External providers (OpenAI/Anthropic/Gemini) can't run the local
search_knowledge_base tool loop, so give them RAG by prefetching:
studio retrieves before calling the provider, injects the chunks into
the user prompt, and surfaces it as a synthetic tool call. Local models
are untouched (they keep tool-based RAG + decomposition).
Backend:
- New POST /api/rag/prefetch: momentarily loads the pre-cached helper
(gemma-4-E2B-it-GGUF) via LlamaCppBackend(kill_orphans=False) to
decompose the question into up to 3 queries, retrieves+merges+dedups
per query, unloads the helper. Raw single-query fallback if the helper
can't load. New core/rag/query_decompose.py owns the helper lifecycle.
- Factored the retrieval body of /search into _execute_search, reused by
both endpoints.
Frontend:
- prefetchRag() client.
- chat-adapter external branch: gated on isExternalRequest + ragToolEnabled
+ scope!=off + ragScopeHasDocs (no docs -> no prefetch, prior behavior
preserved). Formats hits as <chunk id=N> (parseChunks shape), injects
into the last user message (send-only; not shown in the user bubble),
seeds a synthetic search_knowledge_base tool-call part so the existing
chunk-card UI + [N] citations + source badges all work unchanged.
- Extends PR #5674's disabled-tool guard: when RAG is off, reinforce
'no document search (RAG) capabilities'; when prefetch ran, point the
model at the injected excerpts instead.
- RAG pill enabled for external providers regardless of supports_tools.
Not build/UI verified here (no bun/GPU/keys); needs bun typecheck+test
and a browser round-trip with real provider keys.
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