What the data for this category looks like
The core data sources for game industry intelligent due diligence include the National Press and Publication Administration’s game license announcement platform, public disclosure documents from game developers, listing information from mainstream app stores, and operational data APIs. Data update rhythms vary with business milestones: license information updates with approval batches, operational data syncs daily, and compliance filing documents update with version iterations. A single due diligence report includes main qualification fields, license number, online filing mark, user scale statistics fields, and revenue share ratio parameters. Most fields are strings, date types, and numerical types with clear units.
What constraints these characteristics impose on workflow orchestration
Scattered data sources and inconsistent update rhythms for game due diligence require workflows to configure multiple data pull nodes, connecting separately to different APIs for license announcements, app stores, developer disclosures, and other sources. Timed trigger rules must also be configured according to each data source’s update cycle. Single reports have multiple field types with clear units, so field format validation logic must be added to data cleaning nodes to filter numerical values without units or with abnormal formats. Game due diligence involves compliance qualification information, so compliance check links must be embedded in the workflow to desensitize sensitive data and prevent non-compliant content from entering the final report.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
multi_ai_node_output_visibility | Configure per node; only retain visibility for core report generation nodes | Game due diligence workflows often include multiple rounds of data pulling and cleaning nodes. Only retaining AI responses from the final output node avoids interference from redundant information |
workflow_user_scope_var | Enable user-level variable binding, associate with the user_id field | Different developer due diligence tasks in the game due diligence scenario require data isolation. Binding variables via user ID enables differentiated values for the same variable name |
rag_retrieve_count | 8 to 12 results | Game due diligence data includes multi-dimensional compliance and operational fields. Too many retrieved results cause context overload, while too few result in missing critical information |
workflow_timeout | 900 seconds | Game due diligence requires pulling data from multiple sources and completing compliance checks. The timeout period must cover the full workflow execution duration |
schedule_trigger_cron | Configure per data type: license data 0 0 2 * * 1, operational data 0 0 * * * | Different game data has varying update cycles. Scheduled tasks must match the update rhythm of corresponding data sources |
field_validation_rule | Configure that numerical fields must include units, and string fields must not exceed 2000 characters | Match the field format requirements of game due diligence data to prevent invalid data from entering the report generation stage |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: All
<ai conversation>node replies appear in chat conversations after workflow execution. Cause: Themulti_ai_node_output_visibilityparameter is not configured, and all AI node output content is retained by default. - Phenomenon: When testing a workflow based on question classification, only the first question triggers knowledge base retrieval, and subsequent questions return no matching results. Cause: Context variables are not reset after the classification node. The context of subsequent questions is not cleared, leading to abnormal knowledge base retrieval logic.
- Phenomenon: When configuring user-level variables, variable values from different users are not isolated. Cause: The
user_idfield is not bound as the variable scope identifier, and variables still use the global scope.
How to Confirm the Configuration Is Correct
- Enter the AI node configuration panel of the workflow, check if the
multi_ai_node_output_visibilityparameter is set per node, and verify that output from non-core nodes is hidden. - Upload test game due diligence data, trigger workflow execution, and check if user-level variables generate differentiated values for different test accounts.
- Trigger the question classification-based test process, submit multiple classified questions in sequence, and confirm that each question triggers knowledge base retrieval.
- View the scheduled task configuration of the workflow, check if the
schedule_trigger_cronexpression matches the update cycle of different data sources.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.