chore: initialize project repository

app/infrastructure/__init__.py

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+"""Infrastructure adapters."""

app/infrastructure/db.py

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+from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
+
+from app.core.config import get_settings
+
+engine = create_async_engine(get_settings().mysql_dsn, pool_pre_ping=True)
+SessionFactory = async_sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)

app/infrastructure/memory_cache.py

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+from typing import Any, Protocol
+
+
+class CacheClient(Protocol):
+    async def get(self, key: str) -> Any: ...
+
+    async def set(self, key: str, value: str, ex: int | None = None) -> Any: ...
+
+
+class CacheReadResult:
+    def __init__(self, value: Any, degraded: bool = False) -> None:
+        self.value = value
+        self.degraded = degraded
+
+
+class MemoryCacheAdapter:
+    """Cache is an optimization; failures must not block MySQL recall."""
+
+    def __init__(self, client: CacheClient) -> None:
+        self.client = client
+
+    async def get(self, key: str) -> CacheReadResult:
+        try:
+            return CacheReadResult(await self.client.get(key))
+        except Exception:
+            return CacheReadResult(None, degraded=True)
+
+    async def set(self, key: str, value: str, ttl_seconds: int = 300) -> bool:
+        try:
+            await self.client.set(key, value, ex=ttl_seconds)
+            return True
+        except Exception:
+            return False

app/infrastructure/vector_memory.py

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+from typing import Any, Protocol
+
+
+class VectorClient(Protocol):
+    def search(self, collection_name: str, data: list[list[float]], limit: int) -> Any: ...
+
+
+class VectorSearchResult:
+    def __init__(self, hits: Any, degraded: bool = False) -> None:
+        self.hits = hits
+        self.degraded = degraded
+
+
+class VectorMemoryAdapter:
+    def __init__(self, client: VectorClient, collection: str) -> None:
+        self.client = client
+        self.collection = collection
+
+    def search(self, embedding: list[float], limit: int = 10) -> VectorSearchResult:
+        try:
+            hits = self.client.search(
+                collection_name=self.collection,
+                data=[embedding],
+                limit=max(1, min(limit, 100)),
+            )
+            return VectorSearchResult(hits)
+        except Exception:
+            return VectorSearchResult([], degraded=True)