Alibaba Cloud Model Studio:Workflow application

Start and end

• When to use

  • When designing a workflow, define the structure and content of the input/output parameters in the start and end nodes.

• How to use

  • Start node

    Component	Description
    Predefined variables	The workflow provides the following predefined variables to process user input and maintain conversation history: query: Stores the user's text input. historyList: Stores conversation history to maintain context in a multi-turn conversation. To use this variable in nodes that support the memory feature (such as large model and intent classification nodes), select Custom Cache. imageList: Stores user-uploaded images to enable image analysis or multimodal conversation. To use this variable in nodes that support the memory feature (such as large model and intent classification nodes), select Custom Cache.
    Custom variables	Custom variables are structured input parameters that you define for a workflow. They receive data from tests or API calls and can be referenced in subsequent nodes. When creating a custom variable, configure the following parameters: Variable name: Enter a meaningful name. Chinese characters are not supported. Type: The data type of the variable. Supported types are String, Boolean, Number, Object, Array (of String, Boolean, Number, or Object), and File. Description: Briefly describe what the variable does and when to use it.

  • End node

    Component	Description
    Output mode	Text Output: Suitable for unstructured content. In the input box, you can enter fixed content or type / to reference variables, which determines the final result returned to the user. You can source variables from the output of any workflow node or from session variables. Properly mapping output variables allows you to control the workflow's data flow and ensure an accurate and complete final response. JSON Output: Suitable for outputting structured content in JSON format. You can define variable names and enter text or reference variables.
    Streaming output	The Streaming Output switch applies only to Text Output mode. When enabled, responses from large model and application component nodes are streamed token by token. When disabled, a full response is returned after it is completely generated.

Large model

• Why use it

  This node is the intelligent core of your workflow. It can understand language, generate text, analyze images, and engage in multi-turn conversations. You can use it to write copy, summarize text, or even analyze image content (using Qwen-VL models).

• Features

  • Supports both single-input processing and batch processing of large datasets.

  • You can configure different large models, such as Qwen-Plus, so you can choose the right model based on performance, latency, or other requirements.

• Node parameter configuration

  Parameter	Description
  Mode selection	Single Mode: Calls the large model once.
  	Batch Mode: The node runs multiple times, sequentially assigning an item from a list to a batch variable in each run. This process stops when all list items are processed or the configured maximum batch runs is reached. Batch Configuration: Maximum Number of Batches (Range: 1–100; default for standard users: 100): The maximum number of times the batch process will run. Note The number of batch runs depends on the minimum length of the input arrays. If no input variable is configured, the number of runs is determined by the configured batch count. Number of Parallel Runs (Range: 1–10): Sets the concurrency limit for batch processing. A setting of 1 executes all tasks serially.
  Model configuration	Select a suitable large model and adjust its parameters. The available models are listed on the UI. For a detailed introduction to the models, see the model list. For information on the API call rate limits for each model, see rate limiting.
  Parameter configuration	Temperature: Controls the diversity of the generated content. A higher temperature value increases the randomness of the generated text, producing more unique outputs, while a lower value makes the content more conservative and consistent. This configuration is not currently supported for DeepSeek R1 series models.
  	Maximum Reply Length: Limits the maximum length (in tokens) of the text generated by the model, not including the prompt. This limit varies by model.
  	The following parameters are available only when a Qwen-VL model is selected: Model Input Parameters: For vlImageUrl, you can reference a variable or input an image URL. Image Source: Image set: The model treats each uploaded image independently, selecting the most relevant one to answer the user's question. A single image can be passed directly. For example: https://****.com/****.jpg. Multiple images can be passed as a list. For example: ["URL","URL","URL"]. Video frames: The model considers the uploaded images to be from the same video and understands them sequentially as a whole. You must provide a minimum of 4 video frames.
  System prompt	Provides system-level instructions to the model. You can use it to define the model's role, task, or output format. For example: "You are a math expert who specializes in solving math problems. Please output the problem-solving process and result in the correct format."
  User prompt	The user's input to the model, such as a request or instruction. You can configure a prompt template or insert variables. The model uses this input to generate a response.
  Memory	Memory is the context the model retains across multiple turns in a conversation. Current node cache: Uses the output of this node as context. The model will only remember context generated within this node. memory rounds: The number of conversation rounds to remember. One input and its corresponding output constitute one round. Custom cache: Uses a specified context variable as the context. Context variable: Select the source of the context.
  Output variable	The name of the variable that stores the processing result of this node. Subsequent nodes can use this variable to access this node's result. DeepSeek R1 series models support outputting the Deep Thinking process (reasoningContent).
  Retry on failure	If disabled, the node stops executing when an error occurs. If enabled, the node re-executes on error, according to the configured number of retries and interval. maximum retries: The maximum number of retry attempts when a request fails. retry interval: The time interval between each retry attempt, in milliseconds.
  Exception handling	If disabled, the node handles errors using the system's default error handling mechanism. If enabled, the node executes custom processing logic according to the configuration when an error occurs. Default value: The content in result is output when an exception occurs. exception branch: If an exception occurs, this branch is executed. You must configure its processing flow.
  Return results	This feature is deprecated. This option applies only to API calls and determines whether the node's content is output. To learn more about the purpose of this component, see Application calling.

