+
### Enabling Annotated Replies in Prompt Orchestration
Enable the annotated replies switch by navigating to **“Orchestrate -> Add Features”**:
-
Enabling Annotated Replies in Prompt Orchestration
+
When enabling, you need to set the parameters for annotated replies, which include: Score Threshold and Embedding Model.
@@ -33,27 +33,27 @@ When enabling, you need to set the parameters for annotated replies, which inclu
Click save and enable, and the settings will take effect immediately. The system will generate embeddings for all saved annotations using the embedding model.
-
Setting Parameters for Annotated Replies
+
### Adding Annotations in the Conversation Debug Page
You can directly add or edit annotations on the model response information in the debug and preview pages.
-
Adding Annotated Replies
+
Edit the response to the high-quality reply you need and save it.
-
Editing Annotated Replies
+
Re-enter the same user question, and the system will use the saved annotation to reply to the user's question directly.
-
Replying to User Questions with Saved Annotations
+
### Enabling Annotated Replies in Logs and Annotations
Enable the annotated replies switch by navigating to “Logs & Ann. -> Annotations”:
-
Enabling Annotated Replies in Logs and Annotations
+
### Setting Parameters for Annotated Replies in the Annotation Backend
@@ -63,22 +63,22 @@ The parameters that can be set for annotated replies include: Score Threshold an
**Embedding Model:** This is used to vectorize the annotated text. Changing the model will regenerate the embeddings.
-
Setting Parameters for Annotated Replies
+
### Bulk Import of Annotated Q\&A Pairs
In the bulk import feature, you can download the annotation import template, edit the annotated Q\&A pairs according to the template format, and then import them in bulk.
-
Bulk Import of Annotated Q&A Pairs
+
### Bulk Export of Annotated Q\&A Pairs
Through the bulk export feature, you can export all saved annotated Q\&A pairs in the system at once.
-
Bulk Export of Annotated Q&A Pairs
+
### Viewing Annotation Hit History
In the annotation hit history feature, you can view the edit history of all hits on the annotation, the user's hit questions, the response answers, the source of the hits, the matching similarity scores, the hit time, and other information. You can use this information to continuously improve your annotated content.
-
Viewing Annotation Hit History
+
diff --git a/en/guides/annotation/logs.md b/en/guides/annotation/logs.md
index 2adb428e4..c4b0626bd 100644
--- a/en/guides/annotation/logs.md
+++ b/en/guides/annotation/logs.md
@@ -24,7 +24,7 @@ The logs currently do not include interaction records from the Prompt debugging
These annotations will be used for model fine-tuning in future versions of Dify to improve model accuracy and response style. The current preview version only supports annotations.
{% endhint %}
-
Mark logs to improve your app
+
Clicking on a log entry will open the log details panel on the right side of the interface. In this panel, operators can annotate an interaction:
diff --git a/en/guides/application-orchestrate/agent.md b/en/guides/application-orchestrate/agent.md
index 6a73faa31..5f5f01804 100644
--- a/en/guides/application-orchestrate/agent.md
+++ b/en/guides/application-orchestrate/agent.md
@@ -8,15 +8,15 @@ An Agent Assistant can leverage the reasoning abilities of large language models
To facilitate quick learning and use, application templates for the Agent Assistant are available in the 'Explore' section. You can integrate these templates into your workspace. The new Dify 'Studio' also allows the creation of a custom Agent Assistant to suit individual requirements. This assistant can assist in analyzing financial reports, composing reports, designing logos, and organizing travel plans.
-
Explore-Agent Assistant Application Template
+
The task completion ability of the Agent Assistant depends on the inference capabilities of the model selected. We recommend using a more powerful model series like GPT-4 when employing Agent Assistant to achieve more stable task completion results.
-
Selecting the Reasoning Model for Agent Assistant
+
You can write prompts for the Agent Assistant in 'Instructions'. To achieve optimal results, you can clearly define its task objectives, workflow, resources, and limitations in the instructions.
-
Orchestrating Prompts for Agent Assistant
+
## Adding Tools for the Agent Assistant
@@ -26,13 +26,13 @@ In the "Tools" section, you are able to add tools that are required for use. The
You have the option to directly use built-in tools in Dify, or you can easily import custom API tools (currently supporting OpenAPI/Swagger and OpenAI Plugin standards).
-
Adding Tools for the Assistant
+
The **Tools** feature allows you to create more powerful AI applications on Dify. For example, you can orchestrate suitable tools for Agent Assistant, enabling it to complete complex tasks through reasoning, step decomposition, and tool invocation.
Additionally, the tool simplifies the integration of your application with other systems or services, enabling interactions with the external environment, such as executing code or accessing proprietary information sources. Simply mention the name of the tool you want to invoke in the chat box, and it will be automatically activated.
-
+
## Agent Settings
@@ -40,15 +40,15 @@ On Dify, two inference modes are provided for Agent Assistant: Function Calling
In the Agent settings, you can modify the iteration limit of the Agent.
-
+
## Configuring the Conversation Opener
You can set up a conversation opener and initial questions for your Agent Assistant. The configured conversation opener will be displayed at the beginning of each user's first interaction, showcasing the types of tasks the Agent can perform, along with examples of questions that can be asked.
-
Configuring the Conversation Opener and Initial Questions
+
### Uploading Documentation File
@@ -62,8 +62,8 @@ Select an LLM that supports file reading and enable the "Documentation" feature.
After orchestrating your Agent Assistant, you have the option to debug and preview it before publishing it as an application. This allows you to assess the effectiveness of the agent in completing tasks.
-
Debugging and Preview
+
## Application Publish
-
Publishing the Application as a Webapp
+
diff --git a/en/guides/application-orchestrate/app-toolkits/README.md b/en/guides/application-orchestrate/app-toolkits/README.md
index a825fc0cb..6f8ef7592 100644
--- a/en/guides/application-orchestrate/app-toolkits/README.md
+++ b/en/guides/application-orchestrate/app-toolkits/README.md
@@ -4,19 +4,19 @@ In **Application Orchestration**, click **Add Feature** to open the application
The application toolbox provides various additional features for Dify's [applications](../#application_type):
-
+
### Conversation Opening
In conversational applications, the AI will proactively say the first sentence or ask a question. You can edit the content of the opening, including the initial question. Using conversation openings can guide users to ask questions, explain the application background, and lower the barrier for initiating a conversation.
-
Conversation Opening
+
### Next Step Question Suggestions
Setting next step question suggestions allows the AI to generate 3 follow-up questions based on the previous conversation, guiding the next round of interaction.
-
+
### Citation and Attribution
diff --git a/en/guides/application-orchestrate/app-toolkits/moderation-tool.md b/en/guides/application-orchestrate/app-toolkits/moderation-tool.md
index 73c387212..247a64e10 100644
--- a/en/guides/application-orchestrate/app-toolkits/moderation-tool.md
+++ b/en/guides/application-orchestrate/app-toolkits/moderation-tool.md
@@ -2,7 +2,7 @@
In our interactions with AI applications, we often have stringent requirements in terms of content security, user experience, and legal regulations. At this point, we need the "Sensitive Word Review" feature to create a better interactive environment for end-users. On the orchestration page, click "Add Feature" and locate the "Content Review" toolbox at the bottom:
-
Content moderation
+
## Call the OpenAI Moderation API
@@ -10,16 +10,16 @@ OpenAI, along with most companies providing LLMs, includes content moderation fe
Now you can also directly call the OpenAI Moderation API on Dify; you can review either input or output content simply by entering the corresponding "preset reply."
-
Configuring Load Balancing from Add Model
+
## Keywords
Developers can customize the sensitive words they need to review, such as using "kill" as a keyword to perform an audit action when users input. The preset reply content should be "The content is violating usage policies." It can be anticipated that when a user inputs a text chuck containing "kill" at the terminal, it will trigger the sensitive word review tool and return the preset reply content.
-
Configuring Load Balancing from Add Model
+
## Moderation Extension
Different enterprises often have their own mechanisms for sensitive word moderation. When developing their own AI applications, such as an internal knowledge base ChatBot, enterprises need to moderate the query content input by employees for sensitive words. For this purpose, developers can write an API extension based on their enterprise's internal sensitive word moderation mechanisms, which can then be called on Dify to achieve a high degree of customization and privacy protection for sensitive word review.
-
Moderation Extension
+
diff --git a/en/guides/application-orchestrate/chatbot-application.md b/en/guides/application-orchestrate/chatbot-application.md
index 02e5511f0..35d163078 100644
--- a/en/guides/application-orchestrate/chatbot-application.md
+++ b/en/guides/application-orchestrate/chatbot-application.md
@@ -22,7 +22,7 @@ Click the "Create Application" button on the homepage to create an application.
After the application is successfully created, it will automatically redirect to the application overview page. Click on the button on the left menu: **"Orchestrate"** to compose the application.
-
+
**2.1 Fill in Prompts**
@@ -38,17 +38,17 @@ For a better experience, we will add an opening dialogue: `"Hello, {{name}}. I'm
To add the opening dialogue, click the "Add Feature" button in the upper left corner, and enable the "Conversation remarkers" feature:
-
+
And then edit the opening remarks:
-
+
**2.2 Adding Context**
If an application wants to generate content based on private contextual conversations, it can use our [knowledge](../knowledge-base/) feature. Click the "Add" button in the context to add a knowledge base.
-
+
**2.3 Uploading Documentation File**
@@ -62,11 +62,11 @@ Select an LLM that supports file reading and enable the "Documentation" feature.
Enter user inputs on the right side and check the respond content.
-
+
If the results are not satisfactory, you can adjust the prompts and model parameters. Click on the model name in the upper right corner to set the parameters of the model:
-
+
**Multiple Model Debugging:**
diff --git a/en/guides/application-orchestrate/creating-an-application.md b/en/guides/application-orchestrate/creating-an-application.md
index e026807f4..f4068277f 100644
--- a/en/guides/application-orchestrate/creating-an-application.md
+++ b/en/guides/application-orchestrate/creating-an-application.md
@@ -12,17 +12,17 @@ When using Dify for the first time, you might be unfamiliar with creating applic
You can select "Studio" from the navigation menu, then choose "Create from Template" in the application list.
-
Create an application from a template
+
Select any template and click **Use this template.**
-
Dify application templates
+
### Creating a New Application
If you need to create a blank application on Dify, you can select "Studio" from the navigation and then choose "Create from Blank" in the application list.
-
Create a blank application
+
When creating an application for the first time, you might need to first understand the [basic concepts](./#application_type) of the four different types of applications on Dify: Chatbot, Text Generator, Agent, Chatflow and Workflow.
@@ -42,7 +42,7 @@ Dify DSL is an AI application engineering file standard defined by Dify.AI. The
If you have obtained a template (DSL file) from the community or others, you can choose "Import DSL File" from the studio. After importing, all configuration information of the original application will be loaded directly.
-
Create an application by importing a DSL file
+
#### Import DSL file from URL
@@ -52,6 +52,6 @@ You can also import DSL files via a URL, using the following link format:
https://example.com/your_dsl.yml
```
-
Create an application by importing a DSL file
+
> When importing a DSL file, the version will be checked. Significant discrepancies between DSL versions may lead to compatibility issues. For more details, please refer to [Application Management: Import](https://docs.dify.ai/guides/management/app-management#importing-application).
diff --git a/en/guides/application-orchestrate/prompt-engineering/prompt-engineering-expert-mode.md b/en/guides/application-orchestrate/prompt-engineering/prompt-engineering-expert-mode.md
index 968c0dbef..be693a3cb 100644
--- a/en/guides/application-orchestrate/prompt-engineering/prompt-engineering-expert-mode.md
+++ b/en/guides/application-orchestrate/prompt-engineering/prompt-engineering-expert-mode.md
@@ -62,7 +62,7 @@ Well, before you try the new mode, you should be aware of some essential element
After creating an application, you can switch to the **Expert Mode** on the prompt design page.
-
Access to the Expert Mode
+
{% hint style="warning" %}
After modifying the prompts in the **Expert Mode** and publishing the application, you will not be able to revert back to the **Basic Mode**.
@@ -72,7 +72,7 @@ After modifying the prompts in the **Expert Mode** and publishing the applicatio
In both two modes, you can modify the parameters for the inserting context, which includes **TopK** and **Score Threshold**.
-
Context parameters
+
{% hint style="warning" %}
Please note that only after uploading the context, the built-in prompts containing will be displayed on the prompt design page.
@@ -116,7 +116,7 @@ Because LLM stops generating content before the next `Human1:`.
You can enter "/" in the text editor to quickly bring up Blocks to insert into the prompt.
-
shortcut "/"
+
{% hint style="warning" %}
Except for `Variables`, other Blocks cannot be inserted repeatedly. In different applications and models, the Blocks that can be inserted will vary based on different prompt template structures.
@@ -159,7 +159,7 @@ During debugging, you can not only check the user's input and LLM's responses bu
In the debugging preview interface, after engaging in a conversation with the AI, simply move the mouse pointer over any user session, and you will see the "Log" icon button in the upper-left corner. Click on it to view the Prompt Log.
-
Access to the Prompt Log
+.png)
In the Prompt Log, we can clearly see:
@@ -167,7 +167,7 @@ In the Prompt Log, we can clearly see:
2. Relevant text chucks referenced in the current session.
3. Historical session records.
-
Prompt Log
+
From the log, we can view the complete prompts that have been assembled by the system and sent to LLM, and we can continuously improve prompt input based on debugging results.
@@ -177,4 +177,4 @@ In the initial application's main interface, you can find "Logs & Ann." in the l
For more head to 👉 [logs.md](../logs.md "mention") .
-
Logs & Ann.
+
diff --git a/en/guides/application-orchestrate/text-generation-application.md b/en/guides/application-orchestrate/text-generation-application.md
index 082e5043a..cc60fd201 100644
--- a/en/guides/application-orchestrate/text-generation-application.md
+++ b/en/guides/application-orchestrate/text-generation-application.md
@@ -16,13 +16,13 @@ Here, we use a translation application as an example to introduce the way to com
Click the "Create Application" button on the homepage to create an application. Fill in the application name, and select "Text Generator" as the application type.
-
Create Application
+.png)
#### Step 2: Compose the Application
After the application is successfully created, it will automatically redirect to the application overview page. Click on the left-hand menu: “**Prompt Eng.**” to compose the application.
-
+.png)
**2.1 Fill in Prefix Prompts**
@@ -42,7 +42,7 @@ If the application wants to generate content based on private contextual convers
Generating more like this allows you to generate multiple texts at once, which you can edit and continue generating from. Click on the "Add Future" button in the upper left corner to enable this feature.
-
+.png)
**2.4 Debugging**
@@ -62,6 +62,6 @@ After debugging the application, click the **"Publish"** button in the upper rig
You can find the sharing address of the application on the overview page. Click the "Preview" button to preview the shared application. Click the "Share" button to obtain the sharing link address. Click the "Settings" button to set the information of the shared application.
-
+.png)
If you want to customize the application shared outside, you can Fork our open source [WebApp template](https://github.com/langgenius/webapp-text-generator). Based on the template, you can modify the application to meet your specific situation and style requirements.
diff --git a/en/guides/application-publishing/developing-with-apis.md b/en/guides/application-publishing/developing-with-apis.md
index 87ab8dfbf..8aa0a6062 100644
--- a/en/guides/application-publishing/developing-with-apis.md
+++ b/en/guides/application-publishing/developing-with-apis.md
@@ -15,7 +15,7 @@ Dify offers a "Backend-as-a-Service" API, providing numerous benefits to AI appl
Choose an application, and find the API Access in the left-side navigation of the Apps section. On this page, you can view the API documentation provided by Dify and manage credentials for accessing the API.
-
API document
+
You can create multiple access credentials for an application to deliver to different users or developers. This means that API users can use the AI capabilities provided by the application developer, but the underlying Prompt engineering, knowledge, and tool capabilities are encapsulated.
diff --git a/en/guides/application-publishing/launch-your-webapp-quickly/README.md b/en/guides/application-publishing/launch-your-webapp-quickly/README.md
index 57b7be8ff..c3bffee2b 100644
--- a/en/guides/application-publishing/launch-your-webapp-quickly/README.md
+++ b/en/guides/application-publishing/launch-your-webapp-quickly/README.md
@@ -9,7 +9,7 @@ One of the benefits of creating AI applications with Dify is that you can publis
Toggle the **"In service / Disabled"** switch, your Web App URL will be effective immediately publicly shared on the internet.
-
+
We have pre-set Web App UI for the following two types of applications:
@@ -29,7 +29,7 @@ We have pre-set Web App UI for the following two types of applications:
You can modify the language, color theme, copyright ownership, privacy policy link, and disclaimer by clicking the "setting" button.
-
+
Currently, Web App supports multiple languages: English, Simplified Chinese, Traditional Chinese, Portuguese, German, Japanese, Korean, Ukrainian, and Vietnamese. If you want more languages to be supported, you can submit an Issue on GitHub to seek support or submit a PR to contribute code.
diff --git a/en/guides/application-publishing/launch-your-webapp-quickly/conversation-application.md b/en/guides/application-publishing/launch-your-webapp-quickly/conversation-application.md
index f4d730bc8..221c7534e 100644
--- a/en/guides/application-publishing/launch-your-webapp-quickly/conversation-application.md
+++ b/en/guides/application-publishing/launch-your-webapp-quickly/conversation-application.md
@@ -13,29 +13,29 @@ Conversational applications engage in continuous dialogue with users in a questi
If you have set variable filling requirements during application orchestration, you will need to fill out the prompted information before entering the conversation window:
-
+
Fill in the necessary details and click the "Start Conversation" button to begin chatting. Hover over the AI's response to copy the conversation content, and provide "like" or "dislike" feedback.
-
+
### Creation, Pinning, and Deletion of Conversations
Click the "New Conversation" button to start a new conversation. Hover over a conversation to pin or delete it.
-
+
### Conversation Opener
If the "Conversation Opener" feature is enabled on the application orchestration page, the AI application will automatically initiate the first line of dialogue when a new conversation is created.
-
+
### Follow Up
If the "Follow-up" feature is enabled on the application orchestration page, the system will automatically generate 3 relevant question suggestions after the conversation:
-
+
### Speech-to-Text
@@ -43,7 +43,7 @@ If the "Speech-to-Text" feature is enabled during application orchestration, you
_Please ensure that your device environment is authorized to use the microphone._
-
+
### References and Attributions
diff --git a/en/guides/application-publishing/launch-your-webapp-quickly/text-generator.md b/en/guides/application-publishing/launch-your-webapp-quickly/text-generator.md
index 2cdcfe56d..dea418f79 100644
--- a/en/guides/application-publishing/launch-your-webapp-quickly/text-generator.md
+++ b/en/guides/application-publishing/launch-your-webapp-quickly/text-generator.md
@@ -15,7 +15,7 @@ Let's introduce them separately.
Enter the query content, click the run button, and the result will be generated on the right, as shown in the following figure:
-
+
In the generated results section, click the "Copy" button to copy the content to the clipboard. Click the "Save" button to save the content. You can see the saved content in the "Saved" tab. You can also "like" and "dislike" the generated content.
@@ -29,17 +29,17 @@ In the above scenario, the batch operation function is used, which is convenient
Click the "Run Batch" tab to enter the batch run page.
-
+
#### Step 2 Download the template and fill in the content
Click the **"Download the template here"** button to obtain the template file. Edit the file and fill in the required content, then save it as a `.csv` file. Finally, upload the completed file back to Dify.
-
+
#### Step 3 Upload the file and run
-
+
If you need to export the generated content, you can click the download "button" in the upper right corner to export as a `csv` file.
@@ -49,10 +49,10 @@ If you need to export the generated content, you can click the download "button"
Click the "Save" button below the generated results to save the running results. In the "Saved" tab, you can see all saved content.
-
+
### Generate more similar results
If the "More like this" function is turned on the App's Orchestrate page,clicking the "More like this" button in the web application generates content similar to the current result. As shown below:
-
+
diff --git a/en/guides/extension/api-based-extension/README.md b/en/guides/extension/api-based-extension/README.md
index fd9f62c62..359a85cb4 100644
--- a/en/guides/extension/api-based-extension/README.md
+++ b/en/guides/extension/api-based-extension/README.md
@@ -7,7 +7,7 @@ Developers can extend module capabilities through the API extension module. Curr
Before extending module capabilities, prepare an API and an API Key for authentication, which can also be automatically generated by Dify. In addition to developing the corresponding module capabilities, follow the specifications below so that Dify can invoke the API correctly.
-
Add API Extension
+
## API Specifications
@@ -194,11 +194,11 @@ The default port is 8000. The complete address of the API is: `http://127.0.0.1:
#### Configure this API in Dify.
-
+
#### Select this API extension in the App.
-
+
When debugging the App, Dify will request the configured API and send the following content (example):
@@ -230,7 +230,7 @@ Since Dify's cloud version can't access internal network API services, you can u
1. Visit the Ngrok official website at [https://ngrok.com](https://ngrok.com/), register, and download the Ngrok file.
-
+
2. After downloading, go to the download directory. Unzip the package and run the initialization script as instructed:
@@ -241,7 +241,7 @@ $ ./ngrok config add-authtoken 你的Token
3. Check the port of your local API service.
-
+
Run the following command to start:
@@ -251,7 +251,7 @@ $ ./ngrok http [port number]
Upon successful startup, you'll see something like the following:
-
+
4. Find the 'Forwarding' address, like the sample domain `https://177e-159-223-41-52.ngrok-free.app`, and use it as your public domain.
diff --git a/en/guides/extension/api-based-extension/cloudflare-workers.md b/en/guides/extension/api-based-extension/cloudflare-workers.md
index 7607dea09..021609274 100644
--- a/en/guides/extension/api-based-extension/cloudflare-workers.md
+++ b/en/guides/extension/api-based-extension/cloudflare-workers.md
@@ -43,9 +43,9 @@ npm run deploy
After successful deployment, you will get a public internet address, which you can add in Dify as an API Endpoint. Please note not to miss the `endpoint` path.
-
Adding API Endpoint in Dify
+
-
Adding API Tool in the App edit page
+
## Other Logic TL;DR
diff --git a/en/guides/knowledge-base/README.md b/en/guides/knowledge-base/README.md
index 50fc48552..0c24af747 100644
--- a/en/guides/knowledge-base/README.md
+++ b/en/guides/knowledge-base/README.md
@@ -30,7 +30,7 @@ By simply uploading files to the **Knowledge Base**, data processing is handled
> If your team already has an independent knowledge base, you can use the [“Connect to an External Knowledge Base”](connect-external-knowledge.md) feature to establish its connection with Dify.
-
Create a knowledge base
+
### **Use Case**
diff --git a/en/guides/knowledge-base/connect-external-knowledge.md b/en/guides/knowledge-base/connect-external-knowledge.md
index d871fe5f2..b670cd87b 100644
--- a/en/guides/knowledge-base/connect-external-knowledge.md
+++ b/en/guides/knowledge-base/connect-external-knowledge.md
@@ -15,7 +15,7 @@ The **Connect to External Knowledge Base** feature enables integration between t
* The Dify platform can directly obtain the text content hosted in the cloud service provider's knowledge base, so that developers do not need to repeatedly move the content to the knowledge base in Dify;
* The Dify platform can directly obtain the text content processed by algorithms in the self-built knowledge base. Developers only need to focus on the information retrieval mechanism of the self-built knowledge base and continuously optimize and improve the accuracy of information retrieval.
-
Principle of external knowledge base connection
+
Here are the detailed steps for connecting to external knowledge:
@@ -33,13 +33,13 @@ Navigate to the **"Knowledge"** page, click **"External Knowledge API"** in the
* API Endpoint The URL of the external knowledge base API endpoint, e.g., api-endpoint/retrieval; refer to the [External Knowledge API](external-knowledge-api-documentation.md) for detailed instructions;
* API Key Connection key for the external knowledge, refer to the [External Knowledge API](external-knowledge-api-documentation.md) for detailed instructions.
