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>
Freeze interfaces between lanes A (bot), B (backend), C (dashboard) so three developers work in
parallel without touching each other's files: dialog Event/Reply + engine registry with echo stub,
integration protocols with in-memory defaults, MonitoringService with noop stub, /api/v1 schemas
with fixture-backed routes, frozen main.py wiring, config/modules.yaml + due_scales(), DAR-5,
tzdata, Andrey trajectory, lanes in WORKFLOW, ADR-010.
Refs #34
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>
Rule-based PII scrubber: phones, e-mails, SNILS (checksum-aware), passport,
INN, cards, dates of birth, addresses, URLs/handles and person names
(dictionary in config/privacy/names_ru.yaml + declension, patronymic and
surname heuristics). Idempotent via fixed-point iteration; false-positive
guards for ordinary clinical text. pseudo_id normalises SNILS to 11 digits
and returns HMAC-SHA256 truncated to 32 hex chars.
Refs #2
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Two DeclarativeBases on two engines (ADR-003): identity holds direct
identifiers, clinical holds token-keyed pseudonymised data plus audit_log.
Portable types (JSONB variant, tz-aware UTC timestamps rejecting naive
values) so tests run on SQLite. init_db() runs create_all at startup.
Guardrail #5 checked by AST and import-graph tests.
Refs #6
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Pydantic schema for config/scales/*.yaml, cached loader, score() with
severity bands (sex-specific for AUDIT-C), rule-based C-SSRS triage,
flags (PHQ-9 item 9, PCL-5 >= 33, PHQ-4 subscales) and delta().
Starting literature thresholds for phq9, gad7, pcl5, auditc, pcptsd5,
cssrs_screen, phq4; final values are approved by the psychiatrist (#8).
Refs #3
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>
Pavel Eliseev
Eleboba