  Note

  To integrate applications via API, see Application calling.

Knowledge base

• Definition

  The knowledge base node implements retrieval-augmented generation (RAG). It retrieves relevant chunks from a specified knowledge base based on the input, and passes the results as context to a downstream large model node. This process addresses common problems with large models, such as outdated knowledge, inability to access private data, and hallucinations.

• Input configuration

  Defines the criteria for querying the knowledge base.

  Parameter	Description
  content	The text used to query the knowledge base. You can enter text directly or type / in the editor to select and reference an output variable from an upstream node.
  imageList	The images used to query the knowledge base. You can enter a publicly accessible image URL, such as https://xxx.xxx.com/xxx/xxx.jpeg, or type / in the editor to select and reference an output variable from an upstream node.

• Knowledge base selection method

  Selection method	Applicable scenarios	Configuration
  Select Fixed Knowledge Base	Each call uses the same, pre-selected knowledge base.	Select a specific knowledge base from the drop-down list. You can add three types of knowledge bases: documents, tables, and images.
  Dynamic Import	The output of an upstream node dynamically determines which knowledge bases to use.	Configure the CodeList variable. You can specify a list of knowledge bases by referencing a variable or by direct input.

• Knowledge base invocation method

  This setting defines the trigger logic for the knowledge base. The default method is always invoke.

  Invocation method	Description	Parameters
  always invoke	Performs a knowledge base retrieval for each user input. This method is suitable for high-frequency question-and-answer scenarios.	None
  intelligent invoke	The agent determines whether to query the knowledge base based on the user's input and its description. This is suitable for flexible conversational scenarios.	Knowledge Base Description (Required)
  legacy invocation	Results from document, table, and image knowledge bases are recalled together. A single topK parameter controls the number of recalled chunks, and does not support separate debugging for each type.	topK (Range: 1–50, Default: 10)

  Parameter descriptions:

  • Knowledge base description: Describes the content of the knowledge base and the conditions under which its data can be used for a response. Supports text input or entering / to insert a variable.

  • topK: The maximum number of chunks to recall from each knowledge base. The actual number of recalled chunks depends on matching results and may be less than the topK value.

• Debug recall results

  This feature is supported only in always invoke and intelligent invoke modes. Click the Debug button next to a document, table, or image type to test the recall results for that knowledge base. Use the results to fine-tune your configuration. When you are done, click Save in the upper-right corner to apply the configuration to the current application.

