- chat-adapter pre-fetches retrieval on every turn when RAG is on,
regardless of provider. Users no longer have to phrase queries as
'the document I attached' for retrieval to fire. Local tool models
still get search_knowledge_base registered as a refinement path.
- New per-thread ragMinScore slider (Min relevance, 0..1) gates
retrieved hits by dense cosine similarity. Hits below the floor
(and BM25-only hits with no dense signal) are dropped server-side
so unrelated docs don't get injected when the user's query is
off-topic from what's indexed.
- Backend logs at search start (scope, top_k, min_score, query
preview), after retrieval (retrieved vs met_threshold counts),
and on return (final hit count) for both /api/rag/search and the
search_knowledge_base tool path.
- System-prompt nudge prepended when pre-fetch returns hits so the
model knows to cite [1], [2] rather than paraphrase silently.
Promotes RAG to a first-class composer toggle alongside Think / Web
Search / Code, with tool-use semantics on local models that support
tools and a pre-fetch fallback on external providers. The model
decides when to call `search_knowledge_base` on local inference; on
external providers retrieval still fires before each message (the
existing pre-fetch path), gated on the same button.
Backend
- core/rag/tool.py (new): search_knowledge_base handler + JSON-schema
tool spec. Resolves scope (kb_id wins over thread_id) from the
request's rag_scope, runs retrieve_hybrid + optional rerank, then
hydrates filename / page_number / text from sqlite and formats as
numbered Markdown citations ('[1] file.pdf (page 5): ...') for the
LLM to cite. Empty scope returns a user-facing hint; empty results
return a clear no-match message instead of an empty string.
- core/inference/tools.py: SEARCH_KNOWLEDGE_BASE_TOOL added to
ALL_TOOLS (lazy import keeps tools.py importable on inference
paths that never touch RAG). execute_tool() gains a tool_context
parameter that carries per-request extras the LLM doesn't see
(currently just rag_scope). The new 'search_knowledge_base' branch
dispatches to the handler with scope unpacked from tool_context.
- core/inference/llama_cpp.py + safetensors_agentic.py +
orchestrator.py: thread tool_context through generate_chat_completion_
with_tools / run_safetensors_tool_loop / execute_tool. Both local
backends (GGUF llama-server and safetensors agentic) carry the same
context object.
- models/inference.py: ChatCompletionRequest gains optional
rag_scope: dict ({kb_id?, thread_id?, enable_rerank?, default_top_k?,
reranker_model?}). Ignored unless 'search_knowledge_base' is in
enabled_tools.
- routes/inference.py: both the GGUF and safetensors call sites for
generate_chat_completion_with_tools forward payload.rag_scope into
tool_context.
Frontend
- chat-runtime-store.ts: global ragToolEnabled boolean + setter +
CHAT_RAG_TOOL_ENABLED_KEY localStorage, mirroring toolsEnabled /
codeToolsEnabled. Settings-hydration migration auto-flips
ragToolEnabled=true for pre-Phase-4 users who already had ragSource
set, so existing RAG users don't silently lose retrieval on upgrade.
- shared-composer.tsx: new 'RAG' pill button after Images (uses
lucide BookOpenIcon, composer-pill-btn style, data-active toggle).
Disabled when no model is loaded. Toggling on from ragSource='off'
auto-flips source to 'thread' so the sidebar lands ready-to-go.
- chat-adapter.ts:
* The existing pre-fetch block is now gated on ragToolEnabled AND
only fires when the tool path isn't viable (external provider OR
local model without tool-use support). Tool-capable local models
skip pre-fetch and let the LLM decide.
* The local-model body assembly adds 'search_knowledge_base' to
enabled_tools and packs ragSource + enableRerank + ragTopK into a
rag_scope object the backend tool handler consumes.
- chat-settings-sheet.tsx: entire Retrieval CollapsibleSection is
wrapped in {ragToolEnabled && ...} so it hides when the button is
off — the button is now the single on/off control. The 'Off'
option is removed from the Source dropdown (the button handles
that). Default open when shown so settings are one click away.
Tests
- test_rag_tool_handler.py: handler covers empty query, missing
scope, kb_id > thread_id precedence, thread-only path, citation
formatting (numbered + page numbers + unknown source); tool spec
shape (function/name/required); execute_tool dispatch with and
without tool_context; ALL_TOOLS includes the new spec without
dropping the existing ones.
Verification scope
- Local GGUF with tools: toggle button on, upload doc, ask about
doc content → assistant emits a search_knowledge_base tool call
card (rendered by the existing ToolFallback component since no
custom UI exists yet — that's a v2 nice-to-have).
- External provider (Anthropic / OpenAI / etc.): same button, same
UX, but uses the pre-fetch path under the hood.
- Migration: pre-existing ragSource != off → button initializes ON
so retrieval keeps working.
