What the data for this category looks like
Data sources for film and theater chain due diligence include the State Administration of Radio and Television’s film and television filing platform, official theater scheduling APIs, cinema terminal ticketing systems, and copyright holder disclosure documents.
Update frequencies vary significantly. Scheduling information updates 1 to 7 days in advance. Daily box office data updates each day. Copyright filings and contract documents update alongside project progress.
Most documents are structured tables. Fields include theater name, cinema ID, seats per hall, daily box office (unit: ten thousand yuan), scheduled screenings, copyright expiration date, and film filing number. Some scenarios require processing unstructured copyright contracts and scanned cinema qualification documents.
What constraints these characteristics impose on workflow orchestration
Multiple data sources require workflows to integrate different types of pull and parsing nodes. These nodes must adapt to processing logic for API interfaces and unstructured files.
Different update frequencies require layered trigger rules. Scheduled execution nodes are configured for daily and weekly runs separately.
Mixed structured and unstructured data requires initial format conversion and field validation. This ensures units and field formats meet due diligence requirements.
Hierarchical relationships between cinemas and theater chains require matching validation added to the workflow. This prevents cross-project data confusion. Sufficient execution duration must also be reserved to cover the full process of multi-source data pulling and report generation.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
dynamic_kb_bind_var | Bind the film_code or cinema_id variable | Film and theater chain due diligence requires associating exclusive knowledge bases for specific films or theater chains, to avoid cross-project data confusion |
maxContext | 7200 | Due diligence processes involve multiple rounds of data pulling and field validation, requiring retained session context to pass core business parameters |
knowledge_base_recall_limit | Top 6 entries | Film due diligence data is mostly structured entries; a small number of highly relevant entries can support analysis, avoiding interference from redundant information |
similarity_threshold | 0.72–0.78 | Distinguish association matching accuracy between film filing information and theater scheduling data, to avoid incorrect association of information from different projects |
workflow_timeout | 1200 seconds | Film due diligence requires pulling multi-source public data and generating structured reports, reserving sufficient time to complete the full workflow execution |
ai_node_output_mode | Return structured results only | Avoid unnecessary conversational output interfering with final report generation, focusing on obtaining standardized results for task execution |
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 targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Issue: No optional options appear in the reference variable dropdown when configuring
dynamic_kb_bind_var. Cause: Business parameters such asfilm_codeorcinema_idwere not defined in the global variable module in advance, so nodes cannot recognize bindable variables. - Issue: After the AI conversation node runs, results are automatically written to the global conversation return stream, and cannot be output separately as report content. Cause:
ai_node_output_modewas not set to return structured results only; the node mounts output to the session conversation chain by default. - Issue: After multi-round data pulling is complete, subsequent validation nodes cannot obtain cinema code parameters from previous steps. Cause: The
maxContextparameter was not configured to retain session context, or the parameter value was set too small, causing parameter transfer failure between nodes in the workflow.
How to confirm successful configuration
- Trigger a test workflow, check the call logs of the dynamic knowledge base node, and confirm that the bound variable value matches the exclusive identifier of the test film.
- Run the AI conversation node, check that the output panel only returns structured task results, with no additional conversational reply content.
- View the global variable configuration page, confirm that business parameters such as
film_codeandcinema_idhave been defined and assigned values. - Simulate full workflow execution, verify that the workflow completes the operation of all nodes within the preset duration, with no abnormal interