This article will briefly introduce common structures in plugin development. During development, it is strongly recommended to read this alongside Basic Concepts of Plugin Development and the Developer Cheatsheet for a better understanding of the overall architecture.

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Path Specifications

When filling in file paths in Manifest or any yaml files, follow these two specifications depending on the type of file:

  • If the target file is a multimedia file such as an image or video, for example when filling in the plugin’s icon, you should place these files in the _assets folder under the plugin’s root directory.
  • If the target file is a regular text file, such as .py or .yaml code files, you should fill in the absolute path of the file within the plugin project.

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Common Structures

When defining plugins, there are some data structures that can be shared between tools, models, and Endpoints. These shared structures are defined here.

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I18nObject

I18nObject is an internationalization structure that conforms to the IETF BCP 47 standard. Currently, four languages are supported:

  • en_US
  • zh_Hans
  • ja_JP
  • pt_BR

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ProviderConfig

ProviderConfig is a common provider form structure, applicable to both Tool and Endpoint

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ProviderConfigOption(object)

  • value (string, required): Value
  • label (object, required): Following IETF BCP 47

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ProviderConfigType(string)

  • secret-input (string): Configuration information will be encrypted
  • text-input (string): Plain text
  • select (string): Dropdown
  • boolean (bool): Switch
  • model-selector (object): Model configuration information, including provider name, model name, model parameters, etc.
  • app-selector (object): app id
  • tool-selector (object): Tool configuration information, including tool provider, name, parameters, etc.
  • dataset-selector (string): TBD

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ProviderConfigScope(string)

  • When type is model-selector
    • all
    • llm
    • text-embedding
    • rerank
    • tts
    • speech2text
    • moderation
    • vision
  • When type is app-selector
    • all
    • chat
    • workflow
    • completion
  • When type is tool-selector
    • all
    • plugin
    • api
    • workflow

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ModelConfig

  • provider (string): Model provider name containing plugin_id, in the form of langgenius/openai/openai.
  • model (string): Specific model name.
  • model_type (enum): Enumeration of model types, refer to the Model Design Rules document.

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NodeResponse

  • inputs (dict): Variables that are finally input to the node.
  • outputs (dict): Output results of the node.
  • process_data (dict): Data generated during node execution.

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ToolSelector

  • provider_id (string): Tool provider name
  • tool_name (string): Tool name
  • tool_description (string): Tool description
  • tool_configuration (dict[str, Any]): Tool configuration information
  • tool_parameters (dict[str, dict]): Parameters that need LLM reasoning
    • name (string): Parameter name
    • type (string): Parameter type
    • required (bool): Whether required
    • description (string): Parameter description
    • default (any): Default
    • options (list[string]): Options

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