584 lines
27 KiB
Python
584 lines
27 KiB
Python
"""The new RAG orchestrator — LLM understanding in, grounded answer out.
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Replaces the old front-end wholesale:
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- `CatalogDrugResolver` (fuzzy) + `SectionResolver` (keyword) -> `understanding.py`
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- `ConversationalLoopService` + `conversation.py` (Focus / Summariser / manual
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follow-up inheritance) -> the LLM reads a plain turn history and resolves
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"thuốc đó" / "còn liều thì sao" itself.
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What is deliberately KEPT because it is the safety spine, not the brittle part:
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- `RetrievalService.retrieve_framed` (Qdrant section/overview retrieval, whole
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section, provenance, quarantine `VERIFY_PDF`),
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- `GroundedAnswerService.answer_from_result` (`grounding.verify` + entailment
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on every generated claim; a configured generator that fails abstains rather
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than degrading to a raw source dump).
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This module owns routing only; it states no medical fact of its own.
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"""
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from __future__ import annotations
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import logging
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import time
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from dataclasses import dataclass, replace
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from typing import Protocol
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from .answer import AnswerBlock, AnswerPlan, Citation, GroundedAnswerService
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from .budget import RequestBudget
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from .models import EvidenceDecision, RetrievalResult
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from .policy import looks_non_human
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from .service import RetrievalService
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from .understanding import QueryFrame, QueryUnderstander
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logger = logging.getLogger(__name__)
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TUONG_TAC = "tuong_tac_thuoc"
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HISTORY_TURNS = 6
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# F-08: a normal answerable agent turn makes 3 sequential Bedrock calls
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# (understand, generate, entailment). Input-field sufficiency is enforced by
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# the structured state machine; generation still fails closed when retrieved
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# evidence is insufficient. Defaults are sized with headroom, so ordinary
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# traffic never trips the budget — it exists for the pathological case
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# (a stuck/slow call, or an unexpectedly long retry chain), not to shave
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# time off the common path.
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MAX_WALL_CLOCK_MS = 40_000
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MAX_LLM_CALLS_PER_TURN = 8
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# Found live 2026-08-07 (50-question hand-typed browser audit): the
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# understanding LLM can get stuck re-asking the same (or a near-identical)
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# clarify question forever — reproduced 3 times independently, one case
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# never converged after 5 real answered turns. `understanding.py`'s prior-
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# frame merge (F-11) fixes most of the underlying cause, but a code-level
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# backstop is still needed: nothing bounds a model that just keeps deciding
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# needs_clarify=true no matter what. After this many CONSECUTIVE clarify
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# turns on the same conversation, force a hard stop instead of asking again.
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MAX_CONSECUTIVE_CLARIFY = 4
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class ConversationStore(Protocol):
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"""Durable, cross-worker alternative to the in-process history dict —
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the gap ADR 0008 names as still open. Satisfied by
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`adapters.postgres.PostgresConversationStore`; entirely optional — with
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none configured, `RagAgent` behaves exactly as before this existed."""
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def recent(self, conversation_id: str, limit: int) -> list[str]: ...
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def append(self, conversation_id: str, line: str) -> None: ...
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class AutocompleteSource(Protocol):
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"""As-you-type suggestion, kept deterministic and independent of the LLM
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understander — a prefix match needs no model call."""
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def complete(self, prefix: str, k: int) -> list[str]: ...
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@dataclass(frozen=True)
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class AgentReply:
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decision: str # answerable | abstain | clarify | verify_pdf
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reason: str
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answer: str | None = None
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clarification: str | None = None
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citations: tuple[Citation, ...] = ()
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drugs: tuple[str, ...] = ()
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turn_type: str = ""
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generated: bool = False
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quick_replies: tuple[str, ...] = ()
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blocks: tuple[AnswerBlock, ...] = ()
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answer_mode: str = "concise"
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plan: AnswerPlan | None = None
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class RagAgent:
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def __init__(
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self,
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understander: QueryUnderstander,
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retrieval: RetrievalService,
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answers: GroundedAnswerService,
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autocomplete: AutocompleteSource | None = None,
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history_turns: int = HISTORY_TURNS,
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max_wall_clock_ms: int = MAX_WALL_CLOCK_MS,
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max_llm_calls_per_turn: int = MAX_LLM_CALLS_PER_TURN,
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store: ConversationStore | None = None,
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) -> None:
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self._understander = understander
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self._retrieval = retrieval
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self._answers = answers
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self._autocomplete = autocomplete
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self._history_turns = history_turns
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self._max_wall_clock_ms = max_wall_clock_ms
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self._max_llm_calls_per_turn = max_llm_calls_per_turn
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self._store = store
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self._history: dict[str, list[str]] = {}
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# In-process only, same durability caveat as `_history` (ADR 0008's
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# named gap) — lost on restart, not shared across workers.
