs that came back from a tool result. The generated index (chunks.jsonl) is a build artifact and is git-ignored, as are the local virtualenv and bytecode. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Collects the planning documents for the documentation agent work: the RAG strategy (corpus, retrieval, evaluation) and the web delivery plan (architecture, hosting, repository layout, deployment). These are internal planning notes, not published documentation - the MkDocs builds only read lang/en/docs, so nothing here reaches the site. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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This was referenced Jul 31, 2026
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Superseded by #392 |
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