• Knowledge base filtering

  This feature is disabled by default. When enabled, the system uses a large model to intelligently filter the recalled results from documents and tables, improving the final output quality. This feature is suitable for scenarios that require high answer precision or need to filter out irrelevant information.

• Node output

  The knowledge base node outputs a structured object containing a result object:

  Field	Type	Description
  result	Object	Contains all information returned from the current query.
  result.chunkList	Array<Object>	An array of recalled chunks. This array is empty if the query returns no results.
  result.chunkList[ ].content	String	The original content of the recalled knowledge base chunk.
  result.chunkList[ ].title	String	The title of the document to which the chunk belongs.
  result.chunkList[ ].documentName	String	The name of the document where the recalled chunk is located.
  result.chunkList[ ].score	Number	The similarity score of the knowledge base chunk. A higher score indicates a better match.
  result.chunkList[ ].id	String	The chunk ID.
  result.chunkList[ ].dataId	String	The document ID.
  result.chunkList[ ].docUrl	String	The source file download link.
  result.chunkList[ ].knowledgeBaseId	String	The knowledge base ID.
  result.chunkList[ ].nid	String	The identifier for the original text.
  result.chunkList[ ].images	Array<String>	A list of images.
  result.chunkList[ ].pageNumber	Array<Number>	An array of page numbers.
  result.rewriteQuery	String	The rewritten user query.

API

• Definition

  Calls a custom API service using the POST, GET, PUT, PATCH, or DELETE method and outputs the API call result.

• Parameter configuration

  Parameter	Description
  API address	The request URL for the API. Request method: POST: Submits data to the server to create a new resource. GET: Retrieves a resource without modifying data on the server. PUT: Updates a specified resource or creates a new resource on the server. PATCH: Partially updates a resource on the server. DELETE: Deletes a specified resource from the server. URL: Enter the full API address in the input field. You can enter the address directly or use a variable to reference a value from previous nodes. For example, https://dashscope.aliyuncs.com/compatible-mode/v1/files.
  Header settings	Specifies the HTTP request headers, such as Content-Type and Authorization. You can enter values directly or reference them from previous nodes.
  Parameter settings	Query parameters in the request URL. You can enter values directly or reference them from previous nodes. For example, limit in https://dashscope.aliyuncs.com/compatible-mode/v1/files?limit=5.
  Body settings	Select the correct type based on the API's requirements. none: No request body. Suitable for GET requests. form-data: Form data, used for file uploads or key-value pairs. raw: Raw text, such as JSON or XML. JSON: An automatically formatted JSON object.
  Timeout setting (s)	The maximum time to wait for a response. If a response is not received within this time, the request times out.
  Retry on failure	If disabled, the node stops executing when an error occurs. If enabled, the node retries the request according to the configured maximum retries and retry interval when an error occurs. Maximum retries: The maximum number of times to retry a failed request. Retry interval: The interval between retries, in milliseconds.
  Exception handling	If disabled, the node uses the default error handling mechanism when an error occurs. If enabled, the node executes custom logic when an error occurs. Default value: Outputs the content of result when an exception occurs. Exception branch: Proceeds to the exception branch if an exception occurs. You must configure the processing flow for this branch.
  Output	Stores the API response in a specified variable for use by subsequent nodes.

  Note

  For details on integrating applications with an API, see application integration.

Plugin

• Definition

  You can add a plugin node to your workflow application to perform more complex tasks. Model Studio offers a range of official plugins, such as Quark Search, Calculator, and a Python code interpreter. You can also create custom plugins to meet specific needs.

  See Plugin overview.

• Prerequisites

  • To use an official or third-party plugin, you must first request its activation. For detailed instructions, see Plugin overview.

  • If you want to reference a custom plugin, you need to create a custom plugin first.