-
Associate External Knowledge API
+
## 3. Connect to the External Knowledge Base
Go to the **"Knowledge"** page, click **"Connect to an External Knowledge Base"** under the Add Knowledge Base card to direct to the parameter configuration page.
-
Connect to the external knowledge base
+
Fill in the following parameters:
@@ -56,13 +56,13 @@ Fill in the following parameters:
**Score Threshold:** The similarity threshold for text chunk filtering, only retrievaling text chunks that exceed the set score. The default value is 0.5. A higher value indicates a higher requirement for similarity between the text and the question, expecting fewer retrievaled text chunks, and the results will be relatively more precise.
-
+
## 4. Test External Knowledge Base and Retrieval Results
After connected with the external knowledge base, developers can simulate possible question keywords in the **"Retrieval Testing"** to preview text chunks that might be retrieval. If you are unsatisfied with the retrieval results, try modifying the **External Knowledge Base Settings** or adjusting the retrieval strategy of the external knowledge base.
-
Test external knowledge base connection and retrieval
+
## 5. Integrating External Knowledge base in Applications
@@ -70,13 +70,13 @@ After connected with the external knowledge base, developers can simulate possib
Associate the external knowledge base in the orchestration page within Chatbot / Agent type applications.
-
Chatbot / Agent
+
* **Chatflow / Workflow** type application
Add a **"Knowledge Retrieval"** node and select the external knowledge base.
-
Chatflow / Workflow
+
## 6. Manage External Knowledge
@@ -94,7 +94,7 @@ Navigate to the **"Knowledge"** page, external knowledge base cards will list an
The **"External Knowledge API"** and **"External Knowledge ID"** associated with the external knowledge base do not support modification. If modification is needed, please associate a new **"External Knowledge API"** and reset it.
-
+
### Connection Example
diff --git a/en/guides/knowledge-base/create-knowledge-and-upload-documents.md b/en/guides/knowledge-base/create-knowledge-and-upload-documents.md
index 4cfb971c4..4369b0113 100644
--- a/en/guides/knowledge-base/create-knowledge-and-upload-documents.md
+++ b/en/guides/knowledge-base/create-knowledge-and-upload-documents.md
@@ -23,7 +23,7 @@ Steps to upload documents to create a knowledge base:
4. Wait for the chunk embeddings to complete.
5. Once finished, link the knowledge base to your application and start using it. You can then [integrate it into your application](integrate-knowledge-within-application.md) to build an LLM that are capable of Q\&A based on knowledge-bases. If you want to modify and manage the knowledge base further, take refer to [Knowledge Base and Document Maintenance](knowledge-and-documents-maintenance.md).
-
Complete the creation of the knowledge base
+
***
diff --git a/en/guides/knowledge-base/create-knowledge-and-upload-documents/1.-import-text-data/1.1-import-data-from-notion.md b/en/guides/knowledge-base/create-knowledge-and-upload-documents/1.-import-text-data/1.1-import-data-from-notion.md
index ec32d4350..f95ff1074 100644
--- a/en/guides/knowledge-base/create-knowledge-and-upload-documents/1.-import-text-data/1.1-import-data-from-notion.md
+++ b/en/guides/knowledge-base/create-knowledge-and-upload-documents/1.-import-text-data/1.1-import-data-from-notion.md
@@ -13,7 +13,7 @@ The Dify knowledge base supports importing data from Notion with subsequent auto
After completing the authorization verification, go to the create dataset page, click **Sync from Notion Content**, and select the authorized pages you need to import.
-
Import from notion
+
### Chunking and Cleaning
@@ -21,7 +21,7 @@ Next, choose a [chunking mode](1.1-import-data-from-notion.md#chunking-and-clean
_**Note: images and files cannot be imported, and data from tables will be converted to text.**_
-
Preview of Chunked Results for Notion Page
+
### Synchronizing Notion Data
diff --git a/en/guides/knowledge-base/create-knowledge-and-upload-documents/1.-import-text-data/README.md b/en/guides/knowledge-base/create-knowledge-and-upload-documents/1.-import-text-data/README.md
index 8008ca055..5ce35d4b8 100644
--- a/en/guides/knowledge-base/create-knowledge-and-upload-documents/1.-import-text-data/README.md
+++ b/en/guides/knowledge-base/create-knowledge-and-upload-documents/1.-import-text-data/README.md
@@ -13,7 +13,7 @@ Drag and drop or select files to upload. The number of files allowed for **batch
* The upload size limit for a single document is 15MB;
* Different [subscription plans](https://dify.ai/pricing) for the SaaS version limit **batch upload numbers, total document uploads, and vector storage**
-
Create knowledge
+
### Import From Online Data Source
diff --git a/en/guides/knowledge-base/create-knowledge-and-upload-documents/2.-choose-a-chunk-mode.md b/en/guides/knowledge-base/create-knowledge-and-upload-documents/2.-choose-a-chunk-mode.md
index 2185ba78e..1d5890588 100644
--- a/en/guides/knowledge-base/create-knowledge-and-upload-documents/2.-choose-a-chunk-mode.md
+++ b/en/guides/knowledge-base/create-knowledge-and-upload-documents/2.-choose-a-chunk-mode.md
@@ -36,7 +36,7 @@ The knowledge base supports two chunking modes: **General Mode** and **Parent-ch
Once the chunk mode is selected and the knowledge base is created, it cannot be changed later. Any new documents added to the knowledge base will follow the same chunking strategy.
{% endhint %}
-
General mode and Parent-child mode
+
#### General Mode
@@ -46,7 +46,7 @@ In this mode, you need to manually define text chunking rules based on different
* **Chunk identifier**, The default value is `\n`, which means the text will be chunked by paragraphs. You can customize chunking rules using [regex](https://regexr.com/). The system will automatically execute chunking whenever it detects the specified delimiter. For example, means chunk the text by sentences.
-
Chunk results of different identifier syntaxes
+ 
* **Maximum chunk length:** Specifies the maximum number of text characters allowed per chunk. If this limit is exceeded, the system will automatically enforce chunking. The default value is 500 tokens, with a maximum chunk length of 4000 tokens.
* **Overlapping chunk length**: When data is chunked, there is a certain amount of overlap between chunks. This overlap can help to improve the retention of information and the accuracy of analysis, and enhance retrieval effects. It is recommended that the setting be 10-25% of the chunk length Tokens.
@@ -59,7 +59,7 @@ Once configured, click **“Preview Chunk”** to see the chunking results. You
If multiple documents are uploaded in bulk, you can switch between them by clicking the document titles at the top to review the chunk results for other documents.
-
General mode
+
After setting the chunking rules, the next step is to specify the indexing method. General mode supports **High-Quality Indexing** **Method** and **Economical Indexing Method**. For more details, please refer to [Set up the Indexing Method](3.-select-the-indexing-method-and-retrieval-setting.md).
@@ -80,7 +80,7 @@ Its fundamental mechanism includes:
* Larger, encompassing sections—such as a paragraph, a section, or even an entire document—that include the matched child chunks are then retrieved.
* These parent chunks provide comprehensive context for the Language Model (LLM).
-
Parent-child mode schematic
+
In this mode, you need to manually configure separate chunking rules for both parent and child chunks based on different document formats or specific scenario requirements.
@@ -98,7 +98,7 @@ The parent chunk settings offer the following options:
Instead of splitting the text into paragraphs, the entire document is used as the parent chunk and retrieved directly. For performance reasons, only the first 10,000 tokens of the text are retained. This setting is ideal for smaller documents where paragraphs are interrelated, requiring full doc retrieval.
-
The difference between paragraph and Full doc
+
**Child Chunk**
@@ -125,7 +125,7 @@ If you modify the chunking rules, click the button again to view the latest gene
If multiple documents are uploaded in bulk, you can switch between them by clicking the document titles at the top to review the chunk results for other documents.
-
Parent-child mode
+
To ensure accurate content retrieval, the Parent-child chunk mode only supports the [High-Quality Indexing](3.-select-the-indexing-method-and-retrieval-setting.md#high-quality).
@@ -135,7 +135,7 @@ The difference between the two modes lies in the structure of the content chunks
Different chunking methods influence how effectively the LLM can search the knowledge base. When used on the same document, Parent-child Retrieval provides more comprehensive context while maintaining high precision, making it significantly more effective than the traditional single-layer approach.
-
The Difference Between General Mode and Parent-child Mode
+
### Reference
diff --git a/en/guides/knowledge-base/create-knowledge-and-upload-documents/3.-select-the-indexing-method-and-retrieval-setting.md b/en/guides/knowledge-base/create-knowledge-and-upload-documents/3.-select-the-indexing-method-and-retrieval-setting.md
index 87bde428d..a3e20fde6 100644
--- a/en/guides/knowledge-base/create-knowledge-and-upload-documents/3.-select-the-indexing-method-and-retrieval-setting.md
+++ b/en/guides/knowledge-base/create-knowledge-and-upload-documents/3.-select-the-indexing-method-and-retrieval-setting.md
@@ -22,7 +22,7 @@ Once the text chunks are vectorized and stored in the database, an effective ret
After selecting **High Quality** mode, the indexing method for the knowledge base cannot be downgraded to **Economical** mode later. If you need to switch, it is recommended to create a new knowledge base and select the desired indexing method.
-
High Quality Indexing Mode
+
#### Enable Q\&A Mode (Optional, Community Edition Only)
@@ -34,11 +34,11 @@ This approach is particularly effective because the text in FAQ documents **is o
> The **Q to Q** strategy makes the matching between questions and answers clearer and better supports scenarios with high-frequency or highly similar questions.
-
Q&A Chunk
+
When a user asks a question, the system identifies the most similar question and returns the corresponding chunk as the answer. This approach is more precise, as it directly matches the user’s query, helping them retrieve the exact information they need.
-
Difference between Q to P and Q to Q indexing method
+
{% endtab %}
{% tab title="Economical" %}
@@ -48,7 +48,7 @@ Using 10 keywords per chunk for retrieval, no tokens are consumed at the expense
If the performance of the economical indexing method does not meet your expectations, you can upgrade to the High-Quality indexing method in the Knowledge settings page.
-
Economical mode
+
{% endtab %}
{% endtabs %}
@@ -69,13 +69,13 @@ Both retrieval methods are supported in Dify’s knowledge base. The specific re
In the **High-Quality** Indexing Method, Dify offers three retrieval settings: **Vector Search, Full-Text Search, and Hybrid Search**.
-
Retrieval Settings
+
**Vector Search**
**Definition**: Vectorize the user’s question to generate a query vector, then compare it with the corresponding text vectors in the knowledge base to find the nearest chunks.
-
Vector search settings
+
**Vector Search Settings：**
@@ -93,7 +93,7 @@ In the **High-Quality** Indexing Method, Dify offers three retrieval settings: *
**Definition:** Indexing all terms in the document, allowing users to query any terms and return text fragments containing those terms.
-
Full-Text Search Settings
+
**Rerank Model**: Disabled by default. When enabled, a third-party Rerank model will sort the text chunks returned by Full-Text Search to optimize results. This helps the LLM access more precise information and improve output quality. Before enabling this option, go to **Settings** → **Model Providers** and configure the Rerank model’s API key.
@@ -109,7 +109,7 @@ In the **High-Quality** Indexing Method, Dify offers three retrieval settings: *
**Definition**: This process combines full-text search and vector search, performing both simultaneously. It includes a reordering step to select the best-matching results from both search outcomes based on the user’s query.
-
Hybrid Retrieval Setting
+
In this mode, you can specify **"Weight settings"** without needing to configure the Rerank model API, or enable **Rerank model** for retrieval.
@@ -149,7 +149,7 @@ In **Economical Indexing** mode, only the inverted index approach is available.
**TopK:** Determines how many text chunks, deemed most similar to the user’s query, are retrieved. It also automatically adjusts the number of chunks based on the chosen model’s context window. The default value is **3**, and higher numbers will recall more text chunks.
-
Inverted index
+
{% endtab %}
{% endtabs %}
diff --git a/en/guides/knowledge-base/integrate-knowledge-within-application.md b/en/guides/knowledge-base/integrate-knowledge-within-application.md
index 2403f7523..52403804f 100644
--- a/en/guides/knowledge-base/integrate-knowledge-within-application.md
+++ b/en/guides/knowledge-base/integrate-knowledge-within-application.md
@@ -25,11 +25,11 @@ In applications that utilize multiple knowledge bases, it is essential to config
The retriever scans all knowledge bases linked to the application for text content relevant to the user's question. The results are then consolidated. Below is the technical flowchart for the Multi-path Retrieval mode:
-
+
This method simultaneously queries all knowledge bases connected in **"Context"**, seeking relevant text chucks across multiple knowledge bases, collecting all content that aligns with the user's question, and ultimately applying the Rerank strategy to identify the most appropriate content to respond to the user. This retrieval approach offers more comprehensive and accurate results by leveraging multiple knowledge bases simultaneously.
-
+
For instance, in application A, with three knowledge bases K1, K2, and K3. When a user send a question, multiple relevant pieces of content will be retrieved and combined from these knowledge bases. To ensure the most pertinent content is identified, the Rerank strategy is employed to find the content that best relates to the user's query, enhancing the precision and reliability of the results.
@@ -61,7 +61,7 @@ While this method incurs some additional costs, it is more adept at handling com
Dify currently supports multiple Rerank models. To use external Rerank models, you'll need to provide an API Key. Enter the API Key for the Rerank model (such as Cohere, Jina AI, etc.) on the "Model Provider" page.
-
Configuring the Rerank model in the Model Provider
+
**Adjustable Parameters**
@@ -237,7 +237,7 @@ Access **Metadata Filtering** below **Knowledge** (bottom-left). Configuration s
On the left side of the knowledge base, you can see all linked Apps. Hover over the circular icon to view the list of all linked apps. Click the jump button on the right to quickly browser them.
-
Viewing the linked Apps
+
### Frequently Asked Questions
@@ -255,7 +255,7 @@ If the knowledge base is complex, making simple semantic or keyword matches insu
Here's how the knowledge base retrieval method affects Multi-path Retrieval:
-
+
3. **What should I do if I cannot adjust the “Weight Score” when referencing multiple knowledge bases and an error message appears?**
diff --git a/en/guides/knowledge-base/knowledge-and-documents-maintenance.md b/en/guides/knowledge-base/knowledge-and-documents-maintenance.md
index 609b1d06a..15f20e3f1 100644
--- a/en/guides/knowledge-base/knowledge-and-documents-maintenance.md
+++ b/en/guides/knowledge-base/knowledge-and-documents-maintenance.md
@@ -6,7 +6,7 @@ Click the **Knowledge** button at the top of the Dify platform and select the kn
Here, you can modify the knowledge base’s name, description, permissions, indexing method, embedding model and retrieval settings.
-
Knowledge base settings
+
* **Knowledge Name**: Used to distinguish among different knowledge bases.
* **Knowledge Description**: Used to describe the information represented by the documents in the knowledge base.
@@ -26,7 +26,7 @@ Here, you can modify the knowledge base’s name, description, permissions, inde
On the left side of the knowledge base, you can see all linked Apps. Hover over the circular icon to view the list of all linked apps. Click the jump button on the right to quickly browser them.
-
Viewing the Linked Apps
+
You can manage your knowledge base documents either through a web interface or via an API.
@@ -46,4 +46,4 @@ Dify Knowledge Base provides a comprehensive set of standard APIs. Developers ca
[maintain-dataset-via-api.md](knowledge-and-documents-maintenance/maintain-dataset-via-api.md)
{% endcontent-ref %}
-
Knowledge base API management
+
diff --git a/en/guides/knowledge-base/knowledge-and-documents-maintenance/maintain-dataset-via-api.md b/en/guides/knowledge-base/knowledge-and-documents-maintenance/maintain-dataset-via-api.md
index 3cbf51992..9694db586 100644
--- a/en/guides/knowledge-base/knowledge-and-documents-maintenance/maintain-dataset-via-api.md
+++ b/en/guides/knowledge-base/knowledge-and-documents-maintenance/maintain-dataset-via-api.md
@@ -17,7 +17,7 @@ Key advantages include:
Navigate to the knowledge base page, and you can switch to the **API ACCESS** page from the left navigation. On this page, you can view the dataset API documentation provided by Dify and manage the credentials for accessing the dataset API in **API Keys**.
-
Knowledge API Document
+
## API Requesting Examples
diff --git a/en/guides/knowledge-base/knowledge-and-documents-maintenance/maintain-knowledge-documents.md b/en/guides/knowledge-base/knowledge-and-documents-maintenance/maintain-knowledge-documents.md
index 48549a73c..a5f295e22 100644
--- a/en/guides/knowledge-base/knowledge-and-documents-maintenance/maintain-knowledge-documents.md
+++ b/en/guides/knowledge-base/knowledge-and-documents-maintenance/maintain-knowledge-documents.md
@@ -8,7 +8,7 @@ A knowledge base is a collection of documents. Documents can be uploaded by deve
To upload a new document to an existing knowledge base, go to **Knowledge Base** > **Documents** and click **Add File**.
-
Uploading the new documentation on Knowledge Base
+
### Disable / Archive / Delete document
@@ -22,7 +22,7 @@ To upload a new document to an existing knowledge base, go to **Knowledge Base**
> The above options all support batch operations after multiple documents are selected.
-
Batch file Operations
+
**Note:**
@@ -33,7 +33,7 @@ If there are some documents in your knowledge base that haven’t been updated o
You can revert these disable documents and continue using them at any time by clicking the "Enable" button in the knowledge base. Paid users are provided with **one-click revert** function.
-
One-click revert
+
***
@@ -45,11 +45,11 @@ In the knowledge base, each uploaded document is stored as text chunks. By click
Only the first two lines of each content chunk are visible in the preview. If you need to see the full text within a chunk, click the “Expand Chunk” button for a complete view.
-
Expand text chunks
+
You can quickly view all enabled or disabled documents using the filter.
-
Filter text chunks
+
Different [chunking modes](../create-knowledge-and-upload-documents/2.-choose-a-chunk-mode.md) correspond to different text chunking preview methods:
@@ -59,11 +59,11 @@ Different [chunking modes](../create-knowledge-and-upload-documents/2.-choose-a-
Chunks of text in [General mode](../create-knowledge-and-upload-documents.md#general) are independent blocks. If you want to view the complete content of a chunk, click the **full-screen** icon.
-
Full screen viewing
+
Tap the document title at the top to quickly switch to other documents in the knowledge base.
-
General mode - text chunking
+
{% endtab %}
{% tab title="Parent-child Mode" %}
@@ -75,13 +75,13 @@ In[ Parent-child](maintain-knowledge-documents.md#parent-child-chunking-mode) mo
After selecting a document in the knowledge base, you’ll first see the parent chunk content. Parent chunks can be split by **Paragraph** or **Full Doc**, offering a more comprehensive context. The illustration below shows how the text preview differs between these split modes.
-
Difference in preview between paragraph and full doc
+
* **Child chunks**
Child chunks are usually sentences (smaller text blocks) within a paragraph, containing more detailed information. Each chunk displays its character count and the number of times it has been retrieved. Tapping **“Child Chunks”** reveals more details. If you want to see the full content of a chunk, click the full-screen icon in the top-right corner of that chunk to enter full-screen reading mode.
-
Parent-child mode - text chunking
+
{% endtab %}
{% tab title="Q&A Mode (Community Edition Only)" %}
@@ -89,7 +89,7 @@ In[ Parent-child](maintain-knowledge-documents.md#parent-child-chunking-mode) mo
In Q\&A Mode, a content chunk consists of a question and an answer. Click on any document title to view the text chunks.
-
Q&A Mode - check content chunk
+
{% endtab %}
{% endtabs %}
@@ -105,15 +105,15 @@ When checking chunk quality, pay attention to the following situations:
* **Overly short text chunks**, leading to semantic loss;
-
Overly short text chunks
+
* **Overly long text chunks**, leading to semantic noise affecting matching accuracy;
-
Overly long text chunks
+
* **Obvious semantic truncation**, which occurs when using maximum segment length limits, leading to forced semantic truncation and missing content during recall;
-
Obvious semantic truncation
+
***
@@ -129,15 +129,15 @@ You can add text chunks individually to the knowledge base, and different chunki
Click **Add Chunks** in the chunks list page to add one or multiple custom chunks to the document.
-
General mode - Add chunks
+
When manually adding text chunks, you can choose to add both the main content and keywords. After entering the content, select the **“Add another”** checkbox at the bottom to continue adding more text chunks seamlessly.
-
General mode - Add another text chunk
+
To add chunks in bulk, you need to download the upload template in CSV format first and edit all the chunk contents in Excel according to the template format, then save the CSV file and upload it.
-
General mode - Add customize chunks in bulk
+
{% endtab %}
{% tab title="Parent-child Mode" %}
@@ -145,15 +145,15 @@ To add chunks in bulk, you need to download the upload template in CSV format fi
Click Add Chunks in the Chunk list to add one or multiple custom **parent chunks** to the document.
-
Parent-child mode - Add chunks
+
After entering the content, select the **“Add another”** checkbox at the bottom to keep adding more text chunks.
-
Parent-child mode - Add chunks 2
+
You can add child chunks individually under a parent chunk. Click “Add” on the right side of the child chunk within the parent chunk to add it.
-
Parent-child mode - Add child chunks
+
{% endtab %}
{% tab title="Q&A Mode (Community Edition Only)" %}
@@ -175,7 +175,7 @@ You can directly edit or modify the added chunks content, including modifying th
To prevent duplicate edits, an “Edited” tag will appear on the content chunk after it has been modified.
-
Edit text chunks
+
{% endtab %}
{% tab title="Parent-child Mode" %}
@@ -183,7 +183,7 @@ To prevent duplicate edits, an “Edited” tag will appear on the content chunk
A parent chunk contains the content of its child chunks, but they remain independent. You can edit the parent chunk or child chunks separately. Below is a diagram explaining the process of modifying parent and child chunks:
-
Diagram of editing parent-child chunks
+
**To edit a parent chunk:**
@@ -195,13 +195,13 @@ A parent chunk contains the content of its child chunks, but they remain indepen
To prevent duplicate edits, an “Edited” tag will appear on the content chunk after it has been modified.
-
Parent-chid chunks mode - Modify parent chunks
+
**Modify child chunks**: select any child chunks and enter edit mode and save it after modification. The modification will not affect the contents of the parent chunks. Child chunks that have been edited or newly added will be marked with a deep blue label, `C-NUMBER-EDITED`.
You can also treat child chunks as tags for the current parent text block.
-
Parent-child mode - modify child chunks
+
{% endtab %}
{% tab title="Q&A Mode (Community Edition Only)" %}
@@ -209,7 +209,7 @@ You can also treat child chunks as tags for the current parent text block.
In Q\&A chunking mode, each content chunk consists of a question and an answer. Click on the text chunk you wish to edit to modify the question and answer individually. Additionally, you can edit the keywords for the current chunk.
-
Q&A Mode - modify text chunks
+
{% endtab %}
{% endtabs %}
@@ -237,4 +237,8 @@ Go to **Chunk Settings**, adjust the settings, and click **Save & Process** to s
### Metadata
-For more details on metadata, see *[Metadata](https://docs.dify.ai/guides/knowledge-base/metadata)*.
+In addition to capturing metadata (e.g., title, URL, keywords, or a web page description) from various source documents, metadata is also used as structured fields during the chunk retrieval process for filtering or displaying citation sources.
