feat(api): Add image multimodal support for LLMNode (#17372)

Enhance `LLMNode` with multimodal capability, introducing support for
image outputs.

This implementation extracts base64-encoded images from LLM responses,
saves them to the storage service, and records the file metadata in the
`ToolFile` table. In conversations, these images are rendered as
markdown-based inline images.
Additionally, the images are included in the LLMNode's output as
file variables, enabling subsequent nodes in the workflow to utilize them.

To integrate file outputs into workflows, adjustments to the frontend code
are necessary.

For multimodal output functionality, updates to related model configurations
are required. Currently, this capability has been applied exclusively to
Google's Gemini models.

Close #15814.

Signed-off-by: -LAN- <laipz8200@outlook.com>
Co-authored-by: -LAN- <laipz8200@outlook.com>

api/core/file/tool_file_parser.py

@@ -1,12 +1,19 @@
-from typing import TYPE_CHECKING, Any, cast
+from collections.abc import Callable
+from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
     from core.tools.tool_file_manager import ToolFileManager
 
-tool_file_manager: dict[str, Any] = {"manager": None}
+_tool_file_manager_factory: Callable[[], "ToolFileManager"] | None = None
 
 
 class ToolFileParser:
     @staticmethod
     def get_tool_file_manager() -> "ToolFileManager":
-        return cast("ToolFileManager", tool_file_manager["manager"])
+        assert _tool_file_manager_factory is not None
+        return _tool_file_manager_factory()
+
+
+def set_tool_file_manager_factory(factory: Callable[[], "ToolFileManager"]) -> None:
+    global _tool_file_manager_factory
+    _tool_file_manager_factory = factory