# Quick Tool Integration Here, we will use GoogleSearch as an example to demonstrate how to quickly integrate a tool. ## 1. Prepare the Tool Provider yaml ### Introduction This yaml declares a new tool provider, and includes information like the provider's name, icon, author, and other details that are fetched by the frontend for display. ### Example We need to create a `google` module (folder) under `core/tools/provider/builtin`, and create `google.yaml`. The name must be consistent with the module name. Subsequently, all operations related to this tool will be carried out under this module. ```yaml identity: # Basic information of the tool provider author: Dify # Author name: google # Name, unique, no duplication with other providers label: # Label for frontend display en_US: Google # English label zh_Hans: Google # Chinese label description: # Description for frontend display en_US: Google # English description zh_Hans: Google # Chinese description icon: icon.svg # Icon, needs to be placed in the _assets folder of the current module ``` - The `identity` field is mandatory, it contains the basic information of the tool provider, including author, name, label, description, icon, etc. - The icon needs to be placed in the `_assets` folder of the current module, you can refer to [here](../../provider/builtin/google/_assets/icon.svg). ## 2. Prepare Provider Credentials Google, as a third-party tool, uses the API provided by SerpApi, which requires an API Key to use. This means that this tool needs a credential to use. For tools like `wikipedia`, there is no need to fill in the credential field, you can refer to [here](../../provider/builtin/wikipedia/wikipedia.yaml). After configuring the credential field, the effect is as follows: ```yaml identity: author: Dify name: google label: en_US: Google zh_Hans: Google description: en_US: Google zh_Hans: Google icon: icon.svg credentials_for_provider: # Credential field serpapi_api_key: # Credential field name type: secret-input # Credential field type required: true # Required or not label: # Credential field label en_US: SerpApi API key # English label zh_Hans: SerpApi API key # Chinese label placeholder: # Credential field placeholder en_US: Please input your SerpApi API key # English placeholder zh_Hans: 请输入你的 SerpApi API key # Chinese placeholder help: # Credential field help text en_US: Get your SerpApi API key from SerpApi # English help text zh_Hans: 从 SerpApi 获取您的 SerpApi API key # Chinese help text url: https://serpapi.com/manage-api-key # Credential field help link ``` - `type`: Credential field type, currently can be either `secret-input`, `text-input`, or `select` , corresponding to password input box, text input box, and drop-down box, respectively. If set to `secret-input`, it will mask the input content on the frontend, and the backend will encrypt the input content. ## 3. Prepare Tool yaml A provider can have multiple tools, each tool needs a yaml file to describe, this file contains the basic information, parameters, output, etc. of the tool. Still taking GoogleSearch as an example, we need to create a `tools` module under the `google` module, and create `tools/google_search.yaml`, the content is as follows. ```yaml identity: # Basic information of the tool name: google_search # Tool name, unique, no duplication with other tools author: Dify # Author label: # Label for frontend display en_US: GoogleSearch # English label zh_Hans: 谷歌搜索 # Chinese label description: # Description for frontend display human: # Introduction for frontend display, supports multiple languages en_US: A tool for performing a Google SERP search and extracting snippets and webpages.Input should be a search query. zh_Hans: 一个用于执行 Google SERP 搜索并提取片段和网页的工具。输入应该是一个搜索查询。 llm: A tool for performing a Google SERP search and extracting snippets and webpages.Input should be a search query. # Introduction passed to LLM, in order to make LLM better understand this tool, we suggest to write as detailed information about this tool as possible here, so that LLM can understand and use this tool parameters: # Parameter list - name: query # Parameter name type: string # Parameter type required: true # Required or not label: # Parameter label en_US: Query string # English label zh_Hans: 查询语句 # Chinese label human_description: # Introduction for frontend display, supports multiple languages en_US: used for searching zh_Hans: 用于搜索网页内容 llm_description: key words for searching # Introduction passed to LLM, similarly, in order to make LLM better understand this parameter, we suggest to write as detailed information about this parameter as possible here, so that LLM can understand this parameter form: llm # Form type, llm means this parameter needs to be inferred by Agent, the frontend will not display this parameter - name: result_type type: select # Parameter type required: true options: # Drop-down box options - value: text label: en_US: text zh_Hans: 文本 - value: link label: