feat(bot): tool-using chat agent over RAG, ЕМИАС record and drug-safety reference

Free-text treatment and medication questions go to an LLM agent with read-only tools bound to the current
patient: guideline search (RAG, doses stripped), ЕМИАС record mock (diagnoses, comorbidities, intolerances,
other specialists' drugs, current prescription), drug info (composition, contraindications, interactions)
and a deterministic patient-specific check. Providers: OpenRouter (temporary, synthetic data only) and an
offline mock; GigaChat to follow. Scrubbed input only, forbidden-output filter, any failure falls back to
the existing extractive RAG / instruction answers. Off unless OPENROUTER_API_KEY or CHAT_AGENT=mock.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

app/dialog/flows/knowledge.py

@@ -21,7 +21,7 @@ from app.config import get_settings
 from app.dialog.contracts import Event, Reply
 from app.dialog.flows.core import menu_reply
 from app.dialog.fsm import Ctx, Step, abandonable, handler_for, state, trigger
-from app.llm import rag
+from app.llm import agent, rag
 from app.llm.med_qa import DOSE_QUESTION
 
 log = logging.getLogger(__name__)
@@ -74,6 +74,11 @@ async def knowledge_answer(ctx: Ctx, *, explicit: bool) -> str | None:
     if DOSE_QUESTION.search(ctx.text.lower()):
         log.info("kb: dose question redirected to doctor")
         return load_knowledge_texts().dose_redirect
+    # Tool-using agent first (RAG + ЕМИАС record + drug-safety check); disabled/failed → extractive RAG below.
+    smart = await agent.answer_chat(ctx.token, ctx.text)
+    if smart is not None:
+        log.info("kb: agent answered provider=%s tools=%s", smart.provider, ",".join(smart.tools))
+        return agent.render(smart)
     reply = await rag.answer(ctx.text, audience="patient")
     if reply is not None:
         log.info("kb: answered docs=%s", ",".join(c.doc for c in reply.citations))

app/dialog/flows/meds.py

@@ -42,7 +42,8 @@ from app.dialog.contracts import Button, Event, Reply
 from app.dialog.flows.core import menu_reply
 from app.dialog.fsm import Ctx, Step, abandonable, get_trigger, handler_for, state, trigger, trigger_payload
 from app.integrations.base import integrations
-from app.llm.med_qa import fallback_text, med_qa
+from app.llm import agent
+from app.llm.med_qa import DOSE_QUESTION, fallback_text, med_qa
 
 QUESTION_STATE = "meds.question"
 SIDE_EFFECT_TEXT_STATE = "meds.side_effect_text"
@@ -435,6 +436,11 @@ async def answer_question(ctx: Ctx, rx: Rx | None) -> str:
     """Answer from the instruction excerpt only (SPEC §7 п.5); dose questions and gaps → «уточните у врача»."""
     if rx is None or ctx.text is None:
         return load_meds_texts().question.no_prescription
+    if DOSE_QUESTION.search(ctx.text.lower()) is None:
+        # Agent: instruction + ЕМИАС record (other drugs, conditions, intolerances) + guidelines; else excerpt only.
+        smart = await agent.answer_chat(ctx.token, ctx.text)
+        if smart is not None:
+            return agent.render(smart)
     answer = await med_qa(rx.drug, ctx.text)
     return answer.text if answer is not None else fallback_text()