+
+
+
+***
diff --git a/en/guides/knowledge-base/retrieval-test-and-citation.md b/en/guides/knowledge-base/retrieval-test-and-citation.md
index 3f9cf4be9..958497756 100644
--- a/en/guides/knowledge-base/retrieval-test-and-citation.md
+++ b/en/guides/knowledge-base/retrieval-test-and-citation.md
@@ -14,11 +14,11 @@ Enter common user questions into the **Source Text field** and click **Test** to
In **General Mode**, each text chunk stands independently. The score shown in the top-right corner of a chunk represents how closely it matches the query keywords. A higher score indicates a stronger alignment between the chunk and the keywords.
-
General mode - retrieval text chunks
+
Tap a content chunk to see the details of the referenced content. Each chunk shows its source document information at the bottom, letting you verify whether the text chunk is appropriate.
-
Review the details of text chunks
+
{% endtab %}
{% tab title="Parent-child" %}
@@ -28,11 +28,11 @@ Enter typical user questions into the **Source Text** field and click **Test** t
You can click a child chunk to preview its exact content. After the match, the entire parent chunk is recalled to provide more comprehensive information.
-
Retrieval test - Parent-child mode
+
Each chunk displays its source document at the bottom—usually a specific paragraph or sentence. Tap the “Open” button on the right side of the source to view the entire referenced content chunk. Since multiple child chunks can be relevant, this allows you to assess whether the current chunk is appropriate.
-
Check the details of retrieval chunks
+
{% endtab %}
{% endtabs %}
@@ -44,7 +44,7 @@ Click the icon in the upper-right corner of the Source Text field to change the
If you want to permanently modify the retrieval method for the knowledge base, go to **“Knowledge Base Settings”** > **“Retrieval Settings”** to make changes.
-
Retrieval settings
+
**Suggested Steps for Retrieval Testing:**
@@ -63,15 +63,15 @@ If you want to permanently modify the retrieval method for the knowledge base, g
When testing the knowledge base effect within the application, you can go to **Workspace -- Add Feature -- Citation and Attribution** to enable the citation attribution feature.
-
Enable citation and attribution feature
+
After enabling the feature, when the large language model responds to a question by citing content from the knowledge base, you can view specific citation paragraph information below the response content, including **original segment text, segment number, matching degree**, etc. Clicking **Link to Knowledge** above the cited segment allows quick access to the segment list in the knowledge base, facilitating developers in debugging and editing.
-
View citation information in response content
+
#### View Linked Applications in the Knowledge Base
On the left side of the knowledge base, you can see all linked Apps. Hover over the circular icon to view the list of all linked apps. Click the jump button on the right to quickly browser them.
-
Viewing the linked Apps
+
diff --git a/en/guides/knowledge-base/sync-from-website.md b/en/guides/knowledge-base/sync-from-website.md
index 6bd27c32a..1a528ecc0 100644
--- a/en/guides/knowledge-base/sync-from-website.md
+++ b/en/guides/knowledge-base/sync-from-website.md
@@ -14,11 +14,11 @@ The following sections will introduce the usage methods for Firecrawl and Jina R
Click on the avatar in the upper right corner, then go to the **DataSource** page, and click the **Configure** button next to Firecrawl.
-
Configuring Firecrawl credentials
+
Log in to the [Firecrawl website](https://www.firecrawl.dev/) to complete registration, get your API Key, and then enter and save it in Dify.
-
Get the API Key and save it in Dify
+
#### 2. Scrape target webpage
@@ -26,7 +26,7 @@ On the knowledge base creation page, select **Sync from website**, choose Firecr
The configuration options include: Whether to crawl sub-pages, Page crawling limit, Page scraping max depth, Excluded paths, Include only paths, and Content extraction scope. After completing the configuration, click **Run** to preview the parsed pages.
-
Execute scraping
+
#### 3. Review import results
@@ -40,7 +40,7 @@ After importing the parsed text from the webpage, it is stored in the knowledge
Click on the avatar in the upper right corner, then go to the **DataSource** page, and click the **Configure** button next to Jina Reader.
-
Configuring Jina Reader
+
Log in to the [Jina Reader website](https://jina.ai/reader/), complete registration, obtain the API Key, then fill it in and save.
@@ -48,14 +48,14 @@ Log in to the [Jina Reader website](https://jina.ai/reader/), complete registrat
On the knowledge base creation page, select **Sync from website**, choose Jina Reader as the provider, and enter the target URL to be crawled.
-
Web crawling configuration
+
Configuration options include: whether to crawl subpages, maximum number of pages to crawl, and whether to use sitemap for crawling. After completing the configuration, click the **Run** button to preview the page links to be crawled.
-
Executing the crawl process
+
After importing the parsed text from web pages into the knowledge base, you can review the imported results in the documents section. To add more web pages, click the **Add URL** button on the right to continue importing new pages.
-
Importing parsed web text into the knowledge base
+
After crawling is complete, the content from the web pages will be incorporated into the knowledge base.
diff --git a/en/guides/management/app-management.md b/en/guides/management/app-management.md
index fc73b5b0d..752f32bd7 100644
--- a/en/guides/management/app-management.md
+++ b/en/guides/management/app-management.md
@@ -4,7 +4,7 @@
After creating an application, if you want to modify the application name or description, you can click "Edit info" in the upper left corner of the application to revise the application's icon, name, or description.
-
Edit App Info
+
### Duplicating Application
@@ -17,13 +17,13 @@ Applications created in Dify support export in DSL format files, allowing you to
* Click "Export DSL" in the application menu button on the "Studio" page
* After entering the application's orchestration page, click "Export DSL" in the upper left corner
-
+
The DSL file does not include authorization information already filled in [Tool](../workflow/node/tools.md) nodes, such as API keys for third-party services.
If the environment variables contain variables of the `Secret` type, a prompt will appear during file export asking whether to allow the export of this sensitive information.
-
+
{% hint style="info" %}
Dify DSL is an AI application engineering file standard defined by Dify.AI in v0.6 and later. The file format is YML. This standard covers the basic description of the application, model parameters, orchestration configuration, and other information.
diff --git a/en/guides/management/personal-account-management.md b/en/guides/management/personal-account-management.md
index 4860382c5..ade132b7d 100644
--- a/en/guides/management/personal-account-management.md
+++ b/en/guides/management/personal-account-management.md
@@ -8,7 +8,7 @@ The login methods supported by different versions of Dify are as follows:
> Note: For Dify Cloud Service, if the email associated with a GitHub or Google account is the same as the email used to log in with a verification code, the system will automatically link them as the same account, avoiding the need for manual binding and preventing duplicate registrations.
-
+
## Modifying Personal Information
@@ -27,7 +27,7 @@ You can modify the following details:
> Note: The password reset feature is only available in the Community Version.
-
+
### Login Methods
@@ -66,7 +66,7 @@ Deleting your account is irreversible, and the same email address cannot be re-r
Enter the email address you want to delete and the confirmation verification code. Afterward, the system will permanently delete all information related to the account.
-
Delete Personal Account
+
### FAQ
diff --git a/en/guides/management/subscription-management.md b/en/guides/management/subscription-management.md
index 8de5bf478..ead47165e 100644
--- a/en/guides/management/subscription-management.md
+++ b/en/guides/management/subscription-management.md
@@ -10,7 +10,7 @@ After subscribing to Dify's paid services (Professional or Team plan), team owne
On the billing page, you can view the usage statistics for various team resources.
-
Team billing management
+
### Frequently Asked Questions
@@ -21,7 +21,7 @@ Team owners and administrators can navigate to **Settings** → **Billing**, the
* Upgrading from Professional to Team plan requires paying the difference for the current month and takes effect immediately.
* Downgrading from Team to Professional plan takes effect immediately.
-
Changing the paid plan
+
Upon cancellation of the subscription plan, **the team will automatically transition to the Sandbox/Free plan at the end of the current billing cycle**. Subsequently, any team members and resources exceeding the Sandbox/Free plan limitations will become inaccessible.
diff --git a/en/guides/management/team-members-management.md b/en/guides/management/team-members-management.md
index c503e51c3..57b3ad7c7 100644
--- a/en/guides/management/team-members-management.md
+++ b/en/guides/management/team-members-management.md
@@ -14,14 +14,14 @@ Only team owners have permission to invite team members.
To add a member, the team owner can click on the avatar in the upper right corner, then select **"Members"** → **"Add"**. Enter the email address and assign member permissions to complete the process.
-
Assigning permissions to team members
+
> For Community Edition, enabling email functionality requires the team owner to configure and activate the email service via system [environment variables](https://docs.dify.ai/getting-started/install-self-hosted/environments).
- If the invited member has not registered with Dify, they will receive an invitation email. They can complete registration by clicking the link in the email.
- If the invited member is already registered with Dify, permissions will be automatically assigned and **no invitation email will be sent**. The invited member can switch to the new workspace via the menu in the top right corner.
-
+
### Member Permissions
@@ -48,7 +48,7 @@ Only team owners have permission to remove team members.
To remove a member, click on the avatar in the upper right corner of the Dify team homepage, navigate to **"Settings"** → **"Members"**, select the member to be removed, and click **"Remove from team"**.
-
Removing a member
+
### Frequently Asked Questions
diff --git a/en/guides/model-configuration/README.md b/en/guides/model-configuration/README.md
index e24ca394f..1a1b04478 100644
--- a/en/guides/model-configuration/README.md
+++ b/en/guides/model-configuration/README.md
@@ -6,7 +6,7 @@ description: Learn about the Different Models Supported by Dify.
Dify is a development platform for AI application based on LLM Apps, when you are using Dify for the first time, you need to go to **Settings --> Model Providers** to add and configure the LLM you are going to use.
-
Settings - Model Provider
+
Dify supports major model providers like OpenAI's GPT series and Anthropic's Claude series. Each model's capabilities and parameters differ, so select a model provider that suits your application's needs. **Obtain the API key from the model provider's official website before using it in Dify.**
@@ -39,13 +39,13 @@ Dify offers trial quotas for cloud service users to experiment with different mo
Dify automatically selects the default model based on usage. Configure this in `Settings > Model Provider`.
-
+
## Model Integration Settings
Choose your model in Dify's `Settings > Model Provider`.
-
+
Model providers fall into two categories:
@@ -73,4 +73,4 @@ Specific integration methods are not detailed here.
Once configured, these models are ready for application use.
-
+
diff --git a/en/guides/model-configuration/customizable-model.md b/en/guides/model-configuration/customizable-model.md
index 427097261..50e538489 100644
--- a/en/guides/model-configuration/customizable-model.md
+++ b/en/guides/model-configuration/customizable-model.md
@@ -8,7 +8,7 @@ It is important to note that for custom models, each model integration requires
Unlike predefined models, custom vendor integration will always have the following two parameters, which do not need to be defined in the vendor YAML file.
-
+
In the previous section, we have learned that vendors do not need to implement `validate_provider_credential`. The Runtime will automatically call the corresponding model layer's `validate_credentials` based on the model type and model name selected by the user for validation.
diff --git a/en/guides/model-configuration/load-balancing.md b/en/guides/model-configuration/load-balancing.md
index da6b5c769..80bb3c713 100644
--- a/en/guides/model-configuration/load-balancing.md
+++ b/en/guides/model-configuration/load-balancing.md
@@ -6,7 +6,7 @@ In enterprise-level large-scale model API calls, high concurrent requests can ex
You can enable this feature by navigating to **Model Provider -- Model List -- Configure Model Load Balancing** and adding multiple credentials (API keys) for the same model.
-
Model Load Balancing
+
{% hint style="info" %}
Model load balancing is a paid feature. You can enable it by [subscribing to SaaS paid services](../../getting-started/cloud.md#subscription-plan) or purchasing the enterprise edition.
@@ -14,17 +14,17 @@ Model load balancing is a paid feature. You can enable it by [subscribing to Saa
The default API key is the credential added when initially configuring the model provider. You need to click **Add Configuration** to add different API keys for the same model to use the load balancing feature properly.
-
Configuring Load Balancing
+
**At least one additional model credential** must be added to save and enable load balancing.
You can also **temporarily disable** or **delete** configured credentials.
-
+
Once configured, all models with load balancing enabled will be displayed in the model list.
-
Enabling Load Balancing
+
{% hint style="info" %}
By default, load balancing uses the Round-robin strategy. If the rate limit is triggered, a 1-minute cooldown period will be applied.
@@ -32,4 +32,4 @@ By default, load balancing uses the Round-robin strategy. If the rate limit is t
You can also configure load balancing from **Add Model**, following the same process as above.
-
Configuring Load Balancing from Add Model
+
diff --git a/en/guides/monitoring/README.md b/en/guides/monitoring/README.md
index 54ad0ca3f..ed325eca7 100644
--- a/en/guides/monitoring/README.md
+++ b/en/guides/monitoring/README.md
@@ -2,4 +2,4 @@
You can monitor and track the performance of your application in a production environment within the **Overview** section. In the data analytics dashboard, you can analyze various metrics such as usage costs, latency, user feedback, and performance in the production environment. By continuously debugging and iterating, you can continually improve your application.
-
概览
+
diff --git a/en/guides/monitoring/analysis.md b/en/guides/monitoring/analysis.md
index c7776666f..534d3a4fd 100644
--- a/en/guides/monitoring/analysis.md
+++ b/en/guides/monitoring/analysis.md
@@ -2,7 +2,7 @@
The **Overview -- Data Analysis** section displays metrics such as usage, active users, and LLM (Language Learning Model) invocation costs. This allows you to continuously improve the effectiveness, engagement, and cost-efficiency of your application operations. We will gradually provide more useful visualization capabilities, so please let us know what you need.
-
Overview—Data Analysis
+
***
diff --git a/en/guides/monitoring/integrate-external-ops-tools/integrate-langfuse.md b/en/guides/monitoring/integrate-external-ops-tools/integrate-langfuse.md
index 4b40510a6..d6d9964e8 100644
--- a/en/guides/monitoring/integrate-external-ops-tools/integrate-langfuse.md
+++ b/en/guides/monitoring/integrate-external-ops-tools/integrate-langfuse.md
@@ -15,35 +15,35 @@ Introduction to Langfuse: [https://langfuse.com/](https://langfuse.com/)
1. Register and log in to Langfuse on the [official website](https://langfuse.com/)
2. Create a project in Langfuse. After logging in, click **New** on the homepage to create your own project. The **project** will be used to associate with **applications** in Dify for data monitoring.
-
Create a project in Langfuse
+
Edit a name for the project.
-
Create a project in Langfuse
+
3. Create project API credentials. In the left sidebar of the project, click **Settings** to open the settings.
-
Create project API credentials
+
In Settings, click **Create API Keys** to create project API credentials.
-
Create project API credentials
+
Copy and save the **Secret Key**, **Public Key**, and **Host**.
-
Get API Key configuration
+
4. Configure Langfuse in Dify. Open the application you need to monitor, open **Monitoring** in the side menu, and select **Tracing app performance** on the page.
-
Configure Langfuse
+
After clicking configure, paste the **Secret Key, Public Key, Host** created in Langfuse into the configuration and save.
-
Configure Langfuse
+
Once successfully saved, you can view the status on the current page. If it shows as started, it is being monitored.
-
View configuration status
+
***
@@ -51,9 +51,9 @@ Once successfully saved, you can view the status on the current page. If it show
After configuration, debugging or production data of the application in Dify can be viewed in Langfuse.
-
Debugging Applications in Dify
+
-
Viewing application data in Langfuse
+
***
diff --git a/en/guides/monitoring/integrate-external-ops-tools/integrate-langsmith.md b/en/guides/monitoring/integrate-external-ops-tools/integrate-langsmith.md
index 822b3aee3..7f1be46e3 100644
--- a/en/guides/monitoring/integrate-external-ops-tools/integrate-langsmith.md
+++ b/en/guides/monitoring/integrate-external-ops-tools/integrate-langsmith.md
@@ -18,39 +18,39 @@ For more details, please refer to [LangSmith](https://www.langchain.com/langsmit
Create a project in LangSmith. After logging in, click **New Project** on the homepage to create your own project. The **project** will be used to associate with **applications** in Dify for data monitoring.
-
Create a project in LangSmith
+
Once created, you can view all created projects in the Projects section.
-
View created projects in LangSmith
+
#### 3. Create Project Credentials
Find the project settings **Settings** in the left sidebar.
-
Project settings
+
Click **Create API Key** to create project credentials.
-
Create a project API Key
+
Select **Personal Access Token** for subsequent API authentication.
-
Create an API Key
+
Copy and save the created API key.
-
Copy API Key
+
#### 4. Integrating LangSmith with Dify
Configure LangSmith in the Dify application. Open the application you need to monitor, open **Monitoring** in the side menu, and select **Tracing app performance** on the page.
-
Tracing app performance
+
After clicking configure, paste the **API Key** and **project name** created in LangSmith into the configuration and save.
-
Configure LangSmith
+
{% hint style="info" %}
The configured project name needs to match the project set in LangSmith. If the project names do not match, LangSmith will automatically create a new project during data synchronization.
@@ -58,21 +58,21 @@ The configured project name needs to match the project set in LangSmith. If the
Once successfully saved, you can view the monitoring status on the current page.
-
View configuration status
+
### Viewing Monitoring Data in LangSmith
Once configured, the debug or production data from applications within Dify can be monitored in LangSmith.
-
Debugging Applications in Dify
+
When you switch to LangSmith, you can view detailed operation logs of Dify applications in the dashboard.
-
Viewing application data in LangSmith
+
Detailed LLM operation logs through LangSmith will help you optimize the performance of your Dify application.
-
Viewing application data in LangSmith
+
### Monitoring Data List
diff --git a/en/guides/monitoring/integrate-external-ops-tools/integrate-opik.md b/en/guides/monitoring/integrate-external-ops-tools/integrate-opik.md
index 7ef92cc31..2a3241b52 100644
--- a/en/guides/monitoring/integrate-external-ops-tools/integrate-opik.md
+++ b/en/guides/monitoring/integrate-external-ops-tools/integrate-opik.md
@@ -18,17 +18,17 @@ For more details, please refer to [Opik](https://www.comet.com/site/products/opi
Retrieve your Opik API Key from the user menu at the top-right. Click on **API Key**, then on the API Key to copy it:
-
Opik API Key
+
#### 3. Integrating Opik with Dify
Configure Opik in the Dify application. Open the application you need to monitor, open **Monitoring** in the side menu, and select **Tracing app performance** on the page.
-
Tracing app performance
+
After clicking configure, paste the **API Key** and **project name** created in Opik into the configuration and save.
-
Configure Opik
+
Once successfully saved, you can view the monitoring status on the current page.
@@ -36,15 +36,15 @@ Once successfully saved, you can view the monitoring status on the current page.
Once configured, you can debug or use the Dify application as usual. All usage history can be monitored in Opik.
-
Viewing application data in Opik
+
When you switch to Opik, you can view detailed operation logs of Dify applications in the dashboard.
-
Viewing application data in Opik
+
Detailed LLM operation logs through Opik will help you optimize the performance of your Dify application.
-
Viewing application data in Opik
+
### Monitoring Data List
diff --git a/en/guides/tools/README.md b/en/guides/tools/README.md
index 1b5786ade..1cbba073e 100644
--- a/en/guides/tools/README.md
+++ b/en/guides/tools/README.md
@@ -17,7 +17,7 @@ You can directly use the first-party built-in tools provided by the Dify ecosyst
### How to Configure First-party Tools
-
First-party Tools List
+
Dify currently supports:
@@ -31,7 +31,7 @@ We welcome you to contribute your developed tools to Dify. For detailed methods
If you need to use the first-party built-in tools provided by the Dify ecosystem, you need to configure the corresponding credentials before using them.
-
Configure First-party Tool Credentials
+
Once the credentials are successfully verified, the tool will display an "Authorized" status. After configuring the credentials, all members in the workspace can use this tool when arranging applications.
@@ -41,15 +41,15 @@ You can import custom API tools in the "Tools - Custom Tools" section, currently
Currently, tools support two authentication methods: No Authentication and API Key.
-
Create Custom Tools
+
After importing the schema content, the system will automatically parse the parameters in the file, and you can preview the specific parameters, methods, and paths of the tool. You can also test the tool parameters here.
-
Custom Tool Parameter Testing
+
Once the custom tool is created, all members in the workspace can use this tool when arranging applications in the "Studio."
-
Custom Tool Added
+
#### Cloudflare Workers
@@ -62,8 +62,8 @@ You can also use [dify-tools-worker](https://github.com/crazywoola/dify-tools-wo
Currently, you can use the configured tools when creating **intelligent assistant applications** in the "Studio."
-
Add Tools When Creating Intelligent Assistant Applications
+
For example, after adding tools in a financial analysis application, the intelligent assistant will autonomously invoke tools when needed to query financial report data, analyze the data, and complete the conversation with the user.
-
Intelligent Assistant Using Tools to Answer Questions During Conversation
+
diff --git a/en/guides/tools/quick-tool-integration.md b/en/guides/tools/quick-tool-integration.md
index ee33e04bb..7b97259fa 100644
--- a/en/guides/tools/quick-tool-integration.md
+++ b/en/guides/tools/quick-tool-integration.md
@@ -279,4 +279,4 @@ After the above steps are completed, we can see this tool on the frontend, and i
Of course, because google\_search needs a credential, before using it, you also need to input your credentials on the frontend.
-
+
diff --git a/en/guides/tools/tool-configuration/bing.md b/en/guides/tools/tool-configuration/bing.md
index ff38362c7..6cbb7c6ab 100644
--- a/en/guides/tools/tool-configuration/bing.md
+++ b/en/guides/tools/tool-configuration/bing.md
@@ -16,7 +16,7 @@ Please apply for an API Key on the [Azure platform](https://www.microsoft.com/en
In the Dify navigation page, click `Tools > Azure > Authorize` to fill in the API Key.
-
+
## 3. Use the tool
diff --git a/en/guides/tools/tool-configuration/comfyui.md b/en/guides/tools/tool-configuration/comfyui.md
index 68cf015e6..8e445360b 100644
--- a/en/guides/tools/tool-configuration/comfyui.md
+++ b/en/guides/tools/tool-configuration/comfyui.md
@@ -13,10 +13,10 @@ Please refer to its [official documentation](https://docs.comfy.org/get_started/
## 2. Prompt setting
If you don't need dify to pass in the prompt, you can skip this step. If your prompt node is connected to the only `KSampler` node in ComfyUI, you can skip this step.
Otherwise, use the string `{{positive_prompt}}` to replace the positive prompt content, and `{{negative_prompt}}` to replace the negative prompt content.
-
+
## 3. Export the API file of the workflow.
-
+
As shown in the figure, select `Save(API Format)`, if there is no such selection, you need to enable `Dev Mode` in the settings.
## 4. Integrate ComfyUI in Dify
diff --git a/en/guides/tools/tool-configuration/dall-e.md b/en/guides/tools/tool-configuration/dall-e.md
index 1d0bd67fa..95504573b 100644
--- a/en/guides/tools/tool-configuration/dall-e.md
+++ b/en/guides/tools/tool-configuration/dall-e.md
@@ -16,7 +16,7 @@ Please apply for an API Key at [OpenAI Platform](https://platform.openai.com/),
In the Dify navigation page, click `Tools > DALL-E > Authorize` and fill in the API Key.
-
+
## 3. Use the tool
diff --git a/en/guides/tools/tool-configuration/google.md b/en/guides/tools/tool-configuration/google.md
index d5560fb3d..5578ba590 100644
--- a/en/guides/tools/tool-configuration/google.md
+++ b/en/guides/tools/tool-configuration/google.md
@@ -16,7 +16,7 @@ Please apply for an API Key on the [Serp](https://serpapi.com/dashboard).
In the Dify navigation page, click `Tools > Google > Go to authorize` to fill in the API Key.