• Parameter configuration

  Parameter	Description
  Input	Specifies the content for the node to process. The variables change depending on the selected plugin. When you select a plugin, its built-in variables load automatically. You can go to the Official plugins or Custom plugins page, click a plugin card, and view how to configure its variables.
  Output	The output variable that contains the node's processing result. Subsequent nodes use this variable to access the result.
  Retry on failure	If disabled, the node stops executing when an error occurs. If enabled, the node retries execution based on the configured number of retries and interval when an error occurs. Maximum retries: The maximum number of retry attempts when a request fails. Retry interval: The time interval between each retry, in milliseconds.
  Exception handling	If disabled, the node uses the system's default error handling mechanism. If enabled, the node executes custom logic when an error occurs. Default value: Outputs the content of result when an exception occurs. Exception branch: Executes this branch if an exception occurs. You must configure the processing flow for the branch.

Function Compute

• Definition

  Calls a custom service on Alibaba Cloud Function Compute.

• Parameter configuration

  Important

  The default timeout for a Function Compute node is 60 seconds and cannot be changed.

  Parameter	Description
  Input	The input variable that this node processes. You can reference variables from upstream nodes or enter values directly.
  Region	Select the region where your Function Compute service is located: Singapore, Kuala Lumpur, or Indonesia (Jakarta).
  Service configuration	Select the Function Compute service to invoke. You must first create a function. The Alibaba Cloud account that creates the Function Compute service must be the same as the account you use to log in to Model Studio (Bailian), or it must belong to the same Alibaba Cloud root account.
  Output	The variable that stores the node's output. Subsequent nodes can reference this variable.

Script

• Definition

  Uses a script to transform input into a specific template or output format. This involves parsing, transforming, and formatting the data for consistency and readability.

• Parameter configuration

  Parameter	Description
  input	Accepts a fixed value, or a reference to a variable from an upstream node or a session variable.
  code	Defines the core logic for the node. Both JavaScript and Python are supported. Get input: Use the built-in params object to access input parameters in the format params.<variable_name>. For example, params.input1. Return output: The main handler function must return a dictionary or object. The key-value pairs of this object become the node's output.
  output	The dictionary returned by the return statement in the code serves as the output of this node. For example, if you return {'result': 'Processing successful'}, downstream nodes can access the string "Processing successful" by using node_name.result.
  retry on failure	If disabled, the node stops executing when an error occurs. If enabled, the node retries execution according to the configured maximum retries and retry interval. maximum retries: The maximum number of retry attempts after a failure. retry interval: The interval between retries, in milliseconds.
  exception handling	If disabled, the node uses the system's default error handling mechanism. If enabled, the node executes custom logic when an error occurs. Default value: Outputs the content of result when an exception occurs. exception branch: Routes execution to a separate, user-configured branch if an exception occurs.

Condition

• Definition

  The condition node adds branching logic to a workflow. When a variable meets a specified condition, the workflow follows the corresponding downstream link. The node supports AND/OR configurations and evaluates conditions sequentially from top to bottom.

• Parameter configuration

  Parameter	Description
  conditional branch	Enter the conditional expressions. Conditions in different groups are joined by OR, while conditions within the same group are joined by AND.
  else	The default output when no defined conditions are met.

Intent classification

• Definition

  Intelligently matches user input with a predefined intent based on its description, and then routes to the corresponding link.