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self._last_frame: dict[str, QueryFrame] = {}
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self._clarify_streak: dict[str, int] = {}
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def complete(self, prefix: str, k: int = 8) -> list[str]:
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"""Display names matching a typed prefix, for input autocomplete."""
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if self._autocomplete is None:
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return []
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return [_display_name(drug_id) for drug_id in self._autocomplete.complete(prefix, k)]
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def handle(self, turn: str, conversation_id: str | None = None) -> AgentReply:
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# F-08: one budget per turn, threaded through every LLM call this
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# turn makes (understand, then whatever `_route` reaches).
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t0 = time.monotonic()
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budget = RequestBudget.start(self._max_wall_clock_ms, self._max_llm_calls_per_turn)
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history = self._get_history(conversation_id)
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t1 = time.monotonic()
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prior_frame = self._last_frame.get(conversation_id) if conversation_id else None
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frame = self._understander.understand(
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turn, tuple(history), budget=budget, prior_frame=prior_frame
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)
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t2 = time.monotonic()
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reply = self._route(turn, frame, budget)
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reply = self._enforce_clarify_circuit_breaker(conversation_id, reply)
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t3 = time.monotonic()
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if conversation_id is not None:
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self._remember(conversation_id, turn, reply)
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# A clarify can originate downstream of understanding (the dose
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# route invariant or evidence sufficiency). Persist that as an
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# open frame too; otherwise the next short reply sees a prior
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# frame marked complete and the structured merge cannot inherit
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# the drug/population the user already supplied.
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remembered_frame = frame
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if reply.decision == "clarify" and reply.clarification:
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remembered_frame = replace(
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frame,
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needs_clarify=True,
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clarify_reason=reply.clarification,
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quick_replies=reply.quick_replies,
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)
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self._last_frame[conversation_id] = remembered_frame
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t4 = time.monotonic()
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# Temporary instrumentation (2026-08-07): added specifically to
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# pinpoint a live, reproduced-in-browser case of the FIRST LLM call
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# already reporting the F-08 budget exhausted — i.e. >20s elapsed
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# before even one Bedrock call was attempted, with no code between
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# `RequestBudget.start()` and that first `budget.require()` that
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# should plausibly take anywhere near that long. Logs unconditionally
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# (not just on the slow path) so a normal turn's timing is on record
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# too, for comparison.
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# .warning, not .info: uvicorn's default logging config only wires
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# handlers onto its own "uvicorn"/"uvicorn.access" loggers, not the
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# root logger, so a plain .info() here would silently go nowhere —
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# confirmed by `understanding.py`'s existing warning-level log
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# already showing up in the same server output this session.
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logger.warning(
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"handle() timing: history=%.2fs understand=%.2fs route=%.2fs "
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"remember=%.2fs total=%.2fs",
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t1 - t0, t2 - t1, t3 - t2, t4 - t3, t4 - t0,
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)
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return reply
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def _enforce_clarify_circuit_breaker(
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self, conversation_id: str | None, reply: AgentReply
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) -> AgentReply:
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"""Hard stop after `MAX_CONSECUTIVE_CLARIFY` clarify turns in a row.
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Every other failure mode in this file degrades to a bounded, honest
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abstain — this is the one path that previously had no bound at all:
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a model that keeps deciding needs_clarify=true has no natural exit,
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and the user has no way out except abandoning the conversation. Any
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non-clarify decision (answered, or a different abstain reason) resets
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the streak — this only fires on genuinely consecutive clarifies.
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"""
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if conversation_id is None:
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return reply
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if reply.decision != "clarify":
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self._clarify_streak.pop(conversation_id, None)
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return reply
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streak = self._clarify_streak.get(conversation_id, 0) + 1
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if streak >= MAX_CONSECUTIVE_CLARIFY:
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self._clarify_streak.pop(conversation_id, None)
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return AgentReply(
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"abstain", "clarify_loop_exhausted",
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answer=(
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"Hệ thống chưa xác định đủ thông tin sau nhiều lần hỏi lại. "
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"Anh/chị vui lòng gõ lại TOÀN BỘ câu hỏi trong một tin nhắn "
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"đầy đủ (tên thuốc, đối tượng, cân nặng/tuổi nếu có, đường "
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"dùng), hoặc bấm \"Tạo phiên tra cứu mới\" để bắt đầu lại."