-
+
## 3. Using the tool
diff --git a/en/guides/tools/tool-configuration/perplexity.md b/en/guides/tools/tool-configuration/perplexity.md
index a56b40411..23680fb67 100644
--- a/en/guides/tools/tool-configuration/perplexity.md
+++ b/en/guides/tools/tool-configuration/perplexity.md
@@ -16,7 +16,7 @@ Please apply for an API Key at [Perplexity](https://www.perplexity.ai/settings/a
In the Dify navigation page, click on `Tools > Perplexity > Go to authorize` to fill in the API Key.
-
+
## 3. Using the tool
@@ -26,10 +26,10 @@ You can use the Perplexity Search tool in the following application types.
Both Chatflow and Workflow applications support adding Perplexity tool nodes. Pass the user's input content through variables to the "Query" box in the Perplexity tool node, adjust the built-in parameters of the Perplexity tool as needed, and finally select the output content of the Perplexity tool node in the response box of the "End" node.
-
+
* **Agent applications**
Add the `Perplexity Search` tool in the Agent application, then enter relevant commands to invoke this tool.
-
+
diff --git a/en/guides/tools/tool-configuration/searchapi.md b/en/guides/tools/tool-configuration/searchapi.md
index 59fa23e69..e23a813aa 100644
--- a/en/guides/tools/tool-configuration/searchapi.md
+++ b/en/guides/tools/tool-configuration/searchapi.md
@@ -16,7 +16,7 @@ Please apply for an API Key at [SearchApi](https://www.searchapi.io/).
In the Dify navigation page, click on `Tools > SearchApi > Go to authorize` to fill in the API Key.
-
+
## 3. Using the tool
@@ -26,7 +26,7 @@ You can use the SearchApi tool in the following application types.
Both Chatflow and Workflow applications support adding `SearchApi` series tool nodes, providing four tools: Google Jobs API, Google News API, Google Search API, and YouTube Scraper API.
-
+
* **Agent applications**
diff --git a/en/guides/tools/tool-configuration/serper.md b/en/guides/tools/tool-configuration/serper.md
index 9787f17fb..3eb4b131e 100644
--- a/en/guides/tools/tool-configuration/serper.md
+++ b/en/guides/tools/tool-configuration/serper.md
@@ -16,7 +16,7 @@ Please apply for an API Key on the [Serper platform](https://serper.dev/signup).
In the Dify navigation page, click `Tools > Serper > Authorize` to fill in the API Key.
-
+
## 3. Using the tool
diff --git a/en/guides/tools/tool-configuration/siliconflow.md b/en/guides/tools/tool-configuration/siliconflow.md
index 90a99432f..a3b37bfb7 100644
--- a/en/guides/tools/tool-configuration/siliconflow.md
+++ b/en/guides/tools/tool-configuration/siliconflow.md
@@ -16,7 +16,7 @@ Create a new API Key on the [SiliconCloud API management page](https://cloud.sil
In the Dify tool page, click on `SiliconCloud > To Authorize` and fill in the API Key.
-
+
## 3. Using the Tool
@@ -24,12 +24,12 @@ In the Dify tool page, click on `SiliconCloud > To Authorize` and fill in the AP
Chatflow and Workflow applications both support adding `SiliconFlow` tool nodes. You can pass user input content to the SiliconFlow tool node's "prompt" and "negative prompt" boxes through [variables](https://docs.dify.ai/v/zh-hans/guides/workflow/variables), adjust the built-in parameters as needed, and finally select the output content (text, images, etc.) of the SiliconFlow tool node in the "end" node's reply box.
-
+
* **Agent Application**
In the Agent application, add the `Stable Diffusion` or `Flux` tool, and then send a picture description in the conversation box to call the tool to generate images.
-
+
-
+
diff --git a/en/guides/tools/tool-configuration/youtube.md b/en/guides/tools/tool-configuration/youtube.md
index 252d59777..87d62917d 100644
--- a/en/guides/tools/tool-configuration/youtube.md
+++ b/en/guides/tools/tool-configuration/youtube.md
@@ -32,10 +32,10 @@ You can use the Youtube tool in the following application types.
Both Chatflow and Workflow applications support adding `Video statistics` nodes.
-
+
* **Agent applications**
Add the `Free YouTube Transcript API` tool in the Agent application, then enter relevant commands to invoke this tool.
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/en/guides/workflow/README.md b/en/guides/workflow/README.md
index b5ea838d3..304bab72f 100644
--- a/en/guides/workflow/README.md
+++ b/en/guides/workflow/README.md
@@ -9,7 +9,7 @@ Dify workflows are divided into two types:
* **Chatflow**: Designed for conversational scenarios, including customer service, semantic search, and other conversational applications that require multi-step logic in response construction.
* **Workflow**: Geared towards automation and batch processing scenarios, suitable for high-quality translation, data analysis, content generation, email automation, and more.
-
+
To address the complexity of user intent recognition in natural language input, Chatflow provides question understanding nodes. Compared to Workflow, it adds support for Chatbot features such as conversation history (Memory), annotated replies, and Answer nodes.
diff --git a/en/guides/workflow/additional-features.md b/en/guides/workflow/additional-features.md
index bd0657fac..ed83a43fb 100644
--- a/en/guides/workflow/additional-features.md
+++ b/en/guides/workflow/additional-features.md
@@ -55,7 +55,7 @@ This section will mainly introduce the specific usage of the **File Upload** fea
**For application users:** Chatflow applications with file upload enabled will display a "paperclip" icon on the right side of the dialogue box. Click it to upload files and interact with the LLM.
-
Upload file
+
**For application developers:**
@@ -84,7 +84,7 @@ The orchestration steps are as follows:
2. Add an LLM node, enable the VISION feature, and select the `sys.files` variable.
3. Add a "Answer" node at the end, filling in the output variable of the LLM node.
-
Enable vision
+
* **Mixed File Types**
@@ -97,7 +97,7 @@ If you want the application to have the ability to process both document files a
After the application user uploads both document files and images, document files are automatically diverted to the document extractor node, and image files are automatically diverted to the LLM node to achieve joint processing of files.
-
Mixed File Types
+
* **Audio and Video Files**
diff --git a/en/guides/workflow/bulletin.md b/en/guides/workflow/bulletin.md
index 2ae823dbe..69fa63570 100644
--- a/en/guides/workflow/bulletin.md
+++ b/en/guides/workflow/bulletin.md
@@ -4,11 +4,11 @@ The image upload feature has been integrated into the more comprehensive [File U
* The image upload option in Chatflow’s “Features” has been removed and replaced by the new “File Upload” feature. Within the “File Upload” feature, you can select the image file type. Additionally, the image upload icon in the application dialog has been replaced with a file upload icon.
-
+
* The image upload option in Workflow’s “Features” and the `sys.files` [variable](variables.md) will be deprecated in the future. Both have been marked as `LEGACY`, and developers are encouraged to use custom file variables to add file upload functionality to Workflow applications.
-
+
### Why Replace the “Image Upload” Feature?
@@ -25,7 +25,7 @@ To enhance the information processing capabilities of your applications, we have
* The file upload feature allows files to be uploaded, parsed, referenced, and downloaded as file variables within Workflow applications.
* Developers can now easily build applications capable of understanding and processing complex tasks involving images, audio, and video.
-
+
We no longer recommend using the standalone “Image Upload” feature and instead suggest transitioning to the more comprehensive “File Upload” feature to improve the application experience.
@@ -38,11 +38,11 @@ We no longer recommend using the standalone “Image Upload” feature and inste
If you have already created Chatflow applications with the “Image Upload” feature enabled and activated the Vision feature in the LLM node, the system will automatically switch the feature, and it will not affect the application’s image upload capability. If you need to update and republish the application, select the file variable in the Vision variable selection box of the LLM node, clear the item from the checklist, and republish the application.\
-
+
If you wish to add the “Image Upload” feature to a Chatflow application, enable “File Upload” in the features and select only the “image” file type. Then enable the Vision feature in the LLM node and specify the sys.files variable. The upload entry will appear as a “paperclip” icon. For detailed instructions, refer to [Additional Features](additional-features.md).
-
+
* **Workflow Applications**
diff --git a/en/guides/workflow/debug-and-preview/checklist.md b/en/guides/workflow/debug-and-preview/checklist.md
index c5039ffe3..902f5f79f 100644
--- a/en/guides/workflow/debug-and-preview/checklist.md
+++ b/en/guides/workflow/debug-and-preview/checklist.md
@@ -2,4 +2,4 @@
Before publishing the App, you can check the checklist to see if there are any nodes with incomplete configurations or that have not been connected.
-
+
diff --git a/en/guides/workflow/debug-and-preview/history.md b/en/guides/workflow/debug-and-preview/history.md
index 7bd9a8273..470fdf80d 100644
--- a/en/guides/workflow/debug-and-preview/history.md
+++ b/en/guides/workflow/debug-and-preview/history.md
@@ -2,4 +2,4 @@
In the "Run History," you can view the run results and log information from the historical debugging of the current workflow.
-
+
diff --git a/en/guides/workflow/debug-and-preview/log.md b/en/guides/workflow/debug-and-preview/log.md
index e98aa849a..7b7fb8230 100644
--- a/en/guides/workflow/debug-and-preview/log.md
+++ b/en/guides/workflow/debug-and-preview/log.md
@@ -4,4 +4,4 @@ Clicking **"Run History - View Log — Details"** allows you to see a comprehens
This detailed information enables you to review various aspects of each node throughout the complete execution process of the workflow. You can examine inputs and outputs, analyze token consumption, evaluate runtime duration, and assess other pertinent metrics.
-
+
diff --git a/en/guides/workflow/debug-and-preview/step-run.md b/en/guides/workflow/debug-and-preview/step-run.md
index a6fc85079..aff0d5ea7 100644
--- a/en/guides/workflow/debug-and-preview/step-run.md
+++ b/en/guides/workflow/debug-and-preview/step-run.md
@@ -2,8 +2,8 @@
Workflow supports step-by-step debugging of nodes, where you can repetitively test whether the execution of the current node meets expectations.
-
+
After running a step test, you can review the execution status, input/output, and metadata information.
-
+
diff --git a/en/guides/workflow/debug-and-preview/yu-lan-yu-yun-hang.md b/en/guides/workflow/debug-and-preview/yu-lan-yu-yun-hang.md
index 0a210aded..af5c6d900 100644
--- a/en/guides/workflow/debug-and-preview/yu-lan-yu-yun-hang.md
+++ b/en/guides/workflow/debug-and-preview/yu-lan-yu-yun-hang.md
@@ -2,10 +2,10 @@
Dify Workflow offers a comprehensive set of execution and debugging features. In conversational applications, clicking "Preview" enters debugging mode.
-
+
In workflow applications, clicking "Run" enters debugging mode.
-
+
Once in debugging mode, you can debug the configured workflow using the interface on the right side of the screen.
diff --git a/en/guides/workflow/error-handling/README.md b/en/guides/workflow/error-handling/README.md
index 7fab59b24..92836acde 100644
--- a/en/guides/workflow/error-handling/README.md
+++ b/en/guides/workflow/error-handling/README.md
@@ -96,7 +96,7 @@ In the new LLM node, fill in the prompt and reference the exception output from
Add a variable aggregation node to consolidate the results from both the correct and error branches and output them to the end node, completing the entire process.
-
+
> Click [here](https://assets-docs.dify.ai/2024/12/087861aa20e06bb4f8a2bef7e7ae0522.yml) to download the Demo DSL file.
diff --git a/en/guides/workflow/error-handling/error-type.md b/en/guides/workflow/error-handling/error-type.md
index 9f1b0cbcc..48e375d9c 100644
--- a/en/guides/workflow/error-handling/error-type.md
+++ b/en/guides/workflow/error-handling/error-type.md
@@ -19,7 +19,7 @@ This article summarizes the potential exceptions and corresponding error types t
This error occurs due to exceptions in developer-written code, such as: missing variables, calculation logic errors, or treating string array inputs as string variables. You can locate the issue using the error message and exact line number.
-
Code Error
+
2. **Sandbox Network Issues (System Error)**
@@ -29,19 +29,19 @@ This article summarizes the potential exceptions and corresponding error types t
2. Start the sandbox service
3. Verify proxy settings
-
Sandbox network issues
+
3. **Depth Limit Error (DepthLimitError)**
The current node's default configuration only supports up to 5 levels of nested structures. An error will occur if it exceeds 5 levels.
-
OutputValidationError
+
4. **Output Validation Error (OutputValidationError)**
An error occurs when the actual output variable type doesn't match the selected output variable type. Developers need to change the selected output variable type to avoid this issue.
-
+
### LLM Node
@@ -53,7 +53,7 @@ Here are 6 common runtime errors:
This error occurs when the LLM cannot find system prompts or variables set in the context. Application developers can resolve this by replacing the problematic variables.
-
+
2. **Invalid Context Structure Error (InvalidContextStructureError)**
@@ -61,7 +61,7 @@ Here are 6 common runtime errors:
> Context only supports string (String) data structures.
-
InvalidContextStructureError
+
3. **Invalid Variable Type Error (InvalidVariableTypeError)**
@@ -76,7 +76,7 @@ Here are 6 common runtime errors:
An error occurs when the LLM node's prompt is empty, as prompts cannot be blank.
-
+
### HTTP
@@ -104,7 +104,7 @@ The following 3 errors commonly occur during runtime:
-
+ 
2. **Tool Parameter Error (ToolParameterError)**
An error occurs when the configured tool node parameters are invalid, such as passing parameters that don't match the tool node's defined parameters.
diff --git a/en/guides/workflow/error-handling/predefined-error-handling-logic.md b/en/guides/workflow/error-handling/predefined-error-handling-logic.md
index 7d0f66970..40c6debaf 100644
--- a/en/guides/workflow/error-handling/predefined-error-handling-logic.md
+++ b/en/guides/workflow/error-handling/predefined-error-handling-logic.md
@@ -18,7 +18,7 @@ The error handling feature provides three predefined options:
• **Fail branch**: When an error occurs, a predefined error-handling branch is executed. This provides flexibility for developers to create alternative paths in the workflow to address the failure scenario.
-
+
### Logic: None
@@ -37,7 +37,7 @@ When a node fails to execute, the workflow automatically uses the developer’s
The predefined default value’s data type must match the node’s output variable type. For example, if the output variable of a code node is set to the data type `array[number]`, the default value must also be of the `array[number]` data type.
-
Error handling: default value
+
### Logic: Fail Branch
@@ -47,7 +47,7 @@ For instance, you might connect a mail tool node to send error notifications, pr
> The fail branch is highlighted with orange.
-
+
A common approach to handling errors is enable fail branch on nodes. These nodes can address issues, and the corrected outputs can be merged back into the main flow by using variable aggregation nodes to ensure consistency in the final results.
diff --git a/en/guides/workflow/export_import.md b/en/guides/workflow/export_import.md
index be61ea2e6..d98bd1645 100644
--- a/en/guides/workflow/export_import.md
+++ b/en/guides/workflow/export_import.md
@@ -4,12 +4,12 @@ You can export/import application templates as YAML-format DSL (Domain Specific
To import a DSL file in the studio application list:
-
+ (2).png)
To export a DSL file from the studio application list:
-
+ (1).png)
To export a DSL file from the workflow orchestration page:
-
+ (1).png)
diff --git a/en/guides/workflow/file-upload.md b/en/guides/workflow/file-upload.md
index 649282382..a472c99e7 100644
--- a/en/guides/workflow/file-upload.md
+++ b/en/guides/workflow/file-upload.md
@@ -68,7 +68,7 @@ Some LLMs, such as [Claude 3.5 Sonnet](https://docs.anthropic.com/en/docs/build-
1. Click the **"Features"** button in the upper right corner of the Chatflow application to add more functionality to the application. After enabling this feature, application users can upload and update files at any time during the application dialogue. A maximum of 10 files can be uploaded simultaneously, with a size limit of 15MB per file.
-
file upload
+
Enabling this feature does not grant LLMs the ability to directly read files. A **Document Extractor** is still needed to parse documents into text for LLM comprehension.
@@ -79,11 +79,11 @@ Enabling this feature does not grant LLMs the ability to directly read files. A
3. Add an LLM node and select the output variable of the Document Extractor node in the system prompt.
4. Add an "Answer" node at the end, filling in the output variable of the LLM node.
-
+
Once enabled, users can upload files and engage in conversations in the dialogue box. However, with this method, the LLM application does not have the ability to remember file contents, and files need to be uploaded for each conversation.
-
+
If you want the LLM to remember file contents during conversations, please refer to Method 3.
@@ -125,11 +125,11 @@ After uploading, files are stored in single file variables, which LLMs cannot di
Use the file variable from the "Start" node as the input variable for the **"Document Extractor"** node.
-
Document Extractor
+
Fill in the output variable of the "Document Extractor" node in the system prompt of the LLM node.
-
+
After completing these settings, application users can paste file URLs or upload local files in the WebApp, then interact with the LLM about the document content. Users can replace files at any time during the conversation, and the LLM will obtain the latest file content.
@@ -143,7 +143,7 @@ For certain file types (such as images), file variables can be directly used wit
Below is an example configuration:
-
Using file variables directly in LLM node
+
It's important to note that when directly using file variables in LLM node, the developers need to ensure that the file variable contains only image files; otherwise, errors may occur. If users might upload different types of files, we need to use list operator node for filtering files.
@@ -151,7 +151,7 @@ It's important to note that when directly using file variables in LLM node, the
Placing file variables in answer nodes or end nodes will provide a file download card in the conversation box when the application reaches that node. Clicking the card allows for file download.
-
file download
+
## Advanced Usage
diff --git a/en/guides/workflow/node/agent.md b/en/guides/workflow/node/agent.md
index f0a91ddd1..21416aba5 100644
--- a/en/guides/workflow/node/agent.md
+++ b/en/guides/workflow/node/agent.md
@@ -10,17 +10,17 @@ An Agent Node is a component in Dify Chatflow/Workflow that enables autonomous t
In the Dify Chatflow/Workflow editor, drag the Agent node from the components panel onto the canvas.
-
+
### Select an Agent Strategy
In the node configuration panel, click Agent Strategy.
-
+
From the dropdown menu, select the desired Agent reasoning strategy. Dify provides two built-in strategies, **Function Calling and ReAct**, which can be installed from the **Marketplace → Agent Strategies category**.
-
+
#### 1. Function Calling
@@ -34,7 +34,7 @@ Pros:
**• Structured output:** The model outputs structured information about function calls, facilitating processing by downstream nodes.
-
+
#### 2. ReAct (Reason + Act)
@@ -48,7 +48,7 @@ Pros:
**• Wide applicability:** Suitable for scenarios that require external knowledge or need to perform specific actions, such as Q\&A, information retrieval, and task execution.
-
+
Developers can contribute Agent strategy plugins to the public [repository](https://github.com/langgenius/dify-plugins). After review, these plugins will be listed in the Marketplace for others to install.
@@ -66,10 +66,10 @@ After choosing the Agent strategy, the configuration panel will display the rele
5. **Maximum Iterations:** Set the maximum number of execution steps for the Agent.
6. **Output Variables:** Indicates the data structure output by the node.
-
+
## Logs
During execution, the Agent node generates detailed logs. You can see overall node execution information—including inputs and outputs, token usage, time spent, and status. Click Details to view the output from each round of Agent strategy execution.
-
+
diff --git a/en/guides/workflow/node/answer.md b/en/guides/workflow/node/answer.md
index d0c545e74..ecd5bec68 100644
--- a/en/guides/workflow/node/answer.md
+++ b/en/guides/workflow/node/answer.md
@@ -10,10 +10,10 @@ Answer node can be seamlessly integrated at any point to dynamically deliver con
Example 1: Output plain text.
-
+
Example 2: Output image and LLM reply.
-
+
-
+
diff --git a/en/guides/workflow/node/code.md b/en/guides/workflow/node/code.md
index dd48fc01e..6e02befe0 100644
--- a/en/guides/workflow/node/code.md
+++ b/en/guides/workflow/node/code.md
@@ -13,7 +13,7 @@ The code node supports running Python/NodeJS code to perform data transformation
This node significantly enhances the flexibility for developers, allowing them to embed custom Python or JavaScript scripts within the workflow and manipulate variables in ways that preset nodes cannot achieve. Through configuration options, you can specify the required input and output variables and write the corresponding execution code:
-
+
## Configuration
@@ -115,5 +115,5 @@ This code snippet has the following issues:
Dangerous code will be automatically blocked by Cloudflare WAF. You can check if it's been blocked by looking at the "Network" tab in your browser's "Web Developer Tools".
-
Cloudflare WAF
+
diff --git a/en/guides/workflow/node/doc-extractor.md b/en/guides/workflow/node/doc-extractor.md
index f934365d3..c0bc2aa94 100644
--- a/en/guides/workflow/node/doc-extractor.md
+++ b/en/guides/workflow/node/doc-extractor.md
@@ -13,7 +13,7 @@ LLMs cannot directly read or interpret document contents. Therefore, it's necess
The document extractor node can be understood as an information processing center. It recognizes and reads files in the input variables, extracts information, and converts it into string-type output variables for downstream nodes to call.
-
doc extractor
+
The document extractor node structure is divided into input variables and output variables.
@@ -41,7 +41,7 @@ In a typical file interaction Q\&A scenario, the document extractor can serve as
This section will introduce the usage of the document extractor node through a typical ChatPDF example workflow template.
-
Chatpdf workflow
+
**Configuration Process:**
@@ -49,11 +49,11 @@ This section will introduce the usage of the document extractor node through a t
2. Add a document extractor node and select the `pdf` variable in the input variables.
3. Add an LLM node and select the output variable of the document extractor node in the system prompt. The LLM can read the contents of the file through this output variable.
-
+
Configure the end node by selecting the output variable of the LLM node in the end node.
-
chat with pdf
+
After configuration, the application will have file upload functionality, allowing users to upload PDF files and engage in conversation.
diff --git a/en/guides/workflow/node/end.md b/en/guides/workflow/node/end.md
index ff2750853..896a420b2 100644
--- a/en/guides/workflow/node/end.md
+++ b/en/guides/workflow/node/end.md
@@ -18,12 +18,12 @@ End nodes are not supported within Chatflow.
In the following [long story generation workflow](iteration.md#example-2-long-article-iterative-generation-another-scheduling-method), the variable `Output` declared by the end node is the output of the upstream code node. This means the workflow will end after the Code node completes execution and will output the execution result of Code.
-
End Node - Long Story Generation Example
+
**Single Path Execution Example:**
-
+
**Multi-Path Execution Example:**
-
+
diff --git a/en/guides/workflow/node/http-request.md b/en/guides/workflow/node/http-request.md
index 16f07f053..8f6f69e7a 100644
--- a/en/guides/workflow/node/http-request.md
+++ b/en/guides/workflow/node/http-request.md
@@ -15,7 +15,7 @@ This node supports common HTTP request methods:
You can configure various aspects of the HTTP request, including URL, request headers, query parameters, request body content, and authentication information.
-
HTTP Request Configuration
+
***
@@ -25,7 +25,7 @@ You can configure various aspects of the HTTP request, including URL, request he
One practical feature of this node is the ability to dynamically insert variables into different parts of the request based on the scenario. For example, when handling customer feedback requests, you can embed variables such as username or customer ID, feedback content, etc., into the request to customize automated reply messages or fetch specific customer information and send related resources to a designated server.
-
Customer Feedback Classification
+
The return values of an HTTP request include the response body, status code, response headers, and files. Notably, if the response contains a file, this node can automatically save the file for use in subsequent steps of the workflow. This design not only improves processing efficiency but also makes handling responses with files straightforward and direct.