en_US: link zh_Hans: 链接 default: link label: en_US: Result type zh_Hans: 结果类型 human_description: en_US: used for selecting the result type, text or link zh_Hans: 用于选择结果类型，使用文本还是链接进行展示 form: form # Form type, form means this parameter needs to be filled in by the user on the frontend before the conversation starts ``` - The `identity` field is mandatory, it contains the basic information of the tool, including name, author, label, description, etc. - `parameters` Parameter list - `name` Parameter name, unique, no duplication with other parameters - `type` Parameter type, currently supports `string`, `number`, `boolean`, `select`, `secret-input` four types, corresponding to string, number, boolean, drop-down box, and encrypted input box, respectively. For sensitive information, we recommend using `secret-input` type - `required` Required or not - In `llm` mode, if the parameter is required, the Agent is required to infer this parameter - In `form` mode, if the parameter is required, the user is required to fill in this parameter on the frontend before the conversation starts - `options` Parameter options - In `llm` mode, Dify will pass all options to LLM, LLM can infer based on these options - In `form` mode, when `type` is `select`, the frontend will display these options - `default` Default value - `label` Parameter label, for frontend display - `human_description` Introduction for frontend display, supports multiple languages - `llm_description` Introduction passed to LLM, in order to make LLM better understand this parameter, we suggest to write as detailed information about this parameter as possible here, so that LLM can understand this parameter - `form` Form type, currently supports `llm`, `form` two types, corresponding to Agent self-inference and frontend filling ## 4. Add Tool Logic After completing the tool configuration, we can start writing the tool code that defines how it is invoked. Create `google_search.py` under the `google/tools` module, the content is as follows. ```python from core.tools.tool.builtin_tool import BuiltinTool from core.tools.entities.tool_entities import ToolInvokeMessage from typing import Any, Dict, List, Union class GoogleSearchTool(BuiltinTool): def _invoke(self, user_id: str, tool_parameters: Dict[str, Any], ) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]: """ invoke tools """ query = tool_parameters['query'] result_type = tool_parameters['result_type'] api_key = self.runtime.credentials['serpapi_api_key'] # TODO: search with serpapi result = SerpAPI(api_key).run(query, result_type=result_type) if result_type == 'text': return self.create_text_message(text=result) return self.create_link_message(link=result) ``` ### Parameters The overall logic of the tool is in the `_invoke` method, this method accepts two parameters: `user_id` and `tool_parameters`, which represent the user ID and tool parameters respectively ### Return Data When the tool returns, you can choose to return one message or multiple messages, here we return one message, using `create_text_message` and `create_link_message` can create a text message or a link message. ## 5. Add Provider Code Finally, we need to create a provider class under the provider module to implement the provider's credential verification logic. If the credential verification fails, it will throw a `ToolProviderCredentialValidationError` exception. Create `google.py` under the `google` module, the content is as follows. ```python from core.tools.entities.tool_entities import ToolInvokeMessage, ToolProviderType from core.tools.tool.tool import Tool from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController from core.tools.errors import ToolProviderCredentialValidationError from core.tools.provider.builtin.google.tools.google_search import GoogleSearchTool from typing import Any, Dict class GoogleProvider(BuiltinToolProviderController): def _validate_credentials(self, credentials: Dict[str, Any]) -> None: try: # 1. Here you need to instantiate a GoogleSearchTool with GoogleSearchTool(), it will automatically load the yaml configuration of GoogleSearchTool, but at this time it does not have credential information inside # 2. Then you need to use the fork_tool_runtime method to pass the current credential information to GoogleSearchTool # 3. Finally, invoke it, the parameters need to be passed according to the parameter rules configured in the yaml of GoogleSearchTool GoogleSearchTool().fork_tool_runtime( meta={ "credentials": credentials, } ).invoke( user_id='', tool_parameters={ "query": "test", "result_type": "link" }, ) except Exception as e: raise ToolProviderCredentialValidationError(str(e)) ``` ## Completion After the above steps are completed, we can see this tool on the frontend, and it can be used in the Agent. Of course, because google_search needs a credential, before using it, you also need to input your credentials on the frontend. ![Alt text](../zh_Hans/images/index/image-2.png)