feat(bot): LLM gateway with GigaChat and offline mock providers

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>

app/llm/texts.py

@@ -0,0 +1,67 @@
+"""Template texts from `config/texts/phrases.yaml`: base meaning for `phrase`, mock output, fallbacks."""
+
+from collections.abc import Mapping
+from functools import lru_cache
+from pathlib import Path
+from typing import Any
+
+import yaml
+from pydantic import BaseModel, Field
+
+from app.config import get_settings
+
+DEFAULT_INTENT = "default"
+
+
+class SummaryTexts(BaseModel):
+    empty: str
+    labels: dict[str, str] = Field(default_factory=dict)
+
+
+class PhraseTexts(BaseModel):
+    phrases: dict[str, str]
+    summary: SummaryTexts
+
+
+class _BlankMissing(dict[str, Any]):
+    def __missing__(self, key: str) -> str:
+        return ""
+
+
+@lru_cache
+def load_texts(path: Path | None = None) -> PhraseTexts:
+    path = path or get_settings().config_dir / "texts" / "phrases.yaml"
+    texts = PhraseTexts.model_validate(yaml.safe_load(path.read_text(encoding="utf-8")))
+    if DEFAULT_INTENT not in texts.phrases:
+        raise ValueError(f"{path}: phrases.{DEFAULT_INTENT} is required")
+    return texts
+
+
+def render_phrase(intent: str, context: Mapping[str, Any]) -> str:
+    phrases = load_texts().phrases
+    template = phrases.get(intent, phrases[DEFAULT_INTENT])
+    return " ".join(template.format_map(_BlankMissing(context)).split())
+
+
+def _fmt(value: Any) -> str:
+    if isinstance(value, bool):
+        return "да" if value else "нет"
+    if isinstance(value, list | tuple):
+        sep = " → " if all(isinstance(v, int | float) for v in value) else ", "
+        return sep.join(_fmt(v) for v in value)
+    if isinstance(value, Mapping):
+        return ", ".join(f"{k}: {_fmt(v)}" for k, v in value.items())
+    return str(value)
+
+
+def render_summary(structured: Mapping[str, Any]) -> str:
+    """Deterministic doctor summary: one "label: value" line per non-empty field, known labels first."""
+    texts = load_texts().summary
+    labels = texts.labels
+    ordered = [k for k in labels if k in structured] + [k for k in structured if k not in labels]
+    lines = [
+        f"{labels.get(key, key)}: {_fmt(structured[key])}"
+        for key in ordered
+        if structured[key] not in (None, "", [], {})
+    ]
+    return "\n".join(lines) if lines else texts.empty