spec(M4): RAG Knowledge Base — contextual retrieval and multi-turn chat memory #8
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Reference: Circuit-Forge/robin#8
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Goal
Robin's M2 chat answers from the LLM's training data only. M4 adds two things: a curated migration knowledge base Robin can retrieve from, and multi-turn conversation memory so Robin remembers what you said earlier in the session.
Knowledge base content
The knowledge base is a set of structured documents covering:
Content is seeded from the real friction points Xander and Alan's partner encounter during their migrations — this is the primary acceptance test.
Technical approach
Same chunk/embed/retrieve pipeline as cf-core (shared with Turnstone). Documents are chunked, embedded locally, stored in a SQLite vector table. At chat time, the top-k chunks most relevant to the user's question are prepended to the system prompt.
All embedding runs locally — no data leaves the machine.
Multi-turn memory
M2 sends only the current message. M4 keeps a rolling window of the conversation (last N turns) so Robin can answer follow-up questions without losing context: "How do I install that?" after "What's the Arch equivalent of Homebrew?" should work correctly.
Relationship to issue #4
Linux-to-Linux migration paths (Debian/Fedora → Arch) land here. The knowledge base is where distro-specific translation content lives. See issue #4 for the full spec.
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