• Parameter configuration

  Parameter	Description
  input variable	Specifies the input variable to be classified. You can enter a value directly, or use a variable from a preceding node or a session variable.
  model selection	Supports Qwen-Plus and the intent classification model.
  intent classification	Define the intents for the model to classify. The model matches the input to an intent based on its description and then routes to the corresponding link. For example: "For calculating math problems" or "For answering weather-related questions".
  Other intent	This branch executes when the model determines that the input does not match any of the defined intents.
  intent mode	single-select mode: The large model selects the single best-matching intent from the available configurations. multi-select mode: The large model selects all matching intents from the available configurations.
  reasoning mode	fast mode: This mode improves processing speed by avoiding complex reasoning steps, making it suitable for simple scenarios. quality mode: This mode uses step-by-step reasoning to achieve more accurate classification.
  memory	Refers to the model's ability to remember conversation history in multi-turn conversations, which serves as the context. node-local cache: Uses the output of this node as context. The model's memory is limited to the context generated by this node. memory turns: The number of conversation turns to remember. One input and its corresponding output constitute one turn. custom cache: Uses the content of a specified context variable as the context. context variable: Specifies the source of the context.
  prompt	Provides additional instructions or constraints for the intent classification model. You can add examples or define constraints to help the model's classification results better match your requirements. Example Suppose you are developing a customer service system for an e-commerce platform where users might ask about order inquiries, returns, or payments. To ensure accurate classification, you can add relevant prompts and examples in the advanced settings. Please classify the intent based on the following examples: Example 1: User input "I want to return the jacket I just bought", classify as "Returns and exchanges". Example 2: User input "Please help me check the shipping status of my order", classify as "Order inquiry". Constraints: Only process order-related inquiries. Ignore payment and technical issues. Effect: User input: "When will the book I ordered from your website last week be delivered to my home?" Classification result: "Order inquiry" In this example, providing specific classification examples guides the model to classify the "delivery time query" as an "Order inquiry" intent, while the constraints limit the classification scope to exclude other irrelevant questions.
  output	The variable that stores the node's output. Downstream nodes can use this variable to access the result.

  Note

  Running this node consumes tokens. The number of tokens consumed is displayed at runtime.

Flow output

• Definition

  The flow output node outputs specified content at any point in a workflow. This differs from the final output, which is returned only after the workflow completes.

  The flow output node is useful in the following scenarios:

  • Split large outputs: If a workflow generates a large amount of output, you can insert a flow output node to split the content into two parts. One part is output by the flow output node and the other by the end node.

  • Retrieve intermediate information: Insert this node at key points in a workflow to output information such as variable values or process status. This helps you track progress and troubleshoot issues.

  • Provide feedback during long operations: To avoid leaving users without feedback, you can insert a flow output node to display temporary messages. This is useful during time-consuming operations like calling external APIs, performing complex calculations, or processing data in batches. By providing status updates such as "Loading data, please wait..." or "Your request is being processed. Please do not close this window.", you let users know the system is working, not frozen or unresponsive.

• Parameter configuration

  Parameter	Description
  Output content	Enter fixed content or type / to reference variables from preceding nodes or session variables.
  Streaming output	When enabled, content from a large model is streamed to the conversation token by token. When disabled, the entire response is generated first and then output at once.

Variable processing

• Definition

  Used to transform and process text content, such as by extracting specific content or converting formats. This node also supports template mode.

• Parameter configuration

  Parameter	Description
  Output mode	Text output: Converts the input to text. In the editor, you can either enter the content directly or insert variables from upstream nodes or session variables. JSON output: Formats the input variables as a JSON object. Group aggregation: Determines which values are returned from a group according to a specified strategy. You can return either the first or the last non-empty value from each group.
  Output variable	Specifies the variable to store the node's output for use by subsequent nodes.
  Return result	This feature is being deprecated. Applicable only to API calls, this parameter determines whether the node's output is returned. See application call.

Parameter extraction

• Definition

  Extracts structured parameters from text using a model.

• Parameter configuration

  Parameter	Description
  Input	The text to extract parameters from.
  Model selection	Specifies the model used to extract parameters. Choose the one that best suits your business needs.
  Parameters to extract	The model extracts parameters from the input based on the name, type, and description you provide.
  Prompt	Additional instructions to guide the model during parameter extraction.
  Memory	Memory provides conversational context, helping the model remember previous interactions in multi-turn scenarios. Cache in this node: Uses the output of this node as context. The model only remembers context from this node. Memory turns: The number of conversation turns to remember. One turn consists of one input and its corresponding output. Custom cache: Uses a specified context variable to provide context. Context variable: Select the source of the context.
  Output	Contains the parameters extracted by the model. Subsequent nodes can reference this output as a variable.