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),
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turn_type=reply.turn_type,
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)
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self._clarify_streak[conversation_id] = streak
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return reply
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def _get_history(self, conversation_id: str | None) -> list[str]:
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if conversation_id is None:
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return []
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if self._store is not None:
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try:
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return self._store.recent(conversation_id, self._history_turns * 2)
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except Exception:
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# Fail-open (F-09's precedent): a store outage means this
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# turn is understood fresh, with no memory of earlier ones —
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# worse UX, not a 500. `routers/rag.py` wraps
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# `PostgresTraceRepository.save()` the same bare way for the
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# same reason.
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return []
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return self._history.get(conversation_id, [])
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def _route(self, turn: str, frame: QueryFrame, budget: RequestBudget) -> AgentReply:
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tt = frame.turn_type
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section_overview = _is_section_overview(turn, frame)
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if section_overview and not frame.section_overview:
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frame = replace(frame, section_overview=True)
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# Deterministic scope guard precedes every conversational clarify. A
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# non-human dose must abstain, never ask which attribute/route and make
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# an out-of-scope request look recoverable.
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if looks_non_human(turn):
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return AgentReply(
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"abstain", "out_of_scope",
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answer="Nội dung này nằm ngoài phần chuyên luận thuốc của Dược thư "
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"(có thể thuộc phần hướng dẫn chung/phụ lục chưa được đưa vào). "
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"Tôi chưa có dữ liệu để trả lời chính xác.",
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turn_type=tt)
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# Dosing is a small state machine, not an unconstrained model opinion.
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# The LLM extracts the fields and can phrase/populate a useful initial
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# clarify; code decides which core fields are actually required. Live
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# testing caught the model asking an adult's weight repeatedly even
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# after the user supplied a route, while previously skipping route and
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# dumping oral + rectal regimens together.
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if tt == "dosing_calc" and frame.drugs and not section_overview:
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if frame.population is None:
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return AgentReply(
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"clarify", "missing_population",
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clarification=(
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frame.clarify_reason
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if frame.needs_clarify and frame.clarify_reason
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else "Anh/chị muốn tra liều cho người lớn hay trẻ em?"
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),
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drugs=frame.drugs, turn_type=tt,
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quick_replies=(
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frame.quick_replies
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if frame.needs_clarify else ()
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),
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)
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if frame.population in {"tre_em", "tre_so_sinh"} and (
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frame.age_text is None or frame.weight_kg is None
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):
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return AgentReply(
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"clarify", "missing_pediatric_age_or_weight",
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clarification=(
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frame.clarify_reason
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if frame.needs_clarify and frame.clarify_reason
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else "Bé bao nhiêu tuổi và cân nặng bao nhiêu kg?"
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),
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drugs=frame.drugs, turn_type=tt,
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quick_replies=(
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frame.quick_replies
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if frame.needs_clarify else ()
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),
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)
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# Route is intentionally not a universal required slot. Retrieval
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# and the answer contract decide from the actual evidence whether
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# omitting it is harmless (one applicable route -> answer now) or
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# materially ambiguous (several routes -> model clarification and
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# model-proposed quick replies). This prevents chip funnels for a
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# question that was already precise enough to answer.
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if (
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frame.needs_clarify
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and frame.clarify_reason
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and tt != "dosing_calc"
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and not section_overview
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):
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# `system_error` set means this isn't a real clarify at all — the
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# understanding call itself failed (provider outage, malformed
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# output) and failed closed to this same shape. Surface the real
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# reason instead of the generic "needs_more_info" so a technical
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# failure is distinguishable from an ordinary question back, both
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# in the API response and in `/metrics`/traces (found live
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# 2026-08-07: these were indistinguishable, which is why a real
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# outage looked identical to normal clarify traffic).
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if frame.system_error:
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return AgentReply(
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"abstain", frame.system_error,
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answer=frame.clarify_reason,
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drugs=frame.drugs, turn_type=tt,
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)
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return AgentReply(
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"clarify", "needs_more_info",
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clarification=frame.clarify_reason,
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drugs=frame.drugs, turn_type=tt,
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quick_replies=frame.quick_replies,
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)
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if tt == "drug_attribute" and frame.drugs and frame.attribute is None:
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return AgentReply(
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"clarify", "missing_attribute",
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clarification=(
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"Anh/chị muốn tra nội dung nào của thuốc này "
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"(chỉ định, chống chỉ định, thận trọng, tác dụng phụ…)?"