@@ -37,7 +37,7 @@ Example: Suppose you are developing a document management application and need t
Here is a configuration example:
-
http-node-send-file
+
### Advanced Features
diff --git a/en/guides/workflow/node/ifelse.md b/en/guides/workflow/node/ifelse.md
index bca4a2dd3..ed2f41aaa 100644
--- a/en/guides/workflow/node/ifelse.md
+++ b/en/guides/workflow/node/ifelse.md
@@ -27,7 +27,7 @@ A conditional branching node has three parts:
### Scenario
-
+
Taking the above **Text Summary Workflow** as an example:
@@ -41,4 +41,4 @@ Taking the above **Text Summary Workflow** as an example:
For complex condition judgments, you can set multiple condition judgments and configure **AND** or **OR** between conditions to take the **intersection** or **union** of the conditions, respectively.
-
Multiple Condition Judgments
+
diff --git a/en/guides/workflow/node/iteration.md b/en/guides/workflow/node/iteration.md
index 522985cb1..a5136cd09 100644
--- a/en/guides/workflow/node/iteration.md
+++ b/en/guides/workflow/node/iteration.md
@@ -20,13 +20,13 @@ An iteration node consists of three core components: **Input Variables**, **Iter
**Output Variables:** Outputs only array variables (`Array[List]`).
-
+
1. Enter the story title and outline in the **Start Node**.
2. Use a **Generate Subtitles and Outlines Node** to use LLM to generate the complete content from user input.
@@ -38,15 +38,15 @@ An iteration node consists of three core components: **Input Variables**, **Iter
1. Configure the story title (title) and outline (outline) in the **Start Node**.
-
Start Node Configuration
+
2. Use a **Generate Subtitles and Outlines Node** to convert the story title and outline into complete text.
-
Template Node
+
3. Use a **Extract Subtitles and Outlines Node** to convert the story text into an array (Array) structure. The parameter to extract is `sections`, and the parameter type is `Array[Object]`.
-
Parameter Extraction
+
{% hint style="info" %}
The effectiveness of parameter extraction is influenced by the model's inference capability and the instructions given. Using a model with stronger inference capabilities and adding examples in the **instructions** can improve the parameter extraction results.
@@ -54,11 +54,11 @@ The effectiveness of parameter extraction is influenced by the model's inference
4. Use the array-formatted story outline as the input for the iteration node and process it within the iteration node using an **LLM Node**.
-
Configure Iteration Node
+
Configure the input variables `GenerateOverallOutline/output` and `Iteration/item` in the LLM Node.
-
Configure LLM Node
+
{% hint style="info" %}
Built-in variables for iteration: `items[object]` and `index[number]`.
@@ -70,15 +70,15 @@ Built-in variables for iteration: `items[object]` and `index[number]`.
5. Configure a **Direct Reply Node** inside the iteration node to achieve streaming output after each iteration.
-
Configure Answer Node
+
6. Complete debugging and preview.
-
Generate by Iterating Through Story Chapters
+
#### **Example 2: Long Article Iteration Generator (Another Arrangement)**
-
+
* Enter the story title and outline in the **Start Node**.
* Use an **LLM Node** to generate subheadings and corresponding content for the article.
@@ -95,11 +95,11 @@ Built-in variables for iteration: `items[object]` and `index[number]`.
The iteration node supports parallel processing, improving execution efficiency when enabled.
-
Enable parallel mode
+
Below illustrates the comparison between parallel and sequential execution in the iteration node.
-
Sequential and Parallel Processing Flow Diagram
+
Parallel mode supports up to 10 concurrent iterations. When processing more than 10 tasks, the first 10 elements execute simultaneously, with remaining tasks processed after the completion of the initial batch.
@@ -135,11 +135,11 @@ Array variables can be generated via the following nodes as iteration node input
* [Code Node](code.md)
-
+
* [Knowledge Base Retrieval](knowledge-retrieval.md)
* [Iteration](iteration.md)
@@ -154,7 +154,7 @@ The output variable of the iteration node is in array format and cannot be direc
**Convert Using a Code Node**
-
Code Node Conversion
+
CODE Example:
@@ -168,7 +168,7 @@ def main(articleSections: list):
**Convert Using a Template Node**
-
Template Node Conversion
+
CODE Example:
diff --git a/en/guides/workflow/node/knowledge-retrieval.md b/en/guides/workflow/node/knowledge-retrieval.md
index 9ec800ad6..20b46bd58 100644
--- a/en/guides/workflow/node/knowledge-retrieval.md
+++ b/en/guides/workflow/node/knowledge-retrieval.md
@@ -2,7 +2,7 @@
The Knowledge Base Retrieval Node is designed to query text content related to user questions from the Dify Knowledge Base, which can then be used as context for subsequent answers by the Large Language Model (LLM).
-
+
Configuring the Knowledge Base Retrieval Node involves four main steps:
@@ -31,4 +31,4 @@ It's possible to modify the indexing strategy and retrieval mode for an individu
Dify offers two recall strategies for different knowledge base retrieval scenarios: "N-to-1 Recall" and "Multi-way Recall". In the N-to-1 mode, knowledge base queries are executed through function calling, requiring the selection of a system reasoning model. In the multi-way recall mode, a Rerank model needs to be configured for result re-ranking. For a detailed explanation of these two recall strategies, refer to the retrieval mode explanation in the [help documentation](https://docs.dify.ai/guides/knowledge-base/create-knowledge-and-upload-documents#id-5-indexing-methods).
-
+
diff --git a/en/guides/workflow/node/list-operator.md b/en/guides/workflow/node/list-operator.md
index 6784d0ad6..773ab027f 100644
--- a/en/guides/workflow/node/list-operator.md
+++ b/en/guides/workflow/node/list-operator.md
@@ -10,11 +10,11 @@ The list operator can filter and extract attributes such as file format type, fi
For example, in an application that allows users to upload both document files and image files simultaneously, different files need to be sorted through the **list operation node**, with different files being handled by different processes.
-
+
List operation nodes are generally used to extract information from array variables, converting them into variable types that can be accepted by downstream nodes through setting conditions. Its structure is divided into input variables, filter conditions, sorting, taking the first N items, and output variables.
-
+
**Input Variables**
@@ -72,6 +72,6 @@ In file interaction Q\&A scenarios, application users may upload document files
3. Extract document file variables and pass them to the "Doc Extractor" node; extract image file variables and pass them to the "LLM" node.
4. Add a "Answer" node at the end, filling in the output variable of the LLM node.
-
+
After the application user uploads both document files and images, document files are automatically diverted to the doc extractor node, and image files are automatically diverted to the LLM node to achieve joint processing of mixed files.
diff --git a/en/guides/workflow/node/llm.md b/en/guides/workflow/node/llm.md
index c5b6c69d8..0a3c0c750 100644
--- a/en/guides/workflow/node/llm.md
+++ b/en/guides/workflow/node/llm.md
@@ -4,7 +4,7 @@
Invokes the capabilities of large language models to process information input by users in the "Start" node (natural language, uploaded files, or images) and provide effective response information.
-
LLM Node
+
***
@@ -27,7 +27,7 @@ By selecting the appropriate model and writing prompts, you can build powerful a
### How to Configure
-
LLM Node Configuration - Model Selection
+
**Configuration Steps:**
@@ -48,11 +48,11 @@ In the LLM node, you can customize the model input prompts. If you select a chat
If you're struggling to come up with effective system prompts (System), you can use the Prompt Generator to quickly create prompts suitable for your specific business scenarios, leveraging AI capabilities.
-
+
In the prompt editor, you can call out the **variable insertion menu** by typing `/` or `{` to insert **special variable blocks** or **upstream node variables** into the prompt as context content.
-
Calling Out the Variable Insertion Menu
+
***
@@ -62,7 +62,7 @@ In the prompt editor, you can call out the **variable insertion menu** by typing
Context variables are a special type of variable defined within the LLM node, used to insert externally retrieved text content into the prompt.
-
Context Variables
+
In common knowledge base Q\&A applications, the downstream node of knowledge retrieval is typically the LLM node. The **output variable** `result` of knowledge retrieval needs to be configured in the **context variable** within the LLM node for association and assignment. After association, inserting the **context variable** at the appropriate position in the prompt can incorporate the externally retrieved knowledge into the prompt.
@@ -88,13 +88,13 @@ To achieve conversational memory in text completion models (e.g., gpt-3.5-turbo-
The conversation history variable is not widely used and can only be inserted when selecting text completion models in Chatflow.
{% endhint %}
-
Inserting Conversation History Variable
+
**Model Parameters**
The parameters of the model affect the output of the model. Different models have different parameters. The following figure shows the parameter list for `gpt-4`.
-
+
The main parameter terms are explained as follows:
@@ -108,7 +108,7 @@ The main parameter terms are explained as follows:
If you do not understand what these parameters are, you can choose to load presets and select from the three presets: Creative, Balanced, and Precise.
-
+
***
@@ -143,7 +143,7 @@ To enable workflow applications to read "[Knowledge Base](../../knowledge-base/)
2. Fill in the **output variable** `result` of the knowledge retrieval node into the **context variable** of the LLM node;
3. Insert the **context variable** into the application prompt to give the LLM the ability to read text within the knowledge base.
-
+
The `result` variable output by the Knowledge Retrieval Node also includes segmented reference information. You can view the source of information through the **Citation and Attribution** feature.
@@ -161,7 +161,7 @@ To enable workflow applications to read document contents, such as building a Ch
For more information, please refer to [File Upload](../file-upload.md).
-
input system prompts
+
* **Error Handling**
@@ -170,6 +170,6 @@ When processing information, LLM nodes may encounter errors such as input text e
1. Enable "Error Handling" in the LLM node
2. Select and configure an error handling strategy
-
input system prompts
+
For more information about exception handling methods, please refer to the [Error Handling](https://docs.dify.ai/guides/workflow/error-handling).
diff --git a/en/guides/workflow/node/parameter-extractor.md b/en/guides/workflow/node/parameter-extractor.md
index 3d7030b49..25bea1c1c 100644
--- a/en/guides/workflow/node/parameter-extractor.md
+++ b/en/guides/workflow/node/parameter-extractor.md
@@ -16,11 +16,11 @@ Some nodes within the workflow require specific data formats as inputs, such as
In this example: The Arxiv paper retrieval tool requires **paper author** or **paper ID** as input parameters. The parameter extractor extracts the paper ID **2405.10739** from the query "What is the content of this paper: 2405.10739" and uses it as the tool parameter for precise querying.
-
Arxiv Paper Retrieval Tool
+
2. **Converting text to structured data**, such as in the long story iteration generation application, where it serves as a pre-step for the [iteration node](iteration.md), converting chapter content in text format to an array format, facilitating multi-round generation processing by the iteration node.
-
+
1. **Extracting structured data and using the** [**HTTP Request**](https://docs.dify.ai/guides/workflow/node/http-request), which can request any accessible URL, suitable for obtaining external retrieval results, webhooks, generating images, and other scenarios.
diff --git a/en/guides/workflow/node/question-classifier.md b/en/guides/workflow/node/question-classifier.md
index 67168e2e8..cff4676ab 100644
--- a/en/guides/workflow/node/question-classifier.md
+++ b/en/guides/workflow/node/question-classifier.md
@@ -14,7 +14,7 @@ In a typical product customer service Q\&A scenario, the issue classifier can se
The following diagram is an example workflow template for a product customer service scenario:
-
+
In this scenario, we set up three classification labels/descriptions:
@@ -32,7 +32,7 @@ When users input different questions, the issue classifier will automatically cl
### 3. How to Configure
-
+
**Configuration Steps:**
diff --git a/en/guides/workflow/node/start.md b/en/guides/workflow/node/start.md
index 775c8db81..47b2f3a10 100644
--- a/en/guides/workflow/node/start.md
+++ b/en/guides/workflow/node/start.md
@@ -8,7 +8,7 @@ The **“Start”** node is a critical preset node in the Chatflow / Workflow ap
On the Start node's settings page, you'll find two sections: **"Input Fields"** and preset **System Variables**.
-
Chatflow and Workflow
+
#### Input Field
diff --git a/en/guides/workflow/node/template.md b/en/guides/workflow/node/template.md
index 1f1d1bd4a..f39ca5e0b 100644
--- a/en/guides/workflow/node/template.md
+++ b/en/guides/workflow/node/template.md
@@ -2,7 +2,7 @@
Template lets you dynamically format and combine variables from previous nodes into a single text-based output using Jinja2, a powerful templating syntax for Python. It's useful for combining data from multiple sources into a specific structure required by subsequent nodes. The simple example below shows how to assemble an article by piecing together various previous outputs:
-
+
Beyond naive use cases, you can create more complex templates as per Jinja's [documentation](https://jinja.palletsprojects.com/en/3.1.x/templates/) for a variety of tasks. Here's one template that structures retrieved chunks and their relevant metadata from a knowledge retrieval node into a formatted markdown:
@@ -22,7 +22,7 @@ Beyond naive use cases, you can create more complex templates as per Jinja's [do
{% endraw %}
```
-
+
This template node can then be used within a Chatflow to return intermediate outputs to the end user, before a LLM response is initiated.
diff --git a/en/guides/workflow/node/tools.md b/en/guides/workflow/node/tools.md
index 720511337..35ef34874 100644
--- a/en/guides/workflow/node/tools.md
+++ b/en/guides/workflow/node/tools.md
@@ -14,9 +14,9 @@ If built-in tools do not meet your needs, you can create custom tools in the **D
You can also orchestrate a more complex workflow and publish it as a tool.
-
Configuring Google Search Tool to Retrieve External Knowledge
+
Configuring a tool node generally involves two steps:
diff --git a/en/guides/workflow/node/variable-aggregator.md b/en/guides/workflow/node/variable-aggregator.md
index f5538ee3b..343dbc4c7 100644
--- a/en/guides/workflow/node/variable-aggregator.md
+++ b/en/guides/workflow/node/variable-aggregator.md
@@ -16,15 +16,15 @@ Through variable aggregation, you can aggregate multiple outputs, such as from i
Without variable aggregation, the branches of Classification 1 and Classification 2, after different knowledge base retrievals, would require repeated definitions for downstream LLM and direct response nodes.
-
+
By adding variable aggregation, the outputs of the two knowledge retrieval nodes can be aggregated into a single variable.
-
Multi-Branch Aggregation after Issue Classification
+
**Multi-Branch Aggregation after IF/ELSE Conditional Branching**
-
Multi-Branch Aggregation after Conditional Branching
+
### 3 Format Requirements
diff --git a/en/guides/workflow/node/variable-assigner.md b/en/guides/workflow/node/variable-assigner.md
index a1ab23795..88f2156ef 100644
--- a/en/guides/workflow/node/variable-assigner.md
+++ b/en/guides/workflow/node/variable-assigner.md
@@ -8,7 +8,7 @@ The variable assigner node is used to assign values to writable variables. Curre
Usage: Through the variable assigner node, you can assign workflow variables to conversation variables for temporary storage, which can be continuously referenced in subsequent conversations.
-
+
***
@@ -22,7 +22,7 @@ Using the variable assigner node, you can write context from the conversation pr
Example: After the conversation starts, LLM will automatically determine whether the user's input contains facts, preferences, or chat history that need to be remembered. If it has, LLM will first extract and store those information, then use it as context to respond. If there is no new information to remember, LLM will directly use the previously relevant memories to answer questions.
-
+
**Configuration process:**
@@ -108,7 +108,7 @@ def main(arg1: list) -> str:
Example: Before the chatting, the user specifies "English" in the `language` input box. This language will be written to the conversation variable, and the LLM will reference this information when responding, continuing to use "English" in subsequent conversations.
-
+
**Configuration Guide:**
@@ -124,7 +124,7 @@ Example: Before the chatting, the user specifies "English" in the `language` inp
Example: After starting the conversation, the LLM will ask the user to input items related to the Checklist in the chatting box. Once the user mentions content from the Checklist, it will be updated and stored in the Conversation Variable. The LLM will remind the user to continue supplementing missing items after each round of dialogue.
-
+
**Configuration Process:**
@@ -138,7 +138,7 @@ Example: After starting the conversation, the LLM will ask the user to input ite
Click the `+` icon on the right side of the node and select the **“Variable Assignment”** node. Configure the target variables and their corresponding source variables. This node allows you to assign values to multiple variables simultaneously.
-
+
**Setting Variables:**
diff --git a/en/guides/workflow/orchestrate-node.md b/en/guides/workflow/orchestrate-node.md
index e3eda748f..285e90dae 100644
--- a/en/guides/workflow/orchestrate-node.md
+++ b/en/guides/workflow/orchestrate-node.md
@@ -2,7 +2,7 @@
Both Chatflow and Workflow applications support node orchestration through visual drag-and-drop, with two orchestration design patterns: serial and parallel.
-
+
## Serial Node Design Pattern
@@ -16,13 +16,13 @@ Consider a "Novel Generation" Workflow App implementing serial pattern: after th
2. Sequentially link the nodes.
3. Converge all paths to the "End" node to finalize the workflow.
-
+
### Viewing Serial Structure Application Logs
In a serial structure application, logs display node operations sequentially. Click "View Logs - Tracing" in the upper right corner of the dialog box to see the complete workflow process, including input/output, token consumption, and runtime for each node.
-
+
## Designing Parallel Structure
@@ -36,15 +36,15 @@ The following four methods demonstrate how to create a parallel structure throug
**Method 1** Hover over a node to reveal the `+` button. Click it to add multiple nodes, automatically forming a parallel structure.
-
+
**Method 2** Extend a connection from a node by dragging its `+` button, creating a parallel structure.
-
+
**Method 3** With multiple nodes on the canvas, visually drag and link them to form a parallel structure.
-
+
**Method 4** In addition to canvas-based methods, you can generate parallel structures by adding nodes through the "Next Step" section in a node's right-side panel. This approach automatically creates the parallel configuration.
@@ -57,7 +57,7 @@ The following four methods demonstrate how to create a parallel structure throug
* Chatflow applications support multiple "answer" nodes. Each parallel structure in these applications must terminate with an "answer" node to ensure proper output of content;
* All parallel structures will run simultaneously; nodes within the parallel structure output results after completing their tasks, with no order relationship in output. The simpler the parallel structure, the faster the output of results.
-
+
### Designing Parallel Structure Patterns
@@ -69,7 +69,7 @@ Normal parallel refers to the `Start | Parallel Nodes | End three-layer` relatio
The upper limit for parallel branches is 10.
-
+
#### 2. Nested Parallel
@@ -77,22 +77,22 @@ Nested parallel refers to the Start | Multiple Parallel Structures | End multi-l
A workflow supports up to 3 layers of nesting relationships.
-
+
#### 3. Conditional Branch + Parallel
Parallel structures can also be used in conjunction with conditional branches.
-
+
#### 4. Iteration Branch + Parallel
This pattern integrates parallel structures within iteration branches, optimizing the execution efficiency of repetitive tasks.
-
+
### Viewing Parallel Structure Application Logs
Applications with parallel structures generate logs in a tree-like format. Collapsible parallel node groups facilitate easier viewing of individual node logs.
-
+
diff --git a/en/guides/workflow/publish.md b/en/guides/workflow/publish.md
index f37344c7f..49687aa8e 100644
--- a/en/guides/workflow/publish.md
+++ b/en/guides/workflow/publish.md
@@ -2,7 +2,7 @@
After completing debugging, clicking "Publish" in the upper right corner allows you to save and quickly release the workflow as different types of applications.
-
+
Conversational applications can be published as:
diff --git a/en/guides/workflow/variables.md b/en/guides/workflow/variables.md
index 1b06937e3..f18cbd5ac 100644
--- a/en/guides/workflow/variables.md
+++ b/en/guides/workflow/variables.md
@@ -18,7 +18,7 @@ Workflow type application provides the system variables below:
Variables name
Data Type
Description
Remark
sys.files
[LEGACY]
Array[File]
File Parameter: Stores images uploaded by users
The image upload function needs to be enabled in the 'Features' section in the upper right corner of the application orchestration page
sys.user_id
String
User ID: A unique identifier automatically assigned by the system to each user when they use a workflow application. It is used to distinguish different users
sys.app_id
String
App ID: A unique identifier automatically assigned by the system to each App. This parameter is used to record the basic information of the current application.
This parameter is used to differentiate and locate distinct Workflow applications for users with development capabilities
sys.workflow_id
String
Workflow ID: This parameter records information about all nodes information in the current Workflow application.
This parameter can be used by users with development capabilities to track and record information about the nodes contained within a Workflow
sys.workflow_run_id
String
Workflow Run ID: Used to record the runtime status and execution logs of a Workflow application.
This parameter can be used by users with development capabilities to track the application's historical execution records
-
Workflow App System Variables
+
#### Chatflow
@@ -26,13 +26,13 @@ Chatflow type application provides the following system variables:
Variables name
Data Type
Description
Remark
sys.query
String
Content entered by the user in the chatting box.
sys.files
Array[File]
File Parameter: Stores images uploaded by users
The image upload function needs to be enabled in the 'Features' section in the upper right corner of the application orchestration page
sys.dialogue_count
Number
The number of conversations turns during the user's interaction with a Chatflow application. The count automatically increases by one after each chat round and can be combined with if-else nodes to create rich branching logic.
For example, LLM will review the conversation history at the X conversation turn and automatically provide an analysis.
sys.conversation_id
String
A unique ID for the chatting box interaction session, grouping all related messages into the same conversation, ensuring that the LLM continues the chatting on the same topic and context.
sys.user_id
String
A unique ID is assigned for each application user to distinguish different conversation users.
sys.workflow_id
String
Workflow ID: This parameter records information about all nodes information in the current Workflow application.
This parameter can be used by users with development capabilities to track and record information about the nodes contained within a Workflow
sys.workflow_run_id
String
Workflow Run ID: Used to record the runtime status and execution logs of a Workflow application.
This parameter can be used by users with development capabilities to track the application's historical execution records
-
Chatflow App System Variables
+
### Environment Variables
**Environment variables are used to protect sensitive information involved in workflows**, such as API keys and database passwords used when running workflows. They are stored in the workflow rather than in the code, allowing them to be shared across different environments.
-
Environment Variables
+
Supports the following 3 data types:
@@ -56,7 +56,7 @@ Environmental variables have the following characteristics:
For example, you can store the language preference input by the user in the first round of chatting in a conversation variable. The LLM will refer to the information in the conversation variable when answering and use the specified language to reply to the user in subsequent chats.
-
Conversation Variable
+
**Conversation variables** support the following six data types:
diff --git a/en/guides/workspace/app.md b/en/guides/workspace/app.md
index aa216bf42..c0ab857f4 100644
--- a/en/guides/workspace/app.md
+++ b/en/guides/workspace/app.md
@@ -4,11 +4,11 @@
In the **Discover** section, several commonly used template applications are provided. These applications cover areas such as human resources, assistants, translation, programming, and writing.
-
+
To use a template application, click the "Add to Workspace" button on the template. You can then use the application in the workspace on the left side.
-
+
To modify a template and create a new application, click the "Customize" button on the template.
diff --git a/en/guides/workspace/app/README.md b/en/guides/workspace/app/README.md
index 778d8e6ad..2d5d5d602 100644
--- a/en/guides/workspace/app/README.md
+++ b/en/guides/workspace/app/README.md
@@ -4,11 +4,11 @@
In the **Discover** section, several commonly used template applications are provided. These applications cover areas such as human resources, assistants, translation, programming, and writing.
-
+
To use a template application, click the "Add to Workspace" button on the template. You can then use the application in the workspace on the left side.
-
+
To modify a template and create a new application, click the "Customize" button on the template.
diff --git a/en/guides/workspace/billing.md b/en/guides/workspace/billing.md
index 86980f6f8..6aed21144 100644
--- a/en/guides/workspace/billing.md
+++ b/en/guides/workspace/billing.md
@@ -38,7 +38,7 @@ Check out the [pricing page ](https://dify.ai/pricing)to learn more.