Agent group

• Definition

  An agent group contains multiple sub-agents. Based on task requirements, the decision model within the group automatically plans the execution flow, schedules sub-agents, and coordinates them to complete the task. Use this node if you need to complete a large project but are unsure how to design the specific workflow. By breaking down a complex task into sub-tasks that different agents process in parallel, you can improve task execution speed.

• Parameter configuration

  Parameter	Description
  Input	Specifies the content that the node will process. You can reference variables from a previous node.
  Model selection	Select a decision model.
  Group name	Enter a custom name for the agent group.
  Agents	Specifies the sub-agents within the agent group. Only published agent applications can be added. After adding a sub-agent, click Configure to describe its function. This description helps the decision model determine which sub-agent to invoke for the current task.
  Output variable	Specifies the variable for storing the output of this node. This variable can be referenced by subsequent nodes.

Create agent

• Definition

  Creates a new agent exclusively for the orchestration canvas.

• Parameter configuration

  Parameter	Description
  Input	The content to be processed by this node. You can reference variables from preceding nodes.
  Agent name	A custom name for the agent.
  Model selection	The large model used by the agent.
  Prompt	Use natural language to define the agent's role and tasks.
  Knowledge base	Select a knowledge base for the agent.
  Plugin	Enables the agent to call official or custom plugins to extend its capabilities.
  Output variable	The variable that stores the node's output for use in subsequent nodes.

Multimodal generation

• Definition

  The multimodal generation node uses Alibaba Cloud multimodal models to generate image, video, or audio content based on the configured prompts and parameters. Use this node for content creation, marketing asset production, short video generation, and voice-over synthesis.

• Model selection

  Model selection

  Select a multimodal generation model from the drop-down list. Models are categorized by function:

  Model type	Supported models	Features
  Image generation	Qwen-Image	Excels at rendering complex Chinese and English text.
  	Wan 2.6-image-generation	Generates and edits images from text instructions, supporting both image editing and mixed text-and-image modes.
  	Qwen-Image-Plus	Excels at rendering complex Chinese and English text.
  	Z-Image-Turbo	A lightweight text-to-image model that quickly generates high-quality images. It supports bilingual rendering (Chinese and English), complex semantic understanding, and multiple styles and themes.
  	Wan 2.2-text-to-image-Plus	The Pro edition of Wan 2.2, with comprehensive improvements in creativity, stability, and photorealism.
  	Wan 2.2-text-to-image-Flash	The Flash edition of Wan 2.2, with comprehensive improvements in creativity, stability, and photorealism.
  Video generation	Wan 2.6-text-to-video	Wan 2.6. Features multi-shot storytelling capabilities and supports both automatic voice-overs and custom audio file inputs.
  	Wan 2.6-image-to-video	Wan 2.6. Features multi-shot storytelling capabilities and supports both automatic voice-overs and custom audio file inputs.
  	Wan 2.5-text-to-video-Preview	Wan 2.5 preview. Supports automatic voice-overs and custom audio file inputs.
  	Wan 2.5-image-to-video-Preview	Wan 2.5 preview. Supports automatic voice-overs and custom audio file inputs.
  	Wan 2.2-text-to-video-Plus	The Pro edition of Wan 2.2. Offers more accurate instruction understanding, stable and smooth motion generation, and richer details in the output.
  	Wan 2.2-image-to-video-Plus	The Pro edition of Wan 2.2, offering more accurate instruction understanding, controllable camera movement, and consistent visuals. It also features comprehensive improvements in stability, success rate, and content richness.
  	Wan 2.2-image-to-video-Flash	The Flash edition of Wan 2.2, delivering ultra-fast generation, more accurate instruction understanding and camera control, consistent visuals, and comprehensive improvements in stability and success rate.
  Audio generation	Qwen3-TTS-Flash	Supports mixed-language text input and streaming audio output.