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),
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drugs=frame.drugs, turn_type=tt,
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)
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if tt == "smalltalk":
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return AgentReply(
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"answerable", "smalltalk",
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answer="Chào anh/chị! Em là trợ lý tra cứu Dược thư Quốc gia Việt Nam "
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"2018, sẵn sàng hỗ trợ tra liều dùng, chống chỉ định, tương tác "
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"thuốc... Anh/chị đang cần tra thuốc nào ạ?",
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turn_type=tt)
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if tt == "out_of_scope":
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return AgentReply(
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"abstain", "out_of_scope",
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answer="Nội dung này nằm ngoài phần chuyên luận thuốc của Dược thư "
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"(có thể thuộc phần hướng dẫn chung/phụ lục chưa được đưa vào). "
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"Tôi chưa có dữ liệu để trả lời chính xác.",
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turn_type=tt)
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if not frame.drugs:
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if frame.unknown_drugs:
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names = ", ".join(frame.unknown_drugs)
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return AgentReply(
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"abstain", "drug_not_in_formulary",
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answer=f"Không tìm thấy \"{names}\" trong Dược thư Quốc gia Việt Nam.",
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turn_type=tt)
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if tt == "symptom_to_drug":
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if frame.indication:
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return self._symptom_to_drug(turn, frame, budget)
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return AgentReply(
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"clarify", "no_indication",
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clarification="Anh/chị mô tả triệu chứng hoặc chỉ định cần tra giúp "
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"em với?",
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turn_type=tt)
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return AgentReply(
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"clarify", "no_drug",
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clarification="Anh/chị muốn tra thuốc nào?", turn_type=tt)
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if tt == "interaction" and len(frame.drugs) >= 2:
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return self._interaction(turn, frame, budget)
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# drug_attribute / drug_overview / dosing_calc / fallback: one drug + section
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return self._single_drug(turn, frame, budget)
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def _single_drug(self, turn: str, frame: QueryFrame, budget: RequestBudget) -> AgentReply:
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query = _synthesize_query(frame.standalone_query or turn, frame)
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# `dosing_calc` semantically names the dosage section even when the
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# understanding model leaves the separate `attribute` field null.
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# Passing null here falls into an overview retrieval and was observed
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# pulling interactions/precautions into a plain adult-dose answer.
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section_key = (
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"lieu_luong_va_cach_dung"
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if frame.turn_type == "dosing_calc"
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else frame.attribute
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)
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result = self._retrieval.retrieve_framed(
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frame.drugs[0], section_key, query,
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is_overview=frame.turn_type == "drug_overview",
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)
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return self._grounded(query, result, frame, budget=budget)
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def _interaction(self, turn: str, frame: QueryFrame, budget: RequestBudget) -> AgentReply:
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"""Gather the interaction section of each named drug and synthesise.
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Absence of a match is stated as "not found in each drug's interaction
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section", never as "safe" — the answer layer's grounding still applies.
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A quarantined table/formula in EITHER drug's interaction section
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forces the whole combined answer to VERIFY_PDF via
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`RetrievalService.decide` — the same policy the single-drug path
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already applies to a section with quarantined content. Previously
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this only kept `part.decision == ANSWERABLE` parts, so a quarantined
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drug's evidence (and the "table exists, verify PDF" notice it must
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produce per the quarantine contract) was silently dropped instead of
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surfaced; a confident interaction answer could omit exactly the
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unverified contraindication table it should have flagged. Generating
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a synthesis claim from one verified and one unverified source is not
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safer than generating from either alone, so both now block
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generation the same way.
