feat(rag): Implement RAG engine, configuration schema, and vector store integration

src/models.py

@@ -596,6 +596,57 @@ class MCPConfiguration:
   }
   return cls(mcpServers=parsed_servers)
 
+@dataclass
+class VectorStoreConfig:
+ provider: str # 'chroma', 'qdrant', 'mock'
+ url: Optional[str] = None
+ api_key: Optional[str] = None
+ collection_name: str = 'manual_slop'
+
+ def to_dict(self) -> Dict[str, Any]:
+  return {
+   "provider": self.provider,
+   "url": self.url,
+   "api_key": self.api_key,
+   "collection_name": self.collection_name,
+  }
+
+ @classmethod
+ def from_dict(cls, data: Dict[str, Any]) -> "VectorStoreConfig":
+  return cls(
+   provider=data["provider"],
+   url=data.get("url"),
+   api_key=data.get("api_key"),
+   collection_name=data.get("collection_name", "manual_slop"),
+  )
+
+@dataclass
+class RAGConfig:
+ enabled: bool = False
+ vector_store: VectorStoreConfig = field(default_factory=lambda: VectorStoreConfig(provider='mock'))
+ embedding_provider: str = 'gemini'
+ chunk_size: int = 1000
+ chunk_overlap: int = 200
+
+ def to_dict(self) -> Dict[str, Any]:
+  return {
+   "enabled": self.enabled,
+   "vector_store": self.vector_store.to_dict(),
+   "embedding_provider": self.embedding_provider,
+   "chunk_size": self.chunk_size,
+   "chunk_overlap": self.chunk_overlap,
+  }
+
+ @classmethod
+ def from_dict(cls, data: Dict[str, Any]) -> "RAGConfig":
+  return cls(
+   enabled=data.get("enabled", False),
+   vector_store=VectorStoreConfig.from_dict(data.get("vector_store", {"provider": "mock"})),
+   embedding_provider=data.get("embedding_provider", "gemini"),
+   chunk_size=data.get("chunk_size", 1000),
+   chunk_overlap=data.get("chunk_overlap", 200),
+  )
+
 def load_mcp_config(path: str) -> MCPConfiguration:
  if not os.path.exists(path):
   return MCPConfiguration()