Add system memory monitoring, Bedrock support, and Discord integrations

- Add CPU/GPU memory monitoring endpoint and frontend widget
- Add Amazon Bedrock model support (API key / inference profiles) plus model selector entries
- Add Discord weather/laundry notification job (JMA forecast + LLM-generated message)
- Add Discord bot that replies to mentions via the configured LLM, with weather-aware context
- Add scheduled "useless tips" job with separate webhook and quiet-hours skip
- Switch FastAPI startup to lifespan handler, log viewer, markdown rendering, and toast UI additions

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/app/core/llm.py

@@ -1,29 +1,94 @@
-from typing import AsyncIterator
+import json
+import logging
+import time
+import traceback
+from typing import AsyncIterator, AsyncGenerator
+from contextlib import asynccontextmanager
+
 import litellm
-from app.core.config import DEFAULT_MODEL
+from app.core.config import DEFAULT_MODEL, DEBUG
 
 litellm.drop_params = True
+if DEBUG:
+    litellm._turn_on_debug()
+
+logger = logging.getLogger(__name__)
+
+
+@asynccontextmanager
+async def _log_context(
+    model: str,
+    messages: list[dict],
+    session_id: str | None = None,
+) -> AsyncGenerator[dict, None]:
+    """LLM呼び出しの前後でログデータを収集するコンテキストマネージャー。"""
+    log: dict = {
+        "session_id": session_id,
+        "model": model,
+        "request_json": json.dumps(messages, ensure_ascii=False),
+        "response_text": None,
+        "error": None,
+        "latency_ms": None,
+    }
+    start = time.perf_counter()
+    try:
+        yield log
+    except Exception as e:
+        log["error"] = str(e)
+        log["latency_ms"] = round((time.perf_counter() - start) * 1000, 1)
+        logger.error("LLM call failed [model=%s]: %s\n%s", log["model"], e, traceback.format_exc())
+        raise
+    finally:
+        if log["latency_ms"] is None:
+            log["latency_ms"] = round((time.perf_counter() - start) * 1000, 1)
+
+
+def _save_log(log: dict) -> None:
+    from app.db.database import SessionLocal
+    from app.db.models import ApiLog
+    db = SessionLocal()
+    try:
+        db.add(ApiLog(**log))
+        db.commit()
+    finally:
+        db.close()
 
 
 async def stream_chat(
     messages: list[dict],
     model: str | None = None,
+    session_id: str | None = None,
 ) -> AsyncIterator[str]:
-    response = await litellm.acompletion(
-        model=model or DEFAULT_MODEL,
-        messages=messages,
-        stream=True,
-    )
-    async for chunk in response:
-        delta = chunk.choices[0].delta.content
-        if delta:
-            yield delta
+    used_model = model or DEFAULT_MODEL
+    async with _log_context(used_model, messages, session_id) as log:
+        response = await litellm.acompletion(
+            model=used_model,
+            messages=messages,
+            stream=True,
+        )
+        chunks: list[str] = []
+        async for chunk in response:
+            delta = chunk.choices[0].delta.content
+            if delta:
+                chunks.append(delta)
+                yield delta
+        log["response_text"] = "".join(chunks)
+    _save_log(log)
 
 
-async def complete(messages: list[dict], model: str | None = None) -> str:
-    response = await litellm.acompletion(
-        model=model or DEFAULT_MODEL,
-        messages=messages,
-        stream=False,
-    )
-    return response.choices[0].message.content or ""
+async def complete(
+    messages: list[dict],
+    model: str | None = None,
+    session_id: str | None = None,
+) -> str:
+    used_model = model or DEFAULT_MODEL
+    async with _log_context(used_model, messages, session_id) as log:
+        response = await litellm.acompletion(
+            model=used_model,
+            messages=messages,
+            stream=False,
+        )
+        result = response.choices[0].message.content or ""
+        log["response_text"] = result
+    _save_log(log)
+    return result