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295 lines
8.2 KiB
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295 lines
8.2 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: intermediate
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standard_title: Reverse Invocation Model
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language: zh
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title: Model
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description: 本文档详细介绍了插件如何反向调用Dify平台中的模型服务。内容包括反向调用LLM、Summary、TextEmbedding、Rerank、TTS、Speech2Text和Moderation等模型的具体方法，每种模型调用都配有对应的入口、接口参数说明以及实际的使用示例代码，并提供了调用模型的最佳实践建议。
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---
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反向调用 Model 指的是插件能够反向调用 Dify 内 LLM 的能力，包括平台内的所有模型类型与功能，例如 TTS、Rerank 等。如果你对反向调用的基本概念还不熟悉，请先阅读[反向调用 Dify 服务](/plugin-dev-zh/9241-reverse-invocation)。
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不过请注意，调用模型需要传入一个 `ModelConfig` 类型的参数，它的结构可以参考 [通用规范定义](/plugin-dev-zh/0411-general-specifications)，并且对于不同类型的模型，该结构会存在细微的差别。
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例如对于 `LLM` 类型的模型，还需要包含 `completion_params` 与 `mode` 参数，你可以手动构建该结构，或者使用 `model-selector` 类型的参数或配置。
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### 调用 LLM
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#### **入口**
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```python
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self.session.model.llm
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```
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#### **端点**
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```python
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def invoke(
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self,
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model_config: LLMModelConfig,
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prompt_messages: list[PromptMessage],
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tools: list[PromptMessageTool] | None = None,
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stop: list[str] | None = None,
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stream: bool = True,
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) -> Generator[LLMResultChunk, None, None] | LLMResult:
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pass
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```
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请注意，如果你调用的模型不具备 `tool_call` 的能力，那么此处传入的 `tools` 将不会生效。
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#### **用例**
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如果想在 `Tool` 中调用 `OpenAI` 的 `gpt-4o-mini` 模型，请参考以下示例代码：
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```python
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from collections.abc import Generator
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from typing import Any
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from dify_plugin import Tool
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from dify_plugin.entities.model.llm import LLMModelConfig
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from dify_plugin.entities.tool import ToolInvokeMessage
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from dify_plugin.entities.model.message import SystemPromptMessage, UserPromptMessage
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class LLMTool(Tool):
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def _invoke(self, tool_parameters: dict[str, Any]) -> Generator[ToolInvokeMessage]:
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response = self.session.model.llm.invoke(
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model_config=LLMModelConfig(
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provider='openai',
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model='gpt-4o-mini',
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mode='chat',
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completion_params={}
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),
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prompt_messages=[
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SystemPromptMessage(
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content='you are a helpful assistant'
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),
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UserPromptMessage(
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content=tool_parameters.get('query')
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)
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],
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stream=True
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)
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for chunk in response:
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if chunk.delta.message:
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assert isinstance(chunk.delta.message.content, str)
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yield self.create_text_message(text=chunk.delta.message.content)
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```
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可以留意到代码中传入了 `tool_parameters` 中的 `query` 参数。
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### **最佳实践**
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并不建议手动来构建 `LLMModelConfig`，而是允许用户可以在 UI 上选择自己想使用的模型，在这种情况下可以修改一下工具的参数列表，按照如下配置，添加一个 `model` 参数：
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```yaml
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identity:
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name: llm
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author: Dify
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label:
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en_US: LLM
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zh_Hans: LLM
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pt_BR: LLM
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description:
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human:
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en_US: A tool for invoking a large language model
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zh_Hans: 用于调用大型语言模型的工具
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pt_BR: A tool for invoking a large language model
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llm: A tool for invoking a large language model
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parameters:
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- name: prompt
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type: string
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required: true
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label:
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en_US: Prompt string
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zh_Hans: 提示字符串
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pt_BR: Prompt string
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human_description:
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en_US: used for searching
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zh_Hans: 用于搜索网页内容
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pt_BR: used for searching
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llm_description: key words for searching
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form: llm
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- name: model
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type: model-selector
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scope: llm
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required: true
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label:
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en_US: Model
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zh_Hans: 使用的模型
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pt_BR: Model
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human_description:
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en_US: Model
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zh_Hans: 使用的模型
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pt_BR: Model
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llm_description: which Model to invoke
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form: form
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extra:
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python:
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source: tools/llm.py
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```
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请注意在该例子中指定了 `model` 的 `scope` 为 `llm`，那么此时用户就只能选择 `llm` 类型的参数，从而可以将上述用例的代码改成以下代码：