You can view capacity usage details on your workspace's Billing page.
-
monitor resource usage
+
## FAQ
diff --git a/en/guides/workspace/explore.md b/en/guides/workspace/explore.md
index d9fbe62e2..4ab4bb92b 100644
--- a/en/guides/workspace/explore.md
+++ b/en/guides/workspace/explore.md
@@ -4,11 +4,11 @@
In **Explore > Discovery**, some commonly used template applications are provided. These apps cover translate, writing, programming and assistant.
-
+
If you want to use a template application, click the template's "Add to Workspace" button. In the workspace on the left, the app is available.
-
+
If you want to modify a template to create a new application, click the "Customize" button of the template.
@@ -16,6 +16,6 @@ If you want to modify a template to create a new application, click the "Customi
The workspace is the application's navigation. Click an application in the workspace to use the application directly.
-
+
Apps in the workspace include: your own apps and apps added to the workspace by other teams.
diff --git a/en/learn-more/extended-reading/how-to-use-json-schema-in-dify.md b/en/learn-more/extended-reading/how-to-use-json-schema-in-dify.md
index 06bbf8bbc..186890956 100644
--- a/en/learn-more/extended-reading/how-to-use-json-schema-in-dify.md
+++ b/en/learn-more/extended-reading/how-to-use-json-schema-in-dify.md
@@ -18,7 +18,7 @@ JSON Schema is a specification for describing JSON data structures. Developers c
Switch the LLM in your application to one of the models supporting JSON Schema output mentioned above. Then, in the settings form, enable `JSON Schema` and fill in the JSON Schema template. Simultaneously, enable the `response_format` column and switch it to the `json_schema` format.
-
+
The content generated by the LLM supports output in the following format:
@@ -205,7 +205,7 @@ You are a UI generator AI. Convert the user input into a UI.
**Example Output:**
-
+
## Tips
diff --git a/en/learn-more/extended-reading/retrieval-augment/README.md b/en/learn-more/extended-reading/retrieval-augment/README.md
index cf62dd19f..752c69b31 100644
--- a/en/learn-more/extended-reading/retrieval-augment/README.md
+++ b/en/learn-more/extended-reading/retrieval-augment/README.md
@@ -8,7 +8,7 @@ Developers can use this technology to build AI-powered customer service, enterpr
In the diagram below, when a user asks, "Who is the President of the United States?", the system does not directly pass the question to the large model for an answer. Instead, it first performs a vector search in a knowledge base (such as Wikipedia shown in the diagram) to find relevant content through semantic similarity matching (e.g., "Joe Biden is the 46th and current president of the United States..."). Then, the system provides the user's question along with the retrieved relevant knowledge to the large model, allowing it to obtain sufficient information to answer the question reliably.
-
Basic RAG Architecture
+
**Why is this necessary?**
diff --git a/en/learn-more/extended-reading/retrieval-augment/hybrid-search.md b/en/learn-more/extended-reading/retrieval-augment/hybrid-search.md
index 6e8122a13..7888d339f 100644
--- a/en/learn-more/extended-reading/retrieval-augment/hybrid-search.md
+++ b/en/learn-more/extended-reading/retrieval-augment/hybrid-search.md
@@ -29,7 +29,7 @@ For most text search scenarios, the primary goal is to ensure that the most rele
In hybrid search, you need to establish vector indexes and keyword indexes in the database in advance. When a user query is input, the most relevant texts are retrieved from the documents using both retrieval methods.
-
Hybrid Search
+
"Hybrid search" does not have a precise definition. This article uses the combination of vector search and keyword search as an example. If we use other combinations of search algorithms, it can also be called "hybrid search." For instance, we can combine knowledge graph techniques for retrieving entity relationships with vector search techniques.
@@ -39,7 +39,7 @@ Different retrieval systems excel at finding various subtle relationships betwee
Definition: Generating query embeddings and querying the text segments most similar to their vector representations.
-
Vector Search Settings
+
**TopK:** Used to filter the text fragments most similar to the user's query. The system will dynamically adjust the number of fragments based on the context window size of the selected model. The default value is 3.
@@ -51,7 +51,7 @@ Definition: Generating query embeddings and querying the text segments most simi
Definition: Indexing all words in the document, allowing users to query any word and return text fragments containing those words.
-
Full-Text Search Settings
+
**TopK:** Used to filter the text fragments most similar to the user's query. The system will dynamically adjust the number of fragments based on the context window size of the selected model. The default value is 3.
@@ -61,7 +61,7 @@ Definition: Indexing all words in the document, allowing users to query any word
Simultaneously performs full-text search and vector search, applying a re-ranking step to select the best results matching the user's query from both types of query results. Requires configuring the Rerank model API.
-
Hybrid Search Settings
+
**TopK:** Used to filter the text fragments most similar to the user's query. The system will dynamically adjust the number of fragments based on the context window size of the selected model. The default value is 3.
@@ -71,16 +71,16 @@ Simultaneously performs full-text search and vector search, applying a re-rankin
Set different retrieval modes by entering the "Dataset -> Create Dataset" page and configuring the retrieval settings.
-
Setting Retrieval Mode When Creating a Dataset
+
### Modifying Retrieval Mode in Dataset Settings
Modify the retrieval mode of an existing dataset by entering the "Dataset -> Select Dataset -> Settings" page.
-
Modifying Retrieval Mode in Dataset Settings
+
### Modifying Retrieval Mode in Prompt Arrangement
Modify the retrieval mode when creating an application by entering the "Prompt Arrangement -> Context -> Select Dataset -> Settings" page.
-
Modifying Retrieval Mode in Prompt Arrangement
+
diff --git a/en/learn-more/extended-reading/retrieval-augment/rerank.md b/en/learn-more/extended-reading/retrieval-augment/rerank.md
index 8e4081c06..ad121edc2 100644
--- a/en/learn-more/extended-reading/retrieval-augment/rerank.md
+++ b/en/learn-more/extended-reading/retrieval-augment/rerank.md
@@ -6,7 +6,7 @@ Hybrid search can leverage the strengths of different retrieval technologies to
**The re-rank model calculates the semantic match between the list of candidate documents and the user query, reordering them based on semantic match to improve the results of semantic sorting.** The principle is to compute a relevance score between the user query and each candidate document and return a list of documents sorted by relevance from high to low. Common re-rank models include Cohere rerank, bge-reranker, etc.
-
Hybrid Search + Re-ranking
+
In most cases, there is a preliminary retrieval before re-ranking because calculating the relevance score between a query and millions of documents would be highly inefficient. Therefore, **re-ranking is typically placed at the final stage of the search process and is ideal for merging and sorting results from different retrieval systems.**
@@ -24,7 +24,7 @@ For example, with Cohere Rerank, you only need to register an account and apply
Dify currently supports the Cohere Rerank model. You can enter the "Model Providers -> Cohere" page and fill in the API key for the Re-rank model:
-
Configure Cohere Rerank Model in Model Providers
+
### How to Obtain the Cohere Rerank Model?
@@ -34,7 +34,7 @@ Visit: [https://cohere.com/rerank](https://cohere.com/rerank), register on the p
Enter the "Dataset -> Create Dataset -> Retrieval Settings" page to add the Re-rank settings. Besides setting the Re-rank model when creating a dataset, you can also change the Re-rank configuration in the settings of an existing dataset and in the dataset recall mode settings in application orchestration.
-
Setting the Re-rank Model in Dataset Retrieval Mode
+
**TopK:** Used to set the number of relevant documents returned after re-ranking.
@@ -46,4 +46,4 @@ Enter the "Prompt Arrangement -> Context -> Settings" page to enable the Re-rank
Explanation about multi-path recall mode: 🔗Please check the section [Multi-path Retrieval](https://docs.dify.ai/guides/knowledge-base/integrate-knowledge-within-application#multi-path-retrieval-recommended)
-
Setting the Re-rank Model in Multi-Path Recall Mode for Datasets
+
diff --git a/en/learn-more/extended-reading/retrieval-augment/retrieval.md b/en/learn-more/extended-reading/retrieval-augment/retrieval.md
index d823f021e..17127f55b 100644
--- a/en/learn-more/extended-reading/retrieval-augment/retrieval.md
+++ b/en/learn-more/extended-reading/retrieval-augment/retrieval.md
@@ -2,7 +2,7 @@
When users build AI applications with multiple knowledge bases, Dify's retrieval strategy will determine which content will be retrieved.
-
retrieval Mode Settings
+
### Retrieval Setting
@@ -12,6 +12,6 @@ In multi-path retrieval mode, it's recommended that the Rerank model be configur
Below is the technical flowchart for the multi-path retrieval mode:
-
Multi-Path retrieval
+
Since multi-path retrieval mode does not rely on the model's inference capability or dataset descriptions, it can achieve higher-quality retrieval results when retrieving across multiple datasets. Additionally, incorporating a re-ranking step can effectively improve document retrieval effectiveness. Therefore, when creating knowledge base Q\&A applications associated with multiple datasets, we recommend configuring the retrieval mode as multi-path retrieval.
diff --git a/en/learn-more/how-to-use-json-schema-in-dify.md b/en/learn-more/how-to-use-json-schema-in-dify.md
index 9cc24d9af..c96ffb96e 100644
--- a/en/learn-more/how-to-use-json-schema-in-dify.md
+++ b/en/learn-more/how-to-use-json-schema-in-dify.md
@@ -211,7 +211,7 @@ You are a UI generator AI. Convert the user input into a UI.
**Example Output:**
-
+
## Tips
diff --git a/en/learn-more/prompt-engineering/prompt-engineering-1/README.md b/en/learn-more/prompt-engineering/prompt-engineering-1/README.md
index e4a0c6deb..c4d4420ba 100644
--- a/en/learn-more/prompt-engineering/prompt-engineering-1/README.md
+++ b/en/learn-more/prompt-engineering/prompt-engineering-1/README.md
@@ -58,7 +58,7 @@ Before you start experimenting with the new mode, you need to know the essential
After creating an application, you can switch to **Expert Mode** on the prompt orchestration page, where you can edit the complete application prompts.
-
Expert Mode Entry
+
{% hint style="warning" %}
After modifying prompts and publishing the application in **Expert Mode**, you cannot return to **Simple Mode**.
@@ -72,7 +72,7 @@ In both **Simple Mode** and **Expert Mode**, you can modify the parameters for t
Note that the built-in prompt containing \{{#context#\}} will only be displayed in **Expert Mode** after uploading the context.
{% endhint %}
-
Context Parameter Settings
+
**TopK: Value range is an integer from 1 to 10**
@@ -111,7 +111,7 @@ And will not generate additional dialogue (i.e., the LLM will stop generating co
In **Expert Mode**, you can type "`/`" in the text editor to quickly bring up content blocks to insert into the prompt. Content blocks include: `context`, `variable`, `conversation history`, `query content`. You can also type "`{`" to quickly insert a list of previously created variables.
-
Shortcut Key “/”
+
{% hint style="warning" %}
Content blocks other than "variables" cannot be inserted repeatedly. The available content blocks may vary based on the prompt template structure in different applications and models. `Conversation history` and `query content` are only available in text completion models for conversational applications.
@@ -154,7 +154,7 @@ During orchestration debugging, you can not only view the user's input and the L
In the debug preview interface, after a conversation between the user and the AI, move the mouse pointer to any user session, and you will see the "Log" icon button at the top left. Click it to view the prompt log.
-
Debug Log Entry
+
In the log, you can clearly see:
@@ -162,7 +162,7 @@ In the log, you can clearly see:
* Relevant text fragments referenced in the current session
* Historical conversation records
-
View Prompt Log in Debug Preview Interface
+
From the log, you can see the complete prompt sent to the LLM after system assembly and continuously improve the prompt input based on the debugging results.
@@ -170,4 +170,4 @@ From the log, you can see the complete prompt sent to the LLM after system assem
On the main interface for initial app construction, you can see "Logs and Annotations" in the left navigation bar. Click it to view the complete logs. On the main interface of Logs and Annotations, click any conversation log entry, and in the pop-up right dialog box, move the mouse pointer to the conversation to click the "Log" button to view the prompt log.
-
View Prompt Log in Logs and Annotations Interface
\ No newline at end of file
+
\ No newline at end of file
diff --git a/en/learn-more/use-cases/build-an-notion-ai-assistant.md b/en/learn-more/use-cases/build-an-notion-ai-assistant.md
index acd23ccde..02f29e9d2 100644
--- a/en/learn-more/use-cases/build-an-notion-ai-assistant.md
+++ b/en/learn-more/use-cases/build-an-notion-ai-assistant.md
@@ -46,15 +46,15 @@ Select "Sync from Notion" and then click the "Connect" button..
Afterward, you'll be redirected to the Notion login page. Log in with your Notion account.
-
+
Check the permissions needed by Dify, and then click the "Select pages" button.
-
+
Select the pages you want to synchronize with Dify, and press the "Allow access" button.
-
+
#### 4. Start training[](https://wsyfin.com/notion-dify#4-start-training)
@@ -88,15 +88,15 @@ For example, if your Notion notes focus on problem-solving in software developme
_I want you to act as an IT Expert in my Notion workspace, using your knowledge of computer science, network infrastructure, Notion notes, and IT security to solve the problems_.
-
+
It's recommended to initially enable the AI to actively furnish the users with a starter sentence, providing a clue as to what they can ask. Furthermore, activating the 'Speech to Text' feature can allow users to interact with your AI assistant using their voice.
-
+
Finally, Click the "Publish" button on the top right of the page. Now you can click the public URL in the "Monitoring" section to converse with your personalized AI assistant!
-
+
### Utilizing API to Integrate With Your Project
@@ -106,19 +106,19 @@ With effortless API integration, you can conveniently invoke your Notion AI appl
Click the "API Reference" button on the page of Overview page. You can refer to it as your App's API document.
-
+
#### 1. Generate API Secret Key[](https://wsyfin.com/notion-dify#1-generate-api-secret-key)
For security reasons, it's recommended to create a new API secret key to access your AI application.
-
+
#### 2. Retrieve Conversation ID[](https://wsyfin.com/notion-dify#2-retrieve-conversation-id)
After chatting with your AI application, you can retrieve the session ID from the "Logs & Ann." pages.
-
+
#### 3. Invoke API[](https://wsyfin.com/notion-dify#3-invoke-api)
diff --git a/en/learn-more/use-cases/create-a-midjourney-prompt-bot-with-dify.md b/en/learn-more/use-cases/create-a-midjourney-prompt-bot-with-dify.md
index 4a3d7ec71..03226b9c9 100644
--- a/en/learn-more/use-cases/create-a-midjourney-prompt-bot-with-dify.md
+++ b/en/learn-more/use-cases/create-a-midjourney-prompt-bot-with-dify.md
@@ -10,48 +10,48 @@ Dify offers two types of applications: conversational applications similar to Ch
You can access Dify here: https://dify.ai/
-
+
Once you've created your application, the dashboard page will display some data monitoring and application settings. Click on "Prompt Engineering" on the left, which is the main working page.
-
+
On this page, the left side is for prompt settings and other functions, while the right side provides real-time previews and usage of your created content. The prefix prompts are the triggers that the user inputs after each content, and they instruct the GPT model how to process the user's input information.
-
+
Take a look at my prefix prompt structure: the first part instructs GPT to output a description of a photo in the following structure. The second structure serves as the template for generating the prompt, mainly consisting of elements like 'Color photo of the theme,' 'Intricate patterns,' 'Stark contrasts,' 'Environmental description,' 'Camera model,' 'Lens focal length description related to the input content,' 'Composition description relative to the input content,' and 'The names of four master photographers.' This constitutes the main content of the prompt. In theory, you can now save this to the preview area on the right, input the theme you want to generate, and the corresponding prompt will be generated.
-
+
You may have noticed the "\{{proportion\}}" and "\{{version\}}" at the end. These are variables used to pass user-selected information. On the right side, users are required to choose image proportions and model versions, and these two variables help carry that information to the end of the prompt. Let's see how to set them up.
-
+
Our goal is to fill in the user's selected information at the end of the prompt, making it easy for users to copy without having to rewrite or memorize these commands. For this, we use the variable function.
Variables allow us to dynamically incorporate the user's form-filled or selected content into the prompt. For example, I've created two variables: one represents the image proportion, and the other represents the model version. Click the "Add" button to create the variables.
-
+
After creation, you'll need to fill in the variable key and field name. The variable key should be in English. The optional setting means the field will be non-mandatory when the user fills it. Next, click "Settings" in the action bar to set the variable content.
-
+
Variables can be of two types: text variables, where users manually input content, and select options where users select from given choices. Since we want to avoid manual commands, we'll choose the dropdown option and add the required choices.
-
+
Now, let's use the variables. We need to enclose the variable key within double curly brackets {} and add it to the prefix prompt. Since we want the GPT to output the user-selected content as is, we'll include the phrase "Producing the following English photo description based on user input" in the prompt.
-
+
However, there's still a chance that GPT might modify our variable content. To address this, we can lower the diversity in the model selection on the right, reducing the temperature and making it less likely to alter our variable content. You can check the tooltips for other parameters' meanings.
-
+
With these steps, your application is now complete. After testing and ensuring there are no issues with the output, click the "Publish" button in the upper right corner to release your application. You and users can access your application through the publicly available URL. You can also customize the application name, introduction, icon, and other details in the settings.
-
+
That's how you create a simple AI application using Dify. You can also deploy your application on other platforms or modify its UI using the generated API. Additionally, Dify supports uploading your own data, such as building a customer service bot to assist with product-related queries. This concludes the tutorial, and a special thanks to @goocarlos for creating such a fantastic product.
diff --git a/en/learn-more/use-cases/create-an-ai-chatbot-with-business-data-in-minutes.md b/en/learn-more/use-cases/create-an-ai-chatbot-with-business-data-in-minutes.md
index 05c7792ad..b53696f4b 100644
--- a/en/learn-more/use-cases/create-an-ai-chatbot-with-business-data-in-minutes.md
+++ b/en/learn-more/use-cases/create-an-ai-chatbot-with-business-data-in-minutes.md
@@ -21,7 +21,7 @@ If you want to build an AI Chatbot based on the company's existing knowledge bas
3. select the cleaning method
4. Click \[Save and Process], and it will take only a few seconds to complete the processing.
-
+
### Create an AI application and give it instructions
@@ -39,19 +39,19 @@ In this case, we assign a role to the AI:
> Opening remarks：Hey \{{User\_name\}}, I'm Bob☀️, the first AI member of Dify. You can discuss with me any questions related to Dify products, team, and even LLMOps.
-
+
### Debug the performance of AI Chatbot and publish.
After completing the setup, you can send messages to it on the right side of the current page to debug whether its performance meets expectations. Then click "Publish". And then you get an AI chatbot.
-
+
### Embed AI Chatbot application into your front-end page.
This step is to embed the prepared AI chatbot into your official website . Click \[Overview] -> \[Embedded], select the script tag method, and copy the script code into the \ or \ tag of your website. If you are not a technical person, you can ask the developer responsible for the official website to paste and update the page.
-
+
1. Paste the copied code into the target location on your website.
2. Update your official website and you can get an AI intelligent customer service with your business data. Try it out to see the effect.
diff --git a/en/learn-more/use-cases/how-to-connect-aws-bedrock.md b/en/learn-more/use-cases/how-to-connect-aws-bedrock.md
index 1cd691517..6adb12cb8 100644
--- a/en/learn-more/use-cases/how-to-connect-aws-bedrock.md
+++ b/en/learn-more/use-cases/how-to-connect-aws-bedrock.md
@@ -12,13 +12,13 @@ This article will briefly introduce how to connect the Dify platform with the AW
Visit [AWS Bedrock](https://aws.amazon.com/bedrock/) and create the Knowledge Base service.
-
Create AWS Bedrock Knowledge Base
+
### 2. Build the Backend API Service
The Dify platform cannot directly connect to AWS Bedrock Knowledge Base. The developer needs to refer to Dify's [API definition](../../guides/knowledge-base/external-knowledge-api-documentation.md) on external knowledge base connection, manually create the backend API service, and establish a connection with AWS Bedrock. Please refer to the specific architecture diagram:
-
Build the backend API service
+
You can refer to the following 2 demo code.
@@ -117,7 +117,7 @@ During the process, you can construct the API endpoint address and the API Key f
After log in to the AWS Bedrock Knowledge backend and get the ID of the created Knowledge Base, you can use this parameter to connect to the Dify platform in the subsequent steps.
-
Get the AWS Bedrock Knowledge Base ID
+
### 4. Associate the External Knowledge API
@@ -129,13 +129,13 @@ Follow the prompts on the page and fill in the following information:
* API endpoint address, the connection address of the external knowledge base, which can be customized in [Step 2](how-to-connect-aws-bedrock.md#id-2.build-the-backend-api-service). Example: `api-endpoint/retrieval`;
* API Key, the external knowledge base connection key, which can be customized in [Step 2](how-to-connect-aws-bedrock.md#id-2.build-the-backend-api-service).
-
+
### 5. Connect to External Knowledge Base
Go to the **“Knowledge** page, click **“Connect to an External Knowledge Base”** below the add knowledge base card to jump to the parameter configuration page.
-
+
Fill in the following parameters:
@@ -152,7 +152,7 @@ Fill in the AWS Bedrock knowledge base ID obtained in Step 3.
**Score threshold:** The similarity threshold for text chunk filtering. Only text chunks with a score exceeding the set score will be recalled. The default value is 0.5. The higher the value, the higher the similarity required between the text and the question, the smaller the number of texts expected to be recalled, and the more accurate the result will be.
-
+
After the settings are completed, you can establish a connection with the external knowledge base API.
@@ -160,8 +160,8 @@ After the settings are completed, you can establish a connection with the extern
After establishing a connection with an external knowledge base, developers can simulate possible user's question keywords in **"Retrieval Test"** and preview the text chunks retrieval from the AWS Bedrock Knowledge Base.
-
Test the connection and retrieval of the external knowledge base
+
If you are not satisfied with the retrieval results, you can try to modify the retrieval parameters or adjust the retrieval settings of AWS Bedrock Knowledge Base.
-
Adjust the text chunking parameters of AWS Bedrock Knowledge Base
+
### **Upgrading Plugins**
Click the "Plugins" button in the top right corner of the Dify platform, select the plugin that needs updating, and click the **"Upgrade"** button next to the plugin title.
-
+
### **Deleting Plugins**
Click the "Plugins" button in the top right corner of the Dify platform to see all installed plugins in the current Workspace. Click the "Delete" icon or the Remove button on the right side of the plugin details page to remove the plugin.
-
Remove plugin
+
diff --git a/en/plugins/publish-plugins/package-plugin-file-and-publish.md b/en/plugins/publish-plugins/package-plugin-file-and-publish.md
index 1ea6137b6..abdd6a4ae 100644
--- a/en/plugins/publish-plugins/package-plugin-file-and-publish.md
+++ b/en/plugins/publish-plugins/package-plugin-file-and-publish.md
@@ -31,13 +31,13 @@ dify plugin package ./your_plugin_project
After running the command, a file with `.difypkg` extension will be generated in the current path.
-
+
### **Installing Plugins**
Visit the Dify plugin management page, click **Install Plugin** → **Install via Local File** in the top right corner, or drag and drop the plugin file to a blank area of the page to install.
-
+
### **Publishing Plugins**
diff --git a/en/plugins/publish-plugins/publish-plugin-on-personal-github-repo.md b/en/plugins/publish-plugins/publish-plugin-on-personal-github-repo.md
index b563ea6d5..3d40702f0 100644
--- a/en/plugins/publish-plugins/publish-plugin-on-personal-github-repo.md
+++ b/en/plugins/publish-plugins/publish-plugin-on-personal-github-repo.md
@@ -74,11 +74,11 @@ git push origin v0.0.1
Go to the Releases page of your GitHub repository and create a new release. Upload plugin files when publishing. For detailed instructions on packaging plugins, please read the packaging plugins documentation.
