> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dify.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Reverse Invocation Model

> This document details how plugins can reverse invoke model services within the Dify platform. It covers specific methods for reverse invoking LLM, Summary, TextEmbedding, Rerank, TTS, Speech2Text, and Moderation models. Each model invocation includes its entry point, interface parameter descriptions, practical usage code examples, and best practice recommendations for invoking models.

Reverse invoking a Model refers to the ability of a plugin to call Dify's internal LLM capabilities, including all model types and functions within the platform, such as TTS, Rerank, etc. If you are not familiar with the basic concepts of reverse invocation, please read [Reverse Invocation of Dify Services](/en/develop-plugin/features-and-specs/advanced-development/reverse-invocation) first.

However, please note that invoking a model requires passing a `ModelConfig` type parameter. Its structure can be referenced in the [General Specifications Definition](/en/develop-plugin/features-and-specs/plugin-types/general-specifications), and this structure will have slight differences for different types of models.

For example, for `LLM` type models, it also needs to include `completion_params` and `mode` parameters. You can manually construct this structure or use `model-selector` type parameters or configurations.

### Invoke LLM

#### **Entry Point**

```python theme={null}
    self.session.model.llm
```

#### **Endpoint**

```python theme={null}
    def invoke(
        self,
        model_config: LLMModelConfig,
        prompt_messages: list[PromptMessage],
        tools: list[PromptMessageTool] | None = None,
        stop: list[str] | None = None,
        stream: bool = True,
    ) -> Generator[LLMResultChunk, None, None] | LLMResult:
        pass
```

Please note that if the model you are invoking does not have `tool_call` capability, the `tools` passed here will not take effect.

#### **Use Case**

If you want to invoke OpenAI's `gpt-4o-mini` model within a `Tool`, please refer to the following example code:

```python theme={null}
from collections.abc import Generator
from typing import Any

from dify_plugin import Tool
from dify_plugin.entities.model.llm import LLMModelConfig
from dify_plugin.entities.tool import ToolInvokeMessage
from dify_plugin.entities.model.message import SystemPromptMessage, UserPromptMessage

class LLMTool(Tool):
    def _invoke(self, tool_parameters: dict[str, Any]) -> Generator[ToolInvokeMessage]:
        response = self.session.model.llm.invoke(
            model_config=LLMModelConfig(
                provider='openai',
                model='gpt-4o-mini',
                mode='chat',
                completion_params={}
            ),
            prompt_messages=[
                SystemPromptMessage(
                    content='you are a helpful assistant'
                ),
                UserPromptMessage(
                    content=tool_parameters.get('query')
                )
            ],
            stream=True
        )

        for chunk in response:
            if chunk.delta.message:
                assert isinstance(chunk.delta.message.content, str)
                yield self.create_text_message(text=chunk.delta.message.content)
```

Note that the `query` parameter from `tool_parameters` is passed in the code.

### **Best Practice**

It is not recommended to manually construct `LLMModelConfig`. Instead, allow users to select the model they want to use on the UI. In this case, you can modify the tool's parameter list by adding a `model` parameter as follows:

```yaml theme={null}
identity:
  name: llm
  author: Dify
  label:
    en_US: LLM
    zh_Hans: LLM
    pt_BR: LLM
description:
  human:
    en_US: A tool for invoking a large language model
    zh_Hans: 用于调用大型语言模型的工具
    pt_BR: A tool for invoking a large language model
  llm: A tool for invoking a large language model
parameters:
  - name: prompt
    type: string
    required: true
    label:
      en_US: Prompt string
      zh_Hans: 提示字符串
      pt_BR: Prompt string
    human_description:
      en_US: used for searching
      zh_Hans: 用于搜索网页内容
      pt_BR: used for searching
    llm_description: key words for searching
    form: llm
  - name: model
    type: model-selector
    scope: llm
    required: true
    label:
      en_US: Model
      zh_Hans: 使用的模型
      pt_BR: Model
    human_description:
      en_US: Model
      zh_Hans: 使用的模型
      pt_BR: Model
    llm_description: which Model to invoke
    form: form
extra:
  python:
    source: tools/llm.py
```