  The list of available models is subject to change. Refer to the UI for the most current options. For model invocation pricing, see Model Invocation Billing.

• Configuration parameters vary by model. The panel shows only the settings relevant to your selection.

  Image generation

  After you select an image generation model, configure the following parameters. The available parameters vary by model; refer to the UI for the specific options.

  Parameter	Required	Description
  Positive prompt	Yes	Describes the content, style, composition, and other elements of the target image. You can enter text directly or type / to insert an output variable from an upstream node.
  Negative prompt	No	Describes content that you do not want in the image, such as blurriness or watermarks. You can enter text directly or type / to insert an output variable from an upstream node.
  Size/Resolution	Yes	The dimensions of the image.
  prompt_extend	No	If enabled, the system uses a large model to intelligently rewrite the input prompt. This feature applies only to the positive prompt. It significantly improves results for shorter prompts but adds 3-4 seconds of latency.
  Add Watermark	No	Controls whether to add an Alibaba Cloud watermark to the generated image. This feature is disabled by default.
  Reference image	No	Upload a reference image to guide the style or content of the generated image. You can pass a File-type variable from the start node or a public URL.
  Number of Images	No	The number of images to generate in a single request. The default value is 1.
  Intelligent prompt rewriting	No	If enabled, a large model intelligently rewrites the input prompt. This feature applies only to the positive prompt. It significantly improves results for shorter prompts but increases the image generation time.
  enable_interleave	No	Disabled (default): Image editing mode. Edits, performs style transfer, or generates consistent subjects using 1 to 4 input images. Enabled: Mixed text-and-image output mode. Generates a single content block containing both text and images, based on an image or plain text.
  Random seed	No	Controls randomness in the generated output (default: 1234). Using the same seed value and prompt produces similar images.
  Intelligent Thinking	No	If enabled, the large model performs reasoning and prompt rewriting. This can improve the generation quality but also increases the generation time.

  Video generation

  After you select a video generation model, configure the following parameters. The available parameters vary by model; refer to the UI for the specific options.

  Parameter	Required	Description
  Positive prompt	Yes	Describes the scene, actions, style, and other elements of the target video. You can enter text directly or type / to insert an output variable from an upstream node.
  Negative prompt	No	Describes content that you do not want in the video, such as blurriness or watermarks. You can enter text directly or type / to insert an output variable from an upstream node.
  Resolution	Yes	The video resolution. The available options vary by model.
  Video Duration	Yes	The duration of the generated video. The available options vary by model.
  Random seed	Yes	Controls randomness in the generated output. Using the same seed value and prompt produces similar videos.
  Intelligent Expansion	No	If enabled, the system uses a large model to intelligently rewrite the input prompt to improve the video generation quality. This feature applies only to the positive prompt.
  Smart Multi-shot	No	If enabled, the model generates a multi-shot video.
  Generate Audio	No	If enabled, you can add a voice-over by providing a public audio file URL.
  Reference image	Yes (image-to-video models only)	Specifies a reference image to serve as the video's first frame. The model generates the video based on this image. You can pass a File-type variable from the start node or a public URL.

  Audio generation

  Parameter	Required	Description
  Text for Synthesis	Yes	The text content to convert to speech. You can enter text directly or type / to insert an output variable from an upstream node.
  Language Type	No	The language for the synthesized speech (default: Chinese). Supported languages: Chinese, English, German, Italian, Portuguese, Spanish, Japanese, Korean, French, and Russian.
  Voice	No	The voice for the synthesized speech. For a list of supported voices, see Real-time Speech Synthesis - Qwen.