-
Packaging Plugins
+
### **Installing Plugins via GitHub**
Others can install the plugin using the GitHub repository address. Visit the Dify platform's plugin management page, choose to install via GitHub, enter the repository address, select version number and package file to complete installation.
-
+
diff --git a/en/plugins/publish-plugins/publish-to-dify-marketplace/README.md b/en/plugins/publish-plugins/publish-to-dify-marketplace/README.md
index cfab15646..a88b2b0b3 100644
--- a/en/plugins/publish-plugins/publish-to-dify-marketplace/README.md
+++ b/en/plugins/publish-plugins/publish-to-dify-marketplace/README.md
@@ -18,7 +18,7 @@ To publish your plugin on the Dify Marketplace, follow these steps:
Here is the general plugin review process:
-
The process of uploading plugins
+
{% hint style="info" %}
**Note**:
diff --git a/en/plugins/quick-start/develop-plugins/agent-strategy-plugin.md b/en/plugins/quick-start/develop-plugins/agent-strategy-plugin.md
index e6bd40fff..51090f34f 100644
--- a/en/plugins/quick-start/develop-plugins/agent-strategy-plugin.md
+++ b/en/plugins/quick-start/develop-plugins/agent-strategy-plugin.md
@@ -1038,7 +1038,7 @@ class BasicAgentAgentStrategy(AgentStrategy):
After finalizing the plugin’s declaration file and implementation code, run `python -m main` in the plugin directory to restart it. Next, confirm the plugin runs correctly. Dify offers remote debugging—go to [“Plugin Management”](https://console-plugin.dify.dev/plugins) to obtain your debug key and remote server address.
-
+
Back in your plugin project, copy `.env.example` to `.env` and insert the relevant remote server and debug key info.
@@ -1058,7 +1058,7 @@ python -m main
You’ll see the plugin installed in your Workspace, and team members can also access it.
-
Browser Plugins
+
### Packaging the Plugin (Optional)
diff --git a/en/plugins/quick-start/develop-plugins/bundle.md b/en/plugins/quick-start/develop-plugins/bundle.md
index b5cd87bec..45788852a 100644
--- a/en/plugins/quick-start/develop-plugins/bundle.md
+++ b/en/plugins/quick-start/develop-plugins/bundle.md
@@ -29,11 +29,11 @@ Follow the prompts to configure plugin name, author information, and plugin desc
> The name must be 1-128 characters long and can only contain letters, numbers, hyphens, and underscores.
-
Bundle basic informatio
+
Fill in the information and hit enter, the Bundle plugin project directory will be created automatically.
-
+
#### **2. Add Dependencies**
diff --git a/en/plugins/quick-start/develop-plugins/extension-plugin.md b/en/plugins/quick-start/develop-plugins/extension-plugin.md
index d4ede03eb..fd54aea48 100644
--- a/en/plugins/quick-start/develop-plugins/extension-plugin.md
+++ b/en/plugins/quick-start/develop-plugins/extension-plugin.md
@@ -29,17 +29,17 @@ Follow the prompts to configure the plugin name, author information, and plugin
> The plugin name must be 1-128 characters long and can only contain letters, numbers, hyphens, and underscores.
-
Plugins detail
+
Once filled out, select Python in the Plugin Development Language section.
-
Plugins development: Python
+
### **3. Select Plugin Type and Initialize Project Template**
All templates in the scaffolding tool provide complete code projects. For demonstration purposes, this guide will use the `Extension` type plugin template as an example. For developers already familiar with plugin development, templates are not necessary, and you can refer to the interface documentation to complete different types of plugin development.
-
Extension
+
#### **Configure Plugin Permissions**
@@ -55,7 +55,7 @@ The plugin needs permissions to access the Dify main platform for proper connect
After checking all permission items, press Enter to complete the plugin creation. The system will automatically generate the plugin project code.
-
Plugins permissions
+
The base file structure of the plugin contains the following:
@@ -230,7 +230,7 @@ REMOTE_INSTALL_KEY=****-****-****-****-****
Run the `python -m main` command to launch the plugin. You can see on the plugin page that the plugin has been installed into Workspace. Other team members can also access the plugin.
-
+
### Packing Plugin
diff --git a/en/plugins/quick-start/develop-plugins/model-plugin/create-model-providers.md b/en/plugins/quick-start/develop-plugins/model-plugin/create-model-providers.md
index 5a749771b..aea8e2657 100644
--- a/en/plugins/quick-start/develop-plugins/model-plugin/create-model-providers.md
+++ b/en/plugins/quick-start/develop-plugins/model-plugin/create-model-providers.md
@@ -27,7 +27,7 @@ dify plugin init
Plugins are divided into three types: tools, models, and extensions. All templates in the scaffolding tool provide complete code projects. This example will use an `LLM` type plugin.
-
Plugin type: llm
+
#### **Configure Plugin Permissions**
@@ -37,7 +37,7 @@ Configure the following permissions for this LLM plugin:
* LLM
* Storage
-
Model Plugin Permission
+
#### **Model Type Configuration**
diff --git a/en/plugins/quick-start/develop-plugins/tool-plugin.md b/en/plugins/quick-start/develop-plugins/tool-plugin.md
index 072917708..8e1649dbf 100644
--- a/en/plugins/quick-start/develop-plugins/tool-plugin.md
+++ b/en/plugins/quick-start/develop-plugins/tool-plugin.md
@@ -2,7 +2,7 @@
Tool type plugins are external tools that can be referenced by Chatflow / Workflow / Agent application types to enhance the capabilities of Dify applications. For example, adding online search capabilities, image generation capabilities, etc. to an application. Tool Type Plugins provide a complete set of tools and API implementations.
-
+
In this article, a "Tool Plugin" refers to a complete project that includes the tool provider file, functional code, and other related components. A tool provider may encompass multiple Tools (which can be understood as additional functionalities offered by a single tool), structured as follows:
@@ -320,7 +320,7 @@ REMOTE_INSTALL_KEY=****-****-****-****-****
Run the `python -m main` command to launch the plugin. You can see on the plugin page that the plugin has been installed into Workspace. Other team members can also access the plugin.
-
+
### Packing Plugin
diff --git a/en/plugins/quick-start/install-plugins.md b/en/plugins/quick-start/install-plugins.md
index 866ffaaeb..e0c81856f 100644
--- a/en/plugins/quick-start/install-plugins.md
+++ b/en/plugins/quick-start/install-plugins.md
@@ -8,7 +8,7 @@ description: 'Author: Allen'
To install plugins, click **"Plugins"** in the top-right corner of the Dify platform to access the plugin management page. You can install plugins via **Marketplace, GitHub, or Manual Upload**.
-
Install Plugins
+
#### Marketplace
@@ -20,7 +20,7 @@ Browse and select a plugin from the Marketplace. Click **"Install"** to add it t
Install plugins directly using GitHub repository links. This method requires plugins to meet code standards and include a `.difypkg` file attached to a Release. For more details, see [Publishing Plugins on GitHub](../publish-plugins/publish-plugin-on-personal-github-repo.md).
-
GitHub Installation
+
#### Local Upload
@@ -44,7 +44,7 @@ Once installed, plugins can be integrated into your Dify applications. Below are
For example, after installing the `OpenAI` model plugin, click **Profile → Settings → Model Providers** in the top-right corner to configure your API Key and activate the provider.
-
Authorize OpenAI API Key
+
Once authorized, the model can be used in all application types.
@@ -78,7 +78,7 @@ In the plugin's input variables, enter the user query or other required informat
For usage methods of other plugin types, refer to their respective plugin detail pages.
-
Using Plugins
+
***
diff --git a/en/plugins/schema-definition/endpoint.md b/en/plugins/schema-definition/endpoint.md
index 5d2bdcd70..fe46fa477 100644
--- a/en/plugins/schema-definition/endpoint.md
+++ b/en/plugins/schema-definition/endpoint.md
@@ -6,11 +6,11 @@ In this article, we will use the [Quick Start: Rainbow Cat project](../develop-p
An `Endpoint` group is a collection of multiple `Endpoints`. When creating a new `Endpoint` in a `Dify` plugin, you may need to fill in the following configurations.
-
+
Besides the `Endpoint Name`, you can add new form items by writing group configuration information. After saving, you'll see multiple interfaces that will use the same configuration information.
-
+
#### **Structure**
diff --git a/en/policies/agreement/get-compliance-report.md b/en/policies/agreement/get-compliance-report.md
index 05d785366..c838c5f5d 100644
--- a/en/policies/agreement/get-compliance-report.md
+++ b/en/policies/agreement/get-compliance-report.md
@@ -33,7 +33,7 @@ A **SOC 2 Type I** report is a third-party audit report that evaluates and confi
Only team owners can download Dify's SOC 2 Type I report via **Top-right → Compliance**.
-
SOC 2 Type I Report
+
#### **SOC 2 Type II Report**
@@ -41,13 +41,13 @@ A **SOC 2 Type II** report is a third-party audit report that evaluates and conf
Only team owners can download Dify's SOC 2 Type I report via **Top-right → Compliance**.
-
SOC 2 Type II Report
+
**ISO 27001: 2022 Certificate**
The **ISO 27001:2022** certificate is an internationally recognized standard for **information security management systems (ISMS)**. It is part of the **ISO/IEC 27000 family of standards**, which focuses on protecting sensitive information through a comprehensive set of security controls. The ISO 27001:2022 standard is designed to help organizations establish, implement, maintain, and continually improve their information security management system. The **2022** version of the standard reflects the latest updates and best practices in information security, aligning with current security challenges and technological advancements. It provides a systematic approach to managing sensitive company information, ensuring its confidentiality, integrity, and availability through the implementation of risk management and security controls.
-
ISO 27001: 2022 Certificate
+
#### **GDPR Data Protection Agreement**
@@ -55,5 +55,5 @@ A **Data Protection Agreement (DPA)** is a legally binding contract between a **
**Everyone** can Download Dify's DPA in our official website.
-
DPA
+
diff --git a/en/workshop/basic/build-ai-image-generation-app.md b/en/workshop/basic/build-ai-image-generation-app.md
index 788f0589e..23e7e5df1 100644
--- a/en/workshop/basic/build-ai-image-generation-app.md
+++ b/en/workshop/basic/build-ai-image-generation-app.md
@@ -6,7 +6,7 @@ With the rise of image generation, many excellent image generation products have
In this article, you will learn how to develop an AI image generation app using Dify.
-
+
## You Will Learn
@@ -24,7 +24,7 @@ If you haven't registered yet, you will be asked to register before entering the
After entering the management page, click `copy` to copy the key.
-
+
Next, you need to fill in the key in [Dify - Tools - Stability](https://cloud.dify.ai/tools) by following these steps:
@@ -33,7 +33,7 @@ Next, you need to fill in the key in [Dify - Tools - Stability](https://cloud.di
* Select Stability
* Click `Authorize`
-
+
* Fill in the key and save
@@ -47,7 +47,7 @@ If the message credits are insufficient, you can customize other model providers
Click **Your Avatar - Settings - Model Provider**
-
+
If you haven't found a suitable model provider, the groq platform provides free call credits for LLMs like Llama.
@@ -57,17 +57,17 @@ Click **Create API Key**, set a desired name, and copy the API Key.
Back to **Dify - Model Providers**, select **groqcloud**, and click **Setup**.
-
+
Paste the API Key and save.
-
+
## 3. Build an Agent
Back to **Dify - Studio**, select **Create from Blank**.
-
+
In this experiment, we only need to understand the basic usage of Agent.
@@ -79,21 +79,21 @@ An Agent is an AI system that simulates human behavior and capabilities. It inte
Select **Agent**, fill in the name.
-
+
Next, you will enter the Agent orchestration interface as shown below.
-
+
Select the LLM. Here we use Llama-3.1-70B provided by groq as an example:
-
+
Select Stability in **Tools**:
-
+
-
+
### Write Prompts
@@ -113,17 +113,17 @@ Each time the user inputs a command, the Agent will know this system-level instr
For example: Draw a girl holding an open book.
-
+
### Don't want to write prompts? Of course you can!
Click **Generate** in the upper right corner of Instructions.
-
+
Enter your requirements in the **Instructions** and click **Generate**. The generated prompts on the right will show AI-generated prompts.
-
+
However, to develop a good understanding of prompts, we should not rely on this feature in the early stages.
@@ -131,7 +131,7 @@ However, to develop a good understanding of prompts, we should not rely on this
Click the publish button in the upper right corner, and after publishing, select **Run App** to get a web page for an online running Agent.
-
+
Copy the URL of this web page to share with other friends.
@@ -139,7 +139,7 @@ Copy the URL of this web page to share with other friends.
We can add style instructions in the user's input command, for example: Anime style, draw a girl holding an open book.
-
+
But if we want set the default style to anime style, we can add it to the system prompt because we previously learned that the system prompt is known each time the user command is executed and has a higher priority.
@@ -165,10 +165,10 @@ If the user requests content unrelated to drawing, reply: "Sorry, I don't unders
For example, let's ask: What's for dinner tonight?
-
+
In some more formal business scenarios, we can call a sensitive word library to refuse user requests.
Add the keyword "dinner" in **Add Feature - Content Moderation**. When the user inputs the keyword, the Agent app outputs "Sorry, I don't understand what you're saying."
-
+
diff --git a/en/workshop/intermediate/article-reader.md b/en/workshop/intermediate/article-reader.md
index 94ebde5d4..37bf01a1e 100644
--- a/en/workshop/intermediate/article-reader.md
+++ b/en/workshop/intermediate/article-reader.md
@@ -47,7 +47,7 @@ Please choose the appropriate file upload method according to your business scen
Thus, Dify introduced the **doc extractor**, which can extract text from the file variable and output it as a text variable.
-
+
### **LLM**
@@ -116,11 +116,11 @@ To handle multiple uploaded files, an iterative node is needed.
The iterative node is similar to the while loop in many programming languages, except that Dify has no conditional restrictions, and the **input variable can only be of type `array` (list)**. The reason is that Dify will execute all the content in the list until it is done.
-
+
Therefore, you need to adjust the file variable in the start node to an `array` type, i.e., a file list.
-
+
## **Question 2: Handling Specific Files from a File List**
@@ -130,4 +130,4 @@ For example, limit the analysis to only document-type files and sort the files t
Before the iterative node, add a list operation, adjust the **filter condiftion** and **order by**, then change the input of the iterative node to the output of the list operation node.
-
+
diff --git a/en/workshop/intermediate/customer-service-bot.md b/en/workshop/intermediate/customer-service-bot.md
index 9165612f2..fbbf605b5 100644
--- a/en/workshop/intermediate/customer-service-bot.md
+++ b/en/workshop/intermediate/customer-service-bot.md
@@ -26,13 +26,13 @@ Before starting the experiment, remember that the core of the knowledge base is
In Dify, select **Create from Blank - Chatflow.**
-
+
#### Add a Model Provider
This experiment involves using embedding models. Currently, supported embedding model providers include OpenAI and Cohere. In Dify's model providers, those with the `TEXT EMBEDDING` label are supported. Ensure you have added at least one and have sufficient balance.
-
+
> **What is embedding?**
>
@@ -56,7 +56,7 @@ We will start with a uploading local document as an example.
After uploading the document, you will enter the following page:
-
+
You can see a segmentation preview on the right. The default selection is automatic segmentation and cleaning. Dify will automatically divide the article into many paragraphs based on the content. You can also set other segmentation rules in the custom settings.
@@ -78,11 +78,11 @@ Dify provides three retrieval functions: vector retrieval, full-text retrieval,
In hybrid retrieval, you can set weights or use a reranking model. When setting weights, you can set whether the retrieval should focus more on semantics or keywords. For example, in the image below, semantics account for 70% of the weight, and keywords account for 30%.
-
+
Clicking **Save and Process** will process the document. After processing, the document can be used in the application.
-
+
#### Syncing from a Website
@@ -90,7 +90,7 @@ In many cases, we need to build a smart customer service bot based on help docum
Currently, Dify supports processing up to 50 pages. Please pay attention to the quantity limit. If exceeded, you can create a new knowledge base.
-
+
#### Adjusting Knowledge Base Content
@@ -123,7 +123,7 @@ Here is a reference classification:
* User requests explanation of technical terms
* User asks about joining the community
-
+
#### Direct Reply Node
@@ -155,7 +155,7 @@ Context: You need to use the output of the knowledge retrieval node as the conte
System prompt: Based on \{{context\}}, answer \{{user question\}}
-
+
You can use `/` or `{` to reference variables in the prompt writing area. In variables, variables starting with `sys.` are system variables. Please refer to the help documentation for details.
@@ -165,7 +165,7 @@ In addition, you can enable LLM memory to make the user's conversation experienc
In the knowledge base function, you can connect external knowledge bases through external knowledge base APIs, such as the AWS Bedrock knowledge base.
-
+
For best practices on AWS Bedrock knowledge bases, please read: [how-to-connect-aws-bedrock.md](../../learn-more/use-cases/how-to-connect-aws-bedrock.md "mention")
@@ -173,7 +173,7 @@ For best practices on AWS Bedrock knowledge bases, please read: [how-to-connect-
In both the community edition and SaaS version of Dify, you can add, delete, and query the status of knowledge bases through the knowledge base API.
-
+
In the instance with the knowledge base deployed, go to **Knowledge Base -> API** and create an API key. Please keep the API key safe.
diff --git a/en/workshop/intermediate/twitter-chatflow.md b/en/workshop/intermediate/twitter-chatflow.md
index f9c4b0664..92fbb74b2 100644
--- a/en/workshop/intermediate/twitter-chatflow.md
+++ b/en/workshop/intermediate/twitter-chatflow.md
@@ -59,7 +59,7 @@ docker compose up -d
Configure Model Provider in account setting:
-
+
## Create a chatflow
@@ -67,15 +67,15 @@ Now, let's get started on the chatflow.
Click on `Create from Blank` to start:
-
+
The initialized chatflow should be like:
-
+
## Add nodes to chatflow
-
The final chatflow looks like this
+
### Start node
@@ -83,7 +83,7 @@ In start node, we can add some system variables at the beginning of a chat. In t
Click on Start node and add a new variable:
-
+
### Code node
@@ -100,27 +100,27 @@ def main(id: str) -> dict:
Add a code node and select python, and set input and output variable names:
-
+
### HTTP request node
Based on the [Crawlbase docs](https://crawlbase.com/docs/crawling-api/scrapers/#twitter-profile), to scrape a Twitter user’s profile in http format, we need to complete HTTP request node in the following format:
-
+
Importantly, it is best not to directly enter the token value as plain text for security reasons, as this is not a good practice. Actually, in the latest version of Dify, we can set token values in **`Environment Variables`**. Click `env` - `Add Variable` to set the token value, so plain text will not appear in the node.
Check [https://crawlbase.com/dashboard/account/docs](https://crawlbase.com/dashboard/account/docs) for your crawlbase API Key.
-
+
By typing `/` , you can easily insert the API Key as a variable.
-
+
Tap the start button of this node to check whether it works correctly:
-
+
### LLM node
@@ -130,17 +130,17 @@ The value `context` should be `body` from HTTP Request node.
The following is a sample system prompt.
-
+
## Test run
Click `Preview` to start a test run and input twitter user id in `id`
-
+
For example, I want to analyze Elon Musk's tweets and write a tweet about global warming in his tone.
