duocthu/docs/adr/0007-conversational-reasoning-rag.md

ADR 0007: Conversational reasoning RAG — state, bounded loop, and how it is measured

Status: accepted, implementation in progress (2026-08-05) Supersedes: nothing. Extends ADR 0005 (segment output contract) and ADR 0006 (quarantined block references) rather than replacing them.

Context

The service answers one question at a time. POST /v1/rag/query carries no conversation id, apps/chat-service holds zero source files, and every request re-resolves the drug from scratch. Three consequences, all observed in the UI on 2026-08-05:

• paracetamol alone is refused rather than asked about.
• liều dùng paracetamol cho người lớn returns the identical answer to liều dùng paracetamol — the qualifier is not used at any stage.
• A follow-up such as "còn trẻ em thì sao?" cannot work at all, because nothing carries the drug forward.

The owner's requirement is a conversational reasoning RAG: history, an internal reasoning stage, and a bounded self-improvement loop.

The binding constraint is that this is a drug formulary for clinicians. Every capability below is designed so that adding it cannot widen what the system is allowed to assert.

Decision

1. Conversation state

Two stores with different jobs, deliberately not merged.

Focus — structured, drives routing. This is what makes "còn trẻ em thì sao?" resolvable without an LLM.

Field	Purpose
drug_id, drug_name	The drug under discussion
section_key	The attribute last answered
population	nguoi_lon / tre_em / phu_nu_co_thai / …
verbosity	concise | detailed, set when the user asks
set_at_turn	Turn index each field was last set

ConversationState — the whole record.

conversation_id
recent: tuple[Turn, ...]     # last K turns, verbatim
summary: str                 # rolling prose summary of everything older
focus: Focus
turn_count: int

A Turn carries role, text, at, and — for assistant turns — the drug_id, section_key and evidence_ids that produced it. Storing the evidence ids is what lets the planner answer a follow-up from evidence already retrieved instead of retrieving again.

Carry-over is never silent. An inherited drug_id that is wrong is a wrong-drug answer, so any answer built on inherited focus must name what it inherited: "Về Metformin, ở trẻ em: …". This is a hard rule, not a presentation preference.

Focus expires. A field older than FOCUS_TTL_TURNS (6) is dropped rather than inherited. Conversations drift, and a drug from ten turns ago is not context, it is a hazard.

2. Recent history and summary

• recent holds the last K = 6 turns verbatim (three exchanges).
• When a turn falls out of recent, it is folded into summary.
• summary is regenerated at most every S = 4 turns, capped at 400 tokens; recent is capped at 2000 tokens, oldest dropped first.
• The summary records what was discussed, never clinical content. It may say "đã hỏi liều dùng của Metformin cho người lớn"; it may not carry a dose. A dose restated from a summary would have no citation and could not be grounding-verified — the check compares against retrieved evidence, and a summary is not evidence.

3. Reasoning loop

flowchart TD
    A[User turn] --> B[UNDERSTAND<br/>resolve against Focus]
    B --> C{Clarify signal?}
    C -->|ambiguous drug / no attribute /<br/>multi-attribute| Z[ASK — 1 turn, no loop]
    C -->|no| D{Simple?}
    D -->|drug + section resolved,<br/>no follow-up ambiguity| E[RETRIEVE]
    D -->|complex / decomposable| P[PLAN<br/>sub-questions + retrieval set]
    P --> E
    E --> F[ASSESS sufficiency]
    F -->|insufficient AND rounds left| R[REFINE query] --> E
    F -->|sufficient OR rounds exhausted| G[GENERATE]
    G --> H[VERIFY<br/>grounding + coverage]
    H -->|ungrounded / off-target,<br/>repairs left| G
    H -->|grounded| Y[RESPOND]
    H -->|repairs exhausted| X[FALL BACK<br/>verbatim source]
    F -->|exhausted AND still thin| Z

Continue conditions — a round is spent only when all hold:

1. retrieval_rounds < MAX_RETRIEVAL_ROUNDS (2)
2. the assessor named a specific missing thing (a section, a population, a second drug) — "feels incomplete" is not a reason to spend a round
3. the refined query differs from every query already tried this turn

Stop conditions — any one ends the loop:

• sufficiency satisfied
• budget exhausted (rounds, LLM calls, wall-clock, tokens)
• a clarify signal fires (these bypass the loop entirely — asking beats guessing)
• grounding verification fails after MAX_REPAIRS (1) → extractive fallback

Fast path. When the drug resolves and SectionResolver returns a section and no clarify signal fires, the loop is skipped: retrieve → generate → verify. This is the majority path and it costs one LLM call.

4. Budgets

Limit	Value	Enforced at
MAX_RETRIEVAL_ROUNDS	2	loop guard
MAX_REPAIRS	1	loop guard
MAX_LLM_CALLS per turn	4	budget object, checked before each call
MAX_WALL_CLOCK_MS	20000	checked between stages
MAX_EVIDENCE_TOKENS	12000	evidence assembly, oldest-dropped
FOCUS_TTL_TURNS	6	state update

The budget is a single object threaded through the loop and decremented before each call, so exhaustion degrades to the best answer so far rather than to an error.

5. Integration

New domain modules, no SDK imports:

• rag/conversation.py — Focus, Turn, ConversationState, window and focus-update rules. Pure; the follow-up resolution in it needs no LLM.
• rag/reasoning.py — the loop, its budget, and its stage protocols.
• rag/ports.py — ConversationStore (load/save), Summariser, Planner, SufficiencyAssessor. Each has a deterministic no-LLM default so the whole loop runs offline.

New adapter: adapters/postgres.py gains PostgresConversationStore.

Unchanged and still binding: GroundedAnswerService remains the single-turn engine; grounding.verify gates every generated answer; VERIFY_PDF evidence is never generated over.

6. Measurement

A capability that cannot be shown to help does not ship. Three modes are run over the same cases — single-shot, +history, +reasoning-loop:

Metric	Answers
follow-up resolution accuracy	does "còn trẻ em thì sao?" reach the right drug+section+population
on-target rate	does the answer contain the population/attribute actually asked for
grounding rejection rate	does reasoning make fabrication more or less likely
clarify rate / clarify precision	does it ask when it should, and only then
median + p95 latency, LLM calls, tokens per answered turn	what the capability costs

The evaluation set is a new multi-turn golden file — the existing golden_e2e_v1.csv is single-turn by construction and cannot measure any of this. Counters land in rag/metrics.py and on the existing Grafana dashboard.

Consequences

Accepted. More moving parts and more tokens per turn; a stateful service where there was a stateless one; a summary that must be kept free of clinical content by rule rather than by mechanism.

Refused. An LLM confidence score as the loop's uncertainty signal. The signals used are the resolver states that already exist — ambiguous drug, unresolved section, multi-attribute question — because they are deterministic, testable, and explainable to a reviewer. "The model felt 0.73 sure" is not a defensible basis for asking or not asking a clinician a question.

Unchanged. Nothing here lets the system assert a figure absent from the retrieved source. Reasoning chooses what to look up and how to say it; it is not a source of facts.