Currently, `WorkflowNodeExecution.execution_metadata_dict` returns `None` when metadata is absent in the database. This requires all callers to perform `None` checks when processing metadata, leading to more complex caller-side logic.
This pull request updates the `execution_metadata_dict` method to return an empty dictionary instead of `None` when metadata is absent. This change would simplify the caller logic, as it removes the need for explicit `None` checks and provides a more consistent data structure to work with.
Currently, `WorkflowNodeExecution.execution_metadata_dict` returns `None` when metadata is absent in the database. This requires all callers to perform `None` checks when processing metadata, leading to more complex caller-side logic.
This pull request updates the `execution_metadata_dict` method to return an empty dictionary instead of `None` when metadata is absent. This change would simplify the caller logic, as it removes the need for explicit `None` checks and provides a more consistent data structure to work with.
- Introduce `WorkflowDraftVariable` model and the corresponding migration.
- Implement `EnumText`, a custom column type for SQLAlchemy designed
to work seamlessly with enumeration classes based on `StrEnum`.
- Introduce `WorkflowDraftVariable` model and the corresponding migration.
- Implement `EnumText`, a custom column type for SQLAlchemy designed
to work seamlessly with enumeration classes based on `StrEnum`.
Alembic's offline mode generates SQL from SQLAlchemy migration operations,
providing developers with a clear view of database schema changes without
requiring an active database connection.
However, some migration versions (specifically bbadea11becb and d7999dfa4aae)
were performing database schema introspection, which fails in offline mode
since it requires an actual database connection.
This commit:
- Adds offline mode support by detecting context.is_offline_mode()
- Skips introspection steps when in offline mode
- Adds warning messages in SQL output to inform users that assumptions were made
- Prompts users to review the generated SQL for accuracy
These changes ensure migrations work consistently in both online and offline modes.
Close#19284.
Alembic's offline mode generates SQL from SQLAlchemy migration operations,
providing developers with a clear view of database schema changes without
requiring an active database connection.
However, some migration versions (specifically bbadea11becb and d7999dfa4aae)
were performing database schema introspection, which fails in offline mode
since it requires an actual database connection.
This commit:
- Adds offline mode support by detecting context.is_offline_mode()
- Skips introspection steps when in offline mode
- Adds warning messages in SQL output to inform users that assumptions were made
- Prompts users to review the generated SQL for accuracy
These changes ensure migrations work consistently in both online and offline modes.
Close#19284.
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>
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>
-LAN-