-
+
Does this sound like Elon? lol
diff --git a/image_conversion.log b/image_conversion.log
new file mode 100644
index 000000000..9cf3b22bd
--- /dev/null
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diff --git a/jp/community/docs-contribution.md b/jp/community/docs-contribution.md
index 62b694f17..e860da5b0 100644
--- a/jp/community/docs-contribution.md
+++ b/jp/community/docs-contribution.md
@@ -14,7 +14,7 @@ Dify のヘルプドキュメントは、[オープンソースプロジェク
ドキュメントを読んでいる際に内容の誤りを見つけたり、一部を修正したい場合は、文書ページの右側にある目次内の **“Github に編集”** ボタンをクリックしてください。これにより、GitHub のオンラインエディターを使用してファイルを修正できます。その後、修正内容を簡潔に説明した pull request を作成してください。タイトルは `Fix: Update xxx` の形式を使用してください。リクエストを受け取った後、レビューを行い、問題がなければ修正をマージします。
-
+
もちろん、[Issues ページ](https://github.com/langgenius/dify-docs/issues)にドキュメントのリンクを貼り付け、修正が必要な内容を簡単に説明していただくことも可能です。フィードバックを受け取った後、迅速に対応いたします。
diff --git a/jp/development/models-integration/gpustack.md b/jp/development/models-integration/gpustack.md
index 78c079f15..c02269c2b 100644
--- a/jp/development/models-integration/gpustack.md
+++ b/jp/development/models-integration/gpustack.md
@@ -36,7 +36,7 @@ GPUStackにホストされたLLMを使用する方法の例です：
3. モデルを展開するために「Save」をクリックします。
-
+
## APIキーの作成方法
@@ -60,6 +60,6 @@ GPUStackにホストされたLLMを使用する方法の例です：
モデルをアプリケーションで使用するために、「Save」をクリックしてください。
-
+
GPUStackに関する詳細情報は、[Github Repo](https://github.com/gpustack/gpustack)を参照してください。
\ No newline at end of file
diff --git a/jp/development/models-integration/hugging-face.md b/jp/development/models-integration/hugging-face.md
index b3e778547..b75d1e2b2 100644
--- a/jp/development/models-integration/hugging-face.md
+++ b/jp/development/models-integration/hugging-face.md
@@ -11,7 +11,7 @@ Difyはテキスト生成（Text-Generation）と埋め込み（Embeddings）を
2. Hugging FaceのAPIキーを設定します（[取得はこちら](https://huggingface.co/settings/tokens)）。
3. [Hugging Faceのモデル一覧ページ](https://huggingface.co/models)にアクセスし、対応するモデルの種類を選択します。
-
+
DifyはHugging Face上のモデルを次の2つの方法で接続できます：
@@ -24,17 +24,17 @@ DifyはHugging Face上のモデルを次の2つの方法で接続できます：
モデルの詳細ページの右側にHosted Inference APIのセクションがあるモデルのみがHosted Inference APIをサポートしています。以下の図のように表示されます：
-
+
モデルの詳細ページで、モデルの名前を取得できます。
-
+
#### 2 Difyで接続モデルを使用する
`設定 > モデルプロバイダー > Hugging Face > モデルタイプ`のエンドポイントタイプでHosted Inference APIを選択します。以下の図のように設定します：
-
+
APIトークンは記事の冒頭で設定したAPIキーです。モデル名は前のステップで取得したモデル名を入力します。
@@ -44,26 +44,26 @@ APIトークンは記事の冒頭で設定したAPIキーです。モデル名
モデルの詳細ページの右側にある`Deploy`ボタンの下にInference EndpointsオプションがあるモデルのみがInference Endpointをサポートしています。以下の図のように表示されます：
-
+
#### 2 モデルをデプロイ
モデルのデプロイボタンをクリックし、Inference Endpointオプションを選択します。以前にクレジットカードを登録していない場合は、カードの登録が必要です。手順に従って進めてください。カードを登録した後、以下の画面が表示されます：必要に応じて設定を変更し、左下のCreate EndpointボタンをクリックしてInference Endpointを作成します。
-
+
モデルがデプロイされると、エンドポイントURLが表示されます。
-
+
#### 3 Difyで接続モデルを使用する
`設定 > モデルプロバイダー > Hugging Face > モデルタイプ`のエンドポイントタイプでInference Endpointsを選択します。以下の図のように設定します：
-
+
APIトークンは記事の冒頭で設定したAPIキーです。`テキスト生成モデルの名前は任意に設定可能ですが、埋め込みモデルの名前はHugging Faceの名前と一致する必要があります。`エンドポイントURLは前のステップでデプロイしたモデルのエンドポイントURLを入力します。
-
+
> 注意点：埋め込みの「ユーザー名 / 組織名」は、Hugging Faceの[Inference Endpoints](https://huggingface.co/docs/inference-endpoints/guides/access)のデプロイ方法に基づいて、「[ユーザー名](https://huggingface.co/settings/account)」または「[組織名](https://ui.endpoints.huggingface.co/)」を入力する必要があります。
diff --git a/jp/development/models-integration/litellm.md b/jp/development/models-integration/litellm.md
index fe434ae6a..6b33876bc 100644
--- a/jp/development/models-integration/litellm.md
+++ b/jp/development/models-integration/litellm.md
@@ -51,7 +51,7 @@ docker run \
`設定 > モデルプロバイダー > OpenAI-API互換` で、以下を入力してください：
-
+
- モデル名: `gpt-4`
- ベースURL: `http://localhost:4000`
diff --git a/jp/development/models-integration/ollama.md b/jp/development/models-integration/ollama.md
index 7efe36013..63243f9f2 100644
--- a/jp/development/models-integration/ollama.md
+++ b/jp/development/models-integration/ollama.md
@@ -1,6 +1,6 @@
# Ollamaでデプロイしたローカルモデルを統合
-
+
[Ollama](https://github.com/jmorganca/ollama)は、Llama 2、Mistral、LlavaといったLLMを簡単にデプロイできるように設計された、クロスプラットフォーム対応の推論フレームワーククライアントです（MacOS、Windows、Linuxに対応）。ワンクリックでセットアップできるOllamaを利用すれば、LLMをローカル環境で実行でき、データを手元のマシンに保持することで、データプライバシーとセキュリティを強化できます。
@@ -54,7 +54,7 @@
4. Ollamaモデルの使用
- 
+ 
設定が必要なアプリの「プロンプトエンジニアリング」ページを開き、Ollamaプロバイダーの中から`llava`モデルを選択し、モデルパラメーターを設定して使用します。
diff --git a/jp/development/models-integration/replicate.md b/jp/development/models-integration/replicate.md
index fcaf186d3..b84e9570c 100644
--- a/jp/development/models-integration/replicate.md
+++ b/jp/development/models-integration/replicate.md
@@ -9,8 +9,8 @@ DifyはReplicate上の[言語モデル](https://replicate.com/collections/langua
3. モデルを選択します。[言語モデル](https://replicate.com/collections/language-models)と[埋め込みモデル](https://replicate.com/collections/embedding-models)からモデルを選びます。
4. Difyの`設定 > モデルプロバイダ > Replicate`にてモデルを追加します。
-
+
APIキーは第2ステップで設定したAPIキーです。モデル名とモデルバージョンはモデルの詳細ページで見つけることができます：
-
+
diff --git a/jp/development/models-integration/xinference.md b/jp/development/models-integration/xinference.md
index 932f03da0..9d4834dda 100644
--- a/jp/development/models-integration/xinference.md
+++ b/jp/development/models-integration/xinference.md
@@ -29,7 +29,7 @@ Xinferenceのデプロイ方法は、[ローカルデプロイ](https://github.c
`http://127.0.0.1:9997`にアクセスし、デプロイするモデルとその仕様を選択します。以下の図を参照してください：
-
+ 
モデルによっては異なるハードウェアプラットフォームでの互換性が異なるため、[Xinference内蔵モデル](https://inference.readthedocs.io/en/latest/models/builtin/index.html)を確認して、作成するモデルが現在のハードウェアプラットフォームでサポートされているかどうかを確認してください。
4. モデルUIDの取得
diff --git a/jp/explore/images/creat-customize-app.jpg b/jp/explore/images/creat-customize-app.jpg
deleted file mode 100644
index aa6db329e..000000000
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diff --git a/jp/explore/images/explore-app.jpg b/jp/explore/images/explore-app.jpg
deleted file mode 100644
index c41022fda..000000000
Binary files a/jp/explore/images/explore-app.jpg and /dev/null differ
diff --git a/jp/explore/images/workspace.jpg b/jp/explore/images/workspace.jpg
deleted file mode 100644
index 764725143..000000000
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diff --git a/jp/features/datasets/maintain-dataset-via-api.md b/jp/features/datasets/maintain-dataset-via-api.md
new file mode 100644
index 000000000..4fbde6fff
--- /dev/null
+++ b/jp/features/datasets/maintain-dataset-via-api.md
@@ -0,0 +1,139 @@
+# APIによるデータセットの維持
+
+> 認証、メソッド呼び出し、アプリケーションサービスAPIは一貫性を保ちます。ナレッジAPIトークンの違いは、すべてのナレッジベースで操作できることです。
+
+### ナレッジAPIを使用する利点
+* Difyデータセットを使ってデータシステムを同期化し、強力なワークフローを作成します。
+* データセットリスト、ドキュメントリスト、詳細クエリを提供し、独自のデータ管理ページを生成しやすくします。
+* プレーンテキストとファイルのアップロード、ドキュメントの更新。セグメントレベルでの一括追加・変更に対応し、同期プロセスをします。
+* Difyソフトウェアおよびサービスの可視性を向上させ、手動でのドキュメント処理や同期の時間を短縮します。
+
+### 使用方法
+
+ナレッジページに移動し、左側のナビゲーションでAPIページに切り替えます。このページでは、difyが提供するAPIドキュメントを表示し、ナレッジAPIにアクセスするための認証情報を管理できます。
+
+
+
+## **空のナレッジを作成する**
+
+**`POST /datasets`**
+
+{% hint style="warning" %}
+空のデータセットを作成するためだけに使用します
+{% endhint %}
+
+```
+curl --location --request POST 'https://api.dify.ai/v1/datasets' \
+--header 'Authorization: Bearer {api_key}' \
+--header 'コンテンツタイプ: application/json' \
+--data-raw '{"name": "name"}'
+```
+
+#### **ナレッジリスト**
+
+```
+curl --location --request GET 'https://api.dify.ai/v1/datasets?page=1&limit=20' \
+--header 'Authorization: Bearer {api_key}'
+```
+
+#### **テキストでドキュメントを作成**
+
+```
+curl --location --request POST '/document/create_by_text>' \\
+--header 'Authorization: Bearer {api_key}' \\
+--header 'コンテンツタイプ: application/json' \\
+--data-raw '{
+ "name": "Dify",
+ "text": "Dify means Do it for you...",
+ "indexing_technique": "高品質",
+ "process_rule": {
+ "rules": {
+ "pre_processing_rules": [{
+ "id": "余分なスペースを削除",
+ "enabled": true
+ }, {
+ "id": "URLとメールを削除",
+ "enabled": true
+ }],
+ "segmentation": {
+ "separator": "###",
+ "max_tokens": 500
+ }
+ },
+ "mode": "カスタム"
+ }
+}'
+```
+
+#### **ファイルでドキュメントを作成**
+
+```
+curl --location POST 'https://api.dify.ai/v1/datasets/{データセットID}/document/create_by_file' \
+--header 'Authorization: Bearer {api_key}' \
+--form 'data="{
+ "name": "Dify",
+ "indexing_technique": "高品質",
+ "process_rule": {
+ "rules": {
+ "pre_processing_rules": [{
+ "id": "余分なスペースを削除",
+ "enabled": true
+ }, {
+ "id": "URLとメールを削除",
+ "enabled": true
+ }],
+ "segmentation": {
+ "separator": "###",
+ "max_tokens": 500
+ }
+ },
+ "mode": "カスタム"
+ }
+ }";
+ タイプ=テキスト/プレーン' \
+--form 'file=@"/パス/ファイル"'
+```
+
+#### **ドキュメントのインデックスステータスを取得**
+
+```
+curl --location --request GET 'https://api.dify.ai/v1/datasets/{データセットID}/documents/{batch}/indexing-status' \
+--header 'Authorization: Bearer {api_key}'
+```
+
+#### **ドキュメントを削除**
+
+```
+curl --location --request GET 'https://api.dify.ai/v1/datasets/{データセットID}/documents' \
+--header 'Authorization: Bearer {api_key}'
+```
+
+#### **新しいセグメントを追加**
+
+```
+curl --location 'https://api.dify.ai/v1/datasets/{dataset_id}/documents/{document_id}/segments' \
+--header 'Authorization: Bearer {api_key}' \
+--header 'Content-Type: application/json' \
+--data '{"segments": [{"content": "1","answer": "1","keywords": ["a"]}]}'
+```
+
+#### 文書セグメントの削除
+
+```
+curl --location --request DELETE 'https://api.dify.ai/v1/datasets/{dataset_id}/documents/{document_id}/segments/{segment_id}' \
+--header 'Authorization: Bearer {api_key}' \
+--header 'Content-Type: application/json'
+```
+
+### エラーメッセージ
+
+- `document_indexing`、ドキュメントがインデックス化中であることを示します
+- `provider_not_initialize`、埋め込みモデルが設定されていないことを示します
+- `not_found`、ドキュメントが見つからないことを示します
+- `dataset_name_duplicate`、重複した名前が付けられていることを示します
+- `provider_quota_exceeded`、プロバイダーのクォータが最大制限を超えたことを示します
+- `dataset_not_initialized`、データセットが初期化されていないことを示します
+- `unsupported_file_type`、サポートされていないファイルタイプであることを示します
+ - 現在サポートされているファイルタイプは次のとおりです：txt、markdown、md、pdf、html、htm、xlsx、docx、csv
+- `too_many_files`、ファイルの数が多すぎることを示します。現在は単一ファイルのアップロードのみサポートします
+- `file_too_large`、ファイルが大きすぎることを示します。15M以下のファイルのみサポートします
\ No newline at end of file
diff --git a/jp/features/datasets/sync-from-notion.md b/jp/features/datasets/sync-from-notion.md
new file mode 100644
index 000000000..1891fd939
--- /dev/null
+++ b/jp/features/datasets/sync-from-notion.md
@@ -0,0 +1,74 @@
+# コンセプト同期
+
+Difyのコンセプトは、コンセプトからデータをインポートして **同期** を設定し、コンセプトが更新された後に自動的にデータを同期して更新することをサポートしています。
+
+### 認証
+
+1. ナレッジベースを作成する際に、データソースを選択し、**コンセプト同期--接続へ移動** をクリックし、指示に従って認証検証を完了してください。
+2. また、**設定--データソース--データソースの追加** をクリックし、コンセプトソースの **接続** をクリックして認証を完了することもできます。
+
+
+
+### データインポート
+
+認証操作が完了したら、ナレッジベースの構築ページに移動し、**コンセプト同期** をクリックして、インポートする必要な認証ページを選択します。
+
+### セグメンテーションクレンジング
+
+次に、**セグメンテーション設定** と **インデックス方法** を選択し、**保存して処理** をクリックします。Difyがデータを処理するのを待ちます。このステップでは通常、LLMプロバイダーでトークンを使用する必要があります。一般的なページタイプのインポートだけでなく、データベースタイプのページ属性も集計して保存できます。
+
+_**注: 現在、画像やファイルのインポートはサポートしていません。表データはテキストに変換されます。**_
+
+### コンセプトデータの同期
+
+コンセプト内容が変更された場合、Difyのナレッジドキュメントリストページで[同期]ボタンをクリックしてデータを一括で同期できます（注意: 各クリックで現在の内容が同期されます）。このステップでもトークンの使用が必要です。
+
+
+
+### (コミュニティ版) コンセプト統合設定ガイド
+
+統合は **内部統合** と **公共統合** の2つの方法があります。これらは必要に応じてDifyで設定できます。
+
+これら2つの統合方法の具体的な違いについては、[コンセプト公式説明](https://developers.notion.com/docs/authorization) を参照してください。
+
+#### 1. **内部統合を使用**
+
+あなたの統合ページを [統合設定](https://www.notion.so/my-integrations) で作成します。デフォルトでは、すべての統合は内部統合から始まります。内部統合は選択したワークスペースに関連付けられるため、ワークスペースの所有者である必要があります。
+
+**具体的な操作手順:**
+
+" **New integration** " ボタンをクリックすると、デフォルトで内部（変更不可）となります。関連するスペースを選択し、名前を入力してロゴをアップロードし、"**提出**" をクリックして統合を成功させます。
+
+
+
+統合が作成されると、必要に応じて設定を更新できます。 **性能** タブを選択し、 **シークレットキー** を表示するために "**表示**" ボタンをクリックしてシークレットキーをコピーします。
+
+
+
+それをコピーしてDifyのソースコードに戻り、関連する環境変数を **.env** ファイルに設定します。環境変数は以下の通りです：
+
+**NOTION\_INTEGRATION\_TYPE** = 内部 または **NOTION\_INTEGRATION\_TYPE** = 公共
+
+**NOTION\_INTERNAL\_SECRET**= あなたの内部シークレットキー
+
+#### 2. **公共統合を使用**
+
+**内部統合を公共統合にアップグレードする必要があります**。統合配布ページに移動し、統合を表示するためにスイッチを切り替えます。
+
+スイッチを公共設定に切り替えるには、**組織情報に追加情報を入力** する必要があります。会社名、Webサイト、およびリダイレクト目標パスを含む追加情報を入力してから、 "提出" ボタンをクリックします。
+
+
+
+公共統合が成功した後、[統合設定ページ](https://www.notion.so/my-integrations) で、[シークレットキー] タブにアクセスして統合のシークレットキーを確認できます。
+
+
+
+Difyのソースコードに戻り、関連する環境変数を **.env** ファイルに設定します。環境変数は以下の通りです：
+
+**NOTION\_INTEGRATION\_TYPE**=公共
+
+**NOTION\_CLIENT\_SECRET**=あなたのクライアントシークレットキー
+
+**NOTION\_CLIENT\_ID**=あなたのクライアントID
+
+設定が完了すると、ナレッジセクション内のコンセプトデータのインポートおよび同期機能を使用できるようになります。
\ No newline at end of file
diff --git a/jp/getting-started/install-self-hosted/zeabur.md b/jp/getting-started/install-self-hosted/zeabur.md
index 64947e810..2769a0a39 100644
--- a/jp/getting-started/install-self-hosted/zeabur.md
+++ b/jp/getting-started/install-self-hosted/zeabur.md
@@ -17,15 +17,15 @@ Zeabur チームはワンクリックデプロイテンプレートを用意し
ボタンをクリックすると、Zeabur 上のテンプレートページに移動し、デプロイの詳細情報と説明を確認できます。
-
+
デプロイボタンをクリックした後、生成されたドメイン名を入力し、そのドメイン名を Dify インスタンスにバインドし、他のサービスに環境変数として注入します。
次に、お好みのリージョンを選択し、デプロイボタンをクリックすると、数分以内に Dify インスタンスがデプロイされます。
-
+
デプロイが完了すると、Zeabur コンソール上にプロジェクトページが表示されます。以下の図のように、デプロイ中に入力したドメイン名が自動的に NGINX サービスにバインドされ、そのドメイン名を使用して Dify インスタンスにアクセスできます。
-
+
また、NGINX サービスページのネットワーキングタブでドメイン名を変更することもできます。詳細については [Zeabur ドキュメント](https://zeabur.com/docs/deploy/domain-binding) を参照してください。
\ No newline at end of file
diff --git a/jp/guides/annotation/annotation-reply.md b/jp/guides/annotation/annotation-reply.md
index a7b1913ba..ffeb4e03c 100644
--- a/jp/guides/annotation/annotation-reply.md
+++ b/jp/guides/annotation/annotation-reply.md
@@ -17,13 +17,13 @@
4. マッチング項目が見つからなかった場合、質問は通常プロセスを継続します（LLMやRAGに渡されます）。
5. アノテーションリプライ機能をオフにすると、システムはアノテーションからのマッチングリプライを継続して行いません。
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/jp/guides/annotation/logs.md b/jp/guides/annotation/logs.md
index 995366745..4ccadedfb 100644
--- a/jp/guides/annotation/logs.md
+++ b/jp/guides/annotation/logs.md
@@ -24,7 +24,7 @@
これらのマークは、Dify の後続バージョンでモデルの微調整（モデルの微調整）に使用され、モデルの正確性と返信スタイルを向上させることを目的としています。現在のプレビュー版では、マークのみがサポートされています。
{% endhint %}
-
+
4. Forwardingで示されるように、上の図では`https://177e-159-223-41-52.ngrok-free.app`（これは例のドメインです。自分のドメインに置き換えてください）がパブリックドメインとなります。
diff --git a/jp/guides/extension/api-based-extension/cloudflare-workers.md b/jp/guides/extension/api-based-extension/cloudflare-workers.md
index d43f38824..af1849503 100644
--- a/jp/guides/extension/api-based-extension/cloudflare-workers.md
+++ b/jp/guides/extension/api-based-extension/cloudflare-workers.md
@@ -43,9 +43,9 @@ npm run deploy
デプロイが成功すると、公開アドレスが得られます。このアドレスを Dify に API エンドポイントとして追加できます。`endpoint` パスを忘れないようにしてください。
-
Dify に API エンドポイントを追加する
+
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/jp/guides/monitoring/analysis.md b/jp/guides/monitoring/analysis.md
index d14cfe8b5..ddd90361b 100644
--- a/jp/guides/monitoring/analysis.md
+++ b/jp/guides/monitoring/analysis.md
@@ -2,7 +2,7 @@
**監視 — 分析** では、使用量、アクティブユーザー数、大規模言語モデル (LLM) のコール消費などを表示します。これにより、アプリケーションの運営効果、活性度、経済性を継続的に改善できます。さらに多くの有用な可視化能力を段階的に提供していきますので、ぜひご要望をお知らせください。
-
+
**Create API Key**をクリックし，新たな認証情報を作ります。
-
+
**Create API Key**をクリックし，新たな認証情報を作ります。
-
+
この認証を済ませることで、あらゆるアプリタイプでこの高性能な言語モデルを選択し、活用することが可能になります。
-
+
#### SOC 2 Type II レポート
@@ -36,7 +36,7 @@ Difyのクラウド版をサプライヤーの安全評価にご利用の場合
**チームオーナーのみ**、**右上メニューの「コンプライアンス」** から Dify の SOC 2 Type II レポートをダウンロードできます。
-
SOC 2 Type II レポート
+
#### ISO 27001:2022 認証
@@ -44,7 +44,7 @@ Difyのクラウド版をサプライヤーの安全評価にご利用の場合
**チームオーナーのみ**、**右上メニューの「コンプライアンス」** から Dify の ISO 27001:2022 レポートをダウンロードできます。
-
+
登录[ Jina Reader 官网](https://jina.ai/reader) 完成注册，获取 API Key 后并按照页面提示填入并保存。
@@ -44,14 +44,14 @@ Jina Reader 和 Firecrawl 均是开源的网页解析工具，能将网页将其
在知识库创建页选择 **Sync from website**，provider 选中 Jina Reader，填写需要抓取的目标 URL。
-
+
在对话日志中，我们也可以看到 API 返回的实时数据：
-
+
### 常见问题
@@ -21,7 +21,7 @@
* 从 Professional 升级至 Team 需补足当月差额，立即生效。
* 从 Team 降级至 Professional 立即生效。
-
+
3. 创建项目 API 凭据，在项目内左侧边栏中点击 **Settings** 打开设置
-
创建一个项目 API 凭据
+
在 Settings 内点击 **Create API Keys** 创建一个项目 API 凭据。
-
创建一个项目 API 凭据
+
复制并保存 **Secret Key** ，**Public Key，Host**
-
获取 API Key 配置
+
4. 在 Dify 内配置 Langfuse，打开需要监测的应用，在侧边菜单打开**监测**，在页面中选择**配置。**
-
+
点击 **Create API Key**，创建一个项目凭据。
-
创建一个项目 API Key
+
选择 **Personal Access Token** ，用于后续的 API 身份校验。
-
创建一个 API Key
+
将创建的 API key 复制保存。
-
+
列表操作节点一般用于提取数组变量中的信息，通过设置条件将其转化为能够被下游节点所接受的变量类型。它的结构分为**输入变量**、**过滤条件**、**排序（可选）**、**取前 N 项（可选）**、**输出变量**。
-
+
{% hint style="warning" %}
除“`变量`”以外的其他内容块不可重复插入。在不同应用和模型下，可插入的内容块会根据不同的提示词模板结构有所区别，`会话历史`、`查询内容` 仅在对话型应用的文本补全模型中可用。
@@ -149,7 +149,7 @@ And answer according to the language English.
在调试预览界面，用户与 AI 产生对话之后，将鼠标指针移动到任意的用户会话，即可在左上角看到“日志”标志按钮，点击即可查看提示词日志。
-
调试日志入口
+
在日志中，我们可以清晰的查看到：
@@ -157,7 +157,7 @@ And answer according to the language English.
* 当前会话引用的相关文本片段
* 历史会话记录
-
调试预览界面查看提示词日志
+
从日志中，我们可以查看经过系统拼装后最终发送至 LLM 的完整提示词，并根据调试结果持续改进提示词输入。
@@ -165,4 +165,4 @@ And answer according to the language English.
在初始的构建应用主界面，左侧导航栏可以看到“日志与标注”，点击进去即可查看完整的日志。 在日志与标注的主界面，点击任意一个会话日志条目，在弹出的右侧对话框中同样鼠标指针移动到会话上即可点开“日志”按钮查看提示词日志。
-
 (1) (1) (1).png)
.png)
 (1) (1).png)
.png)
.png)
.png)
.png)
.png)
will be displayed on the prompt design page.
@@ -116,7 +116,7 @@ Because LLM stops generating content before the next `Human1:`.
You can enter "/" in the text editor to quickly bring up Blocks to insert into the prompt.
-
.png)
.png)
.png)
.png)
.png)
 (1).png)
.png)
.png)
.png)
.png)
.png)
.png)
.png)
.png)
 (3).png)
 (1).png)
.png)
.png)
.png)
 (1).png)
 (1).png)
 (1).png)
 (1).png)
 (1).png)
 (1) (1) (1).png)
 (1) (2).png)
 (1) (2).png)
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+* **聊天模型** 
在选择模型的时候，模型名字的右侧显示 CHAT 的即为聊天模型，该类模型将消息列表作为输入，并返回模型生成的消息作为输出。尽管聊天格式旨在简化多轮对话，但它对于没有任何对话的单轮任务同样有用。聊天模型使用的是聊天消息作为输入和输出，包含 SYSTEM / USER / ASSISTANT 三种消息类型：
@@ -23,13 +23,13 @@
是指特定的单词、短语或字符，用于向 LLM 发出停止生成文本的信号。
* **专家模式提示词中的内容块**
- * 
+ * 
用户在配置了知识库的 App 中，输入查询内容，App 会将查询内容作为知识库的检索条件，检索的结果在组织之后会作为上下文内容替换 `上下文` 变量，使 LLM 能够参考上下文的内容进行回答。
- * 
+ * 
查询内容仅在对话型应用的文本补全模型中可用，对话中用户输入的内容将替换该变量，以触发每轮新的对话。
- * 
+ * 
会话历史仅在对话型应用的文本补全模型中可用。在对话型应用中多次对话时，Dify 会将历史的对话记录根据内置规则进行组装拼接，并替换 `会话历史` 变量。其中 Human 和 Assistant 前缀可点击 `会话历史` 后的`...` 进行修改。
* **初始模版**
@@ -48,7 +48,7 @@
创建应用后，在提示词编排页可以切换至**专家模式**，在此模式下可以编辑完整的应用提示词。
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