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"""
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evidences = []
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query = _synthesize_query(frame.standalone_query or turn, frame)
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for drug_id in frame.drugs:
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part = self._retrieval.retrieve_framed(drug_id, TUONG_TAC, query)
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if part.decision in (EvidenceDecision.ANSWERABLE, EvidenceDecision.VERIFY_PDF):
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evidences.extend(part.evidence)
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if not evidences:
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listed = " và ".join(frame.drugs)
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return AgentReply(
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"abstain", "no_interaction_evidence",
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answer=f"Không tìm thấy mục tương tác thuốc cho {listed} trong Dược "
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"thư. Điều này KHÔNG có nghĩa là an toàn khi phối hợp.",
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drugs=frame.drugs, turn_type=frame.turn_type)
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combined = self._retrieval.decide(tuple(evidences))
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return self._grounded(query, combined, frame, budget=budget)
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def _symptom_to_drug(
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self, turn: str, frame: QueryFrame, budget: RequestBudget
|
|
) -> AgentReply:
|
|
"""Reverse lookup: a symptom/indication -> which drugs' `chi_dinh`
|
|
actually names it. A factual list from the formulary, not a
|
|
treatment ranking or recommendation — no drug is preferred over
|
|
another here, only cited as indicated ([[feedback_no_recommendation_gate]]:
|
|
this audience is doctors/pharmacists, a lookup like this is normal use).
|
|
Absence is stated plainly, never as "no such drug exists" — the
|
|
formulary may simply not name this indication under any monograph.
|
|
"""
|
|
result = self._retrieval.retrieve_by_indication(frame.indication)
|
|
if result.decision == EvidenceDecision.ABSTAIN:
|
|
return AgentReply(
|
|
"abstain", result.reason,
|
|
answer=f"Không tìm thấy thuốc nào trong Dược thư Quốc gia Việt Nam ghi "
|
|
f"nhận chỉ định cho \"{frame.indication}\". Điều này KHÔNG có "
|
|
"nghĩa là không có thuốc điều trị — vui lòng tra theo tên thuốc "
|
|
"cụ thể nếu đã biết.",
|
|
turn_type=frame.turn_type)
|
|
# `matched_doc_id` is always `{drug_id}__chi_dinh__{part_index}` — the
|
|
# drugs actually found, not `frame.drugs` (empty by construction for
|
|
# this turn_type; the router only reaches here with no named drug).
|
|
matched_drugs = tuple(dict.fromkeys(
|
|
evidence.matched_doc_id.split("__")[0] for evidence in result.evidence
|
|
))
|
|
return self._grounded(
|
|
turn, result, frame, drugs=matched_drugs, list_mode=True, budget=budget
|
|
)
|
|
|
|
def _grounded(
|
|
self, turn: str, result: RetrievalResult, frame: QueryFrame,
|
|
drugs: tuple[str, ...] | None = None, list_mode: bool = False,
|
|
budget: RequestBudget | None = None,
|
|
) -> AgentReply:
|
|
ga = self._answers.answer_from_result(
|
|
turn, result, list_mode=list_mode, budget=budget, prechecked=True
|
|
)
|
|
decision = ga.result.decision.value
|
|
if ga.clarification is not None:
|
|
decision = "clarify"
|
|
reason = "needs_more_info" if ga.clarification is not None else ga.result.reason
|
|
return AgentReply(
|
|
decision=decision,
|
|
reason=reason,
|
|
answer=ga.answer,
|
|
clarification=ga.clarification,
|
|
citations=ga.citations,
|
|
drugs=drugs if drugs is not None else frame.drugs,
|
|
turn_type=frame.turn_type,
|
|
generated=ga.generated,
|
|
quick_replies=ga.quick_replies,
|
|
blocks=ga.blocks,
|
|
answer_mode=ga.answer_mode,
|
|
plan=ga.plan,
|
|
)
|
|
|
|
def _remember(self, conversation_id: str, turn: str, reply: AgentReply) -> None:
|
|
lines = [f"Người dùng: {turn}"]
|
|
spoken = reply.answer or reply.clarification
|
|
if spoken:
|
|
lines.append(f"Trợ lý: {spoken[:300]}")
|
|
|
|
if self._store is not None:
|
|
try:
|
|
for line in lines:
|
|
self._store.append(conversation_id, line)
|
|
except Exception:
|
|
# Fail-open: this turn's memory is lost, not the response
|
|
# already computed and about to be returned to the caller.
|
|
pass
|
|
return
|
|
|
|
history = self._history.setdefault(conversation_id, [])
|
|
history.extend(lines)
|
|
# Keep only the recent window; the LLM re-reads it every turn. Only
|
|
# needed for the in-process dict — the store path windows at READ
|
|
# time instead (`recent(..., limit)`), so old rows just sit unused
|
|
# rather than needing a delete on every turn.
|
|
excess = len(history) - self._history_turns * 2
|
|
if excess > 0:
|
|
del history[:excess]
|
|
|
|
|
|
def _display_name(drug_id: str) -> str:
|
|
"""A readable display name from a drug id ('paracetamol_acetaminophen')."""