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```python
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from collections.abc import Generator
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from typing import Any
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from dify_plugin import Tool
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from dify_plugin.entities.model.llm import LLMModelConfig
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from dify_plugin.entities.tool import ToolInvokeMessage
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from dify_plugin.entities.model.message import SystemPromptMessage, UserPromptMessage
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class LLMTool(Tool):
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def _invoke(self, tool_parameters: dict[str, Any]) -> Generator[ToolInvokeMessage]:
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response = self.session.model.llm.invoke(
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model_config=tool_parameters.get('model'),
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prompt_messages=[
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SystemPromptMessage(
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content='you are a helpful assistant'
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),
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UserPromptMessage(
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content=tool_parameters.get('query')
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)
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],
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stream=True
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)
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for chunk in response:
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if chunk.delta.message:
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assert isinstance(chunk.delta.message.content, str)
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yield self.create_text_message(text=chunk.delta.message.content)
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```
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### 调用 Summary
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你可以请求该端点来总结一段文本，它会使用你当前 workspace 内的系统模型来总结文本。
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**入口**
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```python
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self.session.model.summary
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```
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**端点**
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* `text` 为需要被总结的文本。
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* `instruction` 为你想要额外添加的指令，它可以让你风格化地总结文本。
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```python
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def invoke(
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self, text: str, instruction: str,
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) -> str:
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```
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### 调用 TextEmbedding
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**入口**
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```python
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self.session.model.text_embedding
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```
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**端点**
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```python
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def invoke(
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self, model_config: TextEmbeddingResult, texts: list[str]
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) -> TextEmbeddingResult:
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pass
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```
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### 调用 Rerank
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**入口**
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```python
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self.session.model.rerank
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```
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**端点**
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```python
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def invoke(
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self, model_config: RerankModelConfig, docs: list[str], query: str
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) -> RerankResult:
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pass
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```
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### 调用 TTS
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**入口**
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```python
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self.session.model.tts
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```
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**端点**
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```python
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def invoke(
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self, model_config: TTSModelConfig, content_text: str
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) -> Generator[bytes, None, None]:
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pass
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```
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请注意 `tts` 端点返回的 `bytes` 流是一个 `mp3` 音频字节流，每一轮迭代返回的都是一个完整的音频。如果你想做更深入的处理任务，请选择合适的库进行。
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### 调用 Speech2Text
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**入口**
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```python
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self.session.model.speech2text
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```
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**端点**
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```python
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def invoke(
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self, model_config: Speech2TextModelConfig, file: IO[bytes]
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) -> str:
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pass
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```
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其中 `file` 是一个 `mp3` 格式编码的音频文件。
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### 调用 Moderation
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**入口**
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```python
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self.session.model.moderation
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```
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**端点**
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```python
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def invoke(self, model_config: ModerationModelConfig, text: str) -> bool:
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pass
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```
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若该端点返回 `true` 则表示 `text` 中包含敏感内容。
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## 相关资源
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- [反向调用 Dify 服务](/plugin-dev-zh/9241-reverse-invocation) - 了解反向调用的根本概念
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- [反向调用 App](/plugin-dev-zh/9242-reverse-invocation-app) - 了解如何调用平台内的 App
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- [反向调用 Tool](/plugin-dev-zh/9242-reverse-invocation-tool) - 了解如何调用其它插件
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- [模型插件开发指南](/plugin-dev-zh/0211-getting-started-new-model) - 学习如何开发自定义模型插件
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- [模型设计规则](/plugin-dev-zh/0411-model-designing-rules) - 了解模型插件的设计原则
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{/*
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Contributing Section
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DO NOT edit this section!
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It will be automatically generated by the script.
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*/}
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---
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[编辑此页面](https://github.com/langgenius/dify-docs/edit/main/plugin-dev-zh/9242-reverse-invocation-model.mdx) | [提交问题](https://github.com/langgenius/dify-docs/issues/new?template=docs.yml)
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