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RiskeyL
f5e73aa5b2
Replace the per-page "Contributing Section" (previously baked into every plugin-dev page by tools/contributing_in_page.py) with a single custom JS file that Mintlify auto-includes on every page. It derives the GitHub edit URL from the current path at runtime, localizes the labels (en/zh/ja), and injects an "Edit this page | Report an issue" bar above the site footer, so the feature now covers all pages with no per-page markup. - add contributing-footer.js - remove the 117 baked-in Contributing Sections under en/zh/ja/develop-plugin - retire tools/contributing_in_page.py Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
106 lines
3.2 KiB
Plaintext
106 lines
3.2 KiB
Plaintext
---
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dimensions:
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type:
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primary: implementation
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detail: advanced
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level: advanced
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standard_title: Reverse Invocation Node
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language: en
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title: Node
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description: Invoke the Parameter Extractor and Question Classifier nodes from your plugin
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---
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A plugin can reverse invoke the capabilities of certain nodes within a Dify Chatflow/Workflow application.
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Plugins can call the `ParameterExtractor` and `QuestionClassifier` nodes. Both encapsulate complex prompt and code logic, using LLMs to handle tasks that are difficult to solve with hardcoded rules.
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## Call the Parameter Extractor Node
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### Entry Point
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```python
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self.session.workflow_node.parameter_extractor
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```
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### Interface
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```python
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def invoke(
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self,
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parameters: list[ParameterConfig],
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model: ModelConfig,
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query: str,
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instruction: str = "",
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) -> NodeResponse
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pass
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```
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- **`parameters`**: The list of parameters to extract.
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- **`model`**: Conforms to the `LLMModelConfig` specification.
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- **`query`**: The source text for parameter extraction.
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- **`instruction`**: Any additional instructions the LLM might need.
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For the structure of `NodeResponse`, see the [General Specifications Definition](/en/develop-plugin/features-and-specs/plugin-types/general-specifications#noderesponse).
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### Use Case
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This example extracts a person's name from a conversation:
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```python
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from collections.abc import Generator
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from dify_plugin import Tool
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from dify_plugin.entities.tool import ToolInvokeMessage
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from dify_plugin.entities.workflow_node import ModelConfig, NodeResponse, ParameterConfig
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class ParameterExtractorTool(Tool):
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def _invoke(
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self, tool_parameters: dict
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) -> Generator[ToolInvokeMessage, None, None]:
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response: NodeResponse = self.session.workflow_node.parameter_extractor.invoke(
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parameters=[
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ParameterConfig(
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name="name",
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description="name of the person",
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required=True,
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type="string",
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)
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],
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model=ModelConfig(
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provider="langgenius/openai/openai",
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name="gpt-4o-mini",
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completion_params={},
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),
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query="My name is John Doe",
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instruction="Extract the name of the person",
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)
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extracted_name = response.outputs.get("name", "Name not found")
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yield self.create_text_message(extracted_name)
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```
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`NodeResponse` is a Pydantic model defined in `dify_plugin.entities.workflow_node` with three dictionary fields: `process_data`, `inputs`, and `outputs`. Extracted values live under `response.outputs`.
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## Call the Question Classifier Node
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### Entry Point
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```python
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self.session.workflow_node.question_classifier
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```
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### Interface
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```python
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def invoke(
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self,
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classes: list[ClassConfig],
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model: ModelConfig,
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query: str,
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instruction: str = "",
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) -> NodeResponse:
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pass
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```
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`ClassConfig` is also exported from `dify_plugin.entities.workflow_node`. The interface parameters match those of `ParameterExtractor`, and the final result is stored in `response.outputs["class_name"]`.
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