|
|
return drug_id.replace("_", " ").title()
|
|
|
|
|
|
def _is_section_overview(turn: str, frame: QueryFrame) -> bool:
|
|
"""Separate a handbook survey from a patient-specific decision.
|
|
|
|
The model supplies the first-class flag, while the narrow lexical
|
|
backstop makes a clear section lookup deterministic. A personal target
|
|
always wins: broad words inside a patient-specific dose question must not
|
|
suppress a necessary clarification.
|
|
"""
|
|
text = turn.casefold()
|
|
personal_cues = (
|
|
"cho tôi", "tôi đang", "bệnh nhân này", "ca này", "bé ", "trẻ ",
|
|
"tuổi", "cân nặng", " kg", "suy thận", "suy gan", "nên dùng liều nào",
|
|
)
|
|
if any(cue in text for cue in personal_cues):
|
|
return False
|
|
overview_cues = (
|
|
"dược thư hướng dẫn", "những ", "các ", "toàn bộ", "tất cả",
|
|
"trình bày", "theo nhóm", "theo tần suất", "nếu có", "tổng quan",
|
|
)
|
|
return frame.section_overview or any(cue in text for cue in overview_cues)
|
|
|
|
|
|
_POPULATION_LABELS = {
|
|
"tre_em": "trẻ em",
|
|
"tre_so_sinh": "trẻ sơ sinh",
|
|
"nguoi_lon": "người lớn",
|
|
"nguoi_cao_tuoi": "người cao tuổi",
|
|
"phu_nu_co_thai": "phụ nữ có thai",
|
|
"phu_nu_cho_con_bu": "phụ nữ cho con bú",
|
|
"suy_than": "suy thận",
|
|
"suy_gan": "suy gan",
|
|
}
|
|
|
|
_ROUTE_LABELS = {
|
|
"uong": "uống",
|
|
"tiem_tinh_mach": "tiêm tĩnh mạch",
|
|
"tiem_bap": "tiêm bắp",
|
|
"tiem_duoi_da": "tiêm dưới da",
|
|
"dat_truc_trang": "đặt trực tràng",
|
|
"boi_ngoai_da": "bôi ngoài da",
|
|
"nho_mat": "nhỏ mắt",
|
|
"nho_mui": "nhỏ mũi",
|
|
"khac": "khác",
|
|
}
|
|
|
|
|
|
def _synthesize_query(turn: str, frame: QueryFrame) -> str:
|
|
"""Fold the structured context `understanding.py` resolved — possibly
|
|
across several turns — into one self-contained question.
|
|
|
|
`GroundedAnswerService.answer_from_result` has no conversation history of
|
|
its own; the `query` string it receives IS the entire context its
|
|
generation LLM call sees (and the legacy sufficiency call, if enabled).
|
|
Passing the bare current
|
|
turn loses everything resolved earlier: a reply like "Uống" answering a
|
|
route question three turns into a dose conversation would reach
|
|
generation as just "Uống", indistinguishable from a user who typed
|
|
nothing else — the two P0s the 2026-08-06 audit named (population/
|
|
weight/age/route extracted but discarded downstream) are exactly this
|
|
gap. Redundant when the turn is already self-contained (a fresh
|
|
single-shot question re-states its own population/route, so this just
|
|
repeats it) — harmless, since omission is the failure mode, not
|
|
repetition.
|
|
"""
|
|
parts = [turn]
|
|
if frame.section_overview:
|
|
parts.append(
|
|
"Phạm vi yêu cầu: tra cứu tổng quan toàn mục; trình bày các nhánh "
|
|
"trong Dược thư với nhãn điều kiện, không chọn một phác đồ cho một người bệnh"
|
|
)
|
|
if frame.population:
|
|
parts.append(f"Đối tượng: {_POPULATION_LABELS.get(frame.population, frame.population)}")
|
|
if frame.age_text:
|
|
parts.append(f"Tuổi: {frame.age_text}")
|
|
if frame.weight_kg is not None:
|
|
parts.append(f"Cân nặng: {frame.weight_kg:g} kg")
|
|
if frame.route:
|
|
parts.append(f"Đường dùng: {_ROUTE_LABELS.get(frame.route, frame.route)}")
|
|
if frame.indication:
|
|
parts.append(f"Chỉ định/triệu chứng: {frame.indication}")
|
|
if len(parts) == 1:
|
|
return turn
|
|
return ". ".join(parts) + "."
|