Daily check-in (mood → dose → sleep/nightmares → alcohol → free text with
extract_checkin, buttons win over the LLM, progressive upsert of
daily_checkins), medication reminder flow (taken / skip with reason /
side effects → doses + daily_checkins), medication question menu via
med_qa, med_info rule reactions from the drug instruction excerpt,
«записаться раньше» request recorded for lane B, friendly intros for
scheduled scales. actions.py: additive med_info handler hook.
Refs #37
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Start screening of the veteran module: peer-tone intro, trauma exposure gate for
PC-PTSD-5, PCL-5/PHQ-9/AUDIT-C via scale sessions (sex asked if unknown), weapon
access question stored as facts for lane B with a safe-storage block on risk,
plain-language result, modules enabled in profile, FAQ, resume after a crisis.
Refs #36
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Deep-link /start <code> binds the chat via IdentityProvider, text and
button presses go to get_engine().handle(), replies render as inline
keyboards and are sent verbatim (no parse_mode). TelegramOutbox delivers
scheduler pushes to bound chats and falls back to the previous outbox.
No-op without TG_BOT_TOKEN; logs carry no text or chat ids.
Refs #11
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01DfbiLXFJswz2EK3qtWF7S7
build_structured() aggregates a PatientCard into scores, flags and
adherence (no patient text, no names); build_summary() sends it through
the LLM gateway, falls back to a config-driven template, keeps an open
acute alert first and caches per (token, provider, data hash).
Refs #19
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Topic from engine or keyword classifier, section via integrations.drug_info,
sentence retrieval by keyword overlap (dosing sentences dropped), LLM only
rephrases the excerpt through gateway.phrase("med_answer") with forbidden-output
and added-content guards; dose questions and missing answers -> None + fallback text.
Refs #15
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Single entry point app/llm/gateway.py: extract_item, extract_checkin,
risk_classify, phrase, summarize_for_doctor. Two-level design:
LLMProvider (transport, GigaChat) and LLMTasks (PromptedTasks over a
provider, or rule-based MockTasks). Shared policy in the gateway:
assert_scrubbed backstop, confidence threshold 0.5, risk failure -> NONE,
forbidden-output filter with config/texts/phrases.yaml fallbacks.
Refs #7
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
One Postgres with isolated identity/clinical databases and roles (ADR-003),
FastAPI /health, Streamlit placeholder, typed settings with offline defaults.
Refs #1
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
ElebobaandClaude Opus 5.5