• Node output

  After the multimodal generation node runs, it outputs the following variables:

  Parameter	Type	Description	Example
  output	Object	The complete output object, containing the task status, execution time, and results.	-
  output.task_status	String	The task status, such as "SUCCEEDED" or "FAILED".	SUCCEEDED
  output.submit_time	String	The task submission time, in YYYY-MM-DD HH:mm:ss.SSS format.	2026-01-22 10:54:44.200
  output.end_time	String	The task end time, in YYYY-MM-DD HH:mm:ss.SSS format.	2026-01-22 10:54:51.685
  output.task_id	String	The unique identifier for the task.	e36c2221-b7fd-xxxx
  output.scheduled_time	String	The task scheduled time, in YYYY-MM-DD HH:mm:ss.SSS format.	2026-01-22 10:54:44.251
  output.results	Array<Object>	An array of generation results, containing metadata for the generated assets.	-
  output.results[].orig_prompt	String	The original prompt entered by the user.	a cute cat sitting on a windowsill
  output.results[].actual_prompt	String	The prompt used for generation after model expansion.	a cute white cat with fluffy, soft fur...
  output.results[].url	String	The URL of the generated file.	https://dashscope-result...
  usage	Object	Model usage statistics.	-
  usage.image_count	Number	The number of generated images (image generation mode).	1
  urls	Array<String>	An array of URLs for the generated files. Use these URLs to access or download the output.	["https://dashscope-result..."]

Workflow applications

1. How do I write the results of a workflow run to a database?

   Use a script conversion node to write the output from the previous node to a database.

2. How do I upload files when building a workflow application in Model Studio?

   Add an API node to your workflow application to upload files.

3. How do I upload images?

   Use a VL model and pass the image URL as a parameter.

4. Can I use an asynchronous task API within a workflow application?

   The timeout for a workflow application is 600 seconds. Avoid using an asynchronous task API within a workflow.

5. How can I call the Model Studio workflow API from a frontend application and receive a streaming output?

   Frontend calls are not currently supported.

6. Why can't I import a standalone .yaml file into a Model Studio workflow?

   Model Studio does not support importing standalone .yaml files. You must provide a compressed package that includes an MD5 file. We recommend regenerating the MD5 if you encounter issues.

7. Can variable names in Model Studio workflows be in Chinese?

   No, variable names cannot contain Chinese characters.

8. How is conversation history stored?

   Workflow applications store data for only one month. You must save your own conversation history. The session_id is valid for one hour.

Nodes

1. Why does my intent classification node fail when context is enabled?

   If you enable context for an intent classification node, the variable that you pass to it must be a list.

2. Why does my API node fail when I use streaming output?

   The API node within a workflow does not support streaming output. However, streaming output is supported by the underlying HTTP API.

3. How do I speed up a slow conditional node?

   1. Check your workflow configuration: Ensure each node, especially the conditional node, is configured correctly. Avoid unnecessarily complex calculations or data processing to reduce response time.

   2. Optimize script logic: If the conditional logic involves a custom script, optimize the script by reducing unnecessary loops or redundant data processing to improve performance.

   3. Run batch tests: Measure the average response time of your workflow to identify performance bottlenecks under specific conditions.

4. How do I stream the reasoning process from a large model node?

   You need to add a text conversion node after the large model node, configure the reasoning_content variable, and enable Result Return. The end node must receive the result.

5. How can I customize the output parameters of a large model node?

   1. Use a script node to process the output: Add a script node after the large model node to process its output, convert it to your desired format, or add extra parameters.

   2. Configure a batch node: If you are using a large model node within a batch node, you can select the output of the large model node as the final output in the batch node's configuration. The steps are as follows:

      • Add a large model node to the batch node.

      • In the batch node configuration, select the output of the large model node as the final output resultList.

   See Parameter passing for applications.

6. Why does my API node return no result or fail to pass parameters?

   Verify that your API key and Base URL are correct. Ensure that the input parameters are configured correctly, and adjust the field input types if necessary. Use Model Observation to view model usage details.

7. How do I resolve issues when accessing Excel data from a knowledge base?

   You cannot directly access local files. Use MCP to access local resources. You must manually process the output from a knowledge base node. We recommend adding a large model node to convert the output to a table format before passing it to a script node for further processing.