Please note that in this example, the `scope` of `model` is specified as `llm`. This means the user can only select `llm` type parameters. Thus, the code from the previous use case can be modified as follows:

```python theme={null}
from collections.abc import Generator
from typing import Any

from dify_plugin import Tool
from dify_plugin.entities.model.llm import LLMModelConfig
from dify_plugin.entities.tool import ToolInvokeMessage
from dify_plugin.entities.model.message import SystemPromptMessage, UserPromptMessage

class LLMTool(Tool):
    def _invoke(self, tool_parameters: dict[str, Any]) -> Generator[ToolInvokeMessage]:
        response = self.session.model.llm.invoke(
            model_config=tool_parameters.get('model'),
            prompt_messages=[
                SystemPromptMessage(
                    content='you are a helpful assistant'
                ),
                UserPromptMessage(
                    content=tool_parameters.get('query') # Assuming 'query' is still needed, otherwise use 'prompt' from parameters
                )
            ],
            stream=True
        )

        for chunk in response:
            if chunk.delta.message:
                assert isinstance(chunk.delta.message.content, str)
                yield self.create_text_message(text=chunk.delta.message.content)
```

### Invoke Summary

You can request this endpoint to summarize a piece of text. It will use the system model within your current workspace to summarize the text.

**Entry Point**

```python theme={null}
    self.session.model.summary
```

**Endpoint**

* `text` is the text to be summarized.
* `instruction` is the additional instruction you want to add, allowing you to summarize the text stylistically.

```python theme={null}
    def invoke(
        self, text: str, instruction: str,
    ) -> str:
```

### Invoke TextEmbedding

**Entry Point**

```python theme={null}
    self.session.model.text_embedding
```

**Endpoint**

```python theme={null}
    def invoke(
        self, model_config: TextEmbeddingResult, texts: list[str]
    ) -> TextEmbeddingResult:
        pass
```

### Invoke Rerank

**Entry Point**

```python theme={null}
    self.session.model.rerank
```

**Endpoint**

```python theme={null}
    def invoke(
        self, model_config: RerankModelConfig, docs: list[str], query: str
    ) -> RerankResult:
        pass
```

### Invoke TTS

**Entry Point**

```python theme={null}
    self.session.model.tts
```

**Endpoint**

```python theme={null}
    def invoke(
        self, model_config: TTSModelConfig, content_text: str
    ) -> Generator[bytes, None, None]:
        pass
```

Please note that the `bytes` stream returned by the `tts` endpoint is an `mp3` audio byte stream. Each iteration returns a complete audio segment. If you want to perform more in-depth processing tasks, please choose an appropriate library.

### Invoke Speech2Text

**Entry Point**

```python theme={null}
    self.session.model.speech2text
```

**Endpoint**

```python theme={null}
    def invoke(
        self, model_config: Speech2TextModelConfig, file: IO[bytes]
    ) -> str:
        pass
```

Where `file` is an audio file encoded in `mp3` format.

### Invoke Moderation

**Entry Point**

```python theme={null}
    self.session.model.moderation
```

**Endpoint**

```python theme={null}
    def invoke(self, model_config: ModerationModelConfig, text: str) -> bool:
        pass
```

If this endpoint returns `true`, it indicates that the `text` contains sensitive content.

## Related Resources

* [Reverse Invocation of Dify Services](/en/develop-plugin/features-and-specs/advanced-development/reverse-invocation) - Understand the fundamental concepts of reverse invocation
* [Reverse Invocation of App](/en/develop-plugin/features-and-specs/advanced-development/reverse-invocation-app) - Learn how to invoke Apps within the platform
* [Reverse Invocation of Tool](/en/develop-plugin/features-and-specs/advanced-development/reverse-invocation-tool) - Learn how to invoke other plugins
* [Model Plugin Development Guide](/en/develop-plugin/dev-guides-and-walkthroughs/creating-new-model-provider) - Learn how to develop custom model plugins
* [Model Designing Rules](/en/develop-plugin/features-and-specs/plugin-types/model-designing-rules) - Understand the design principles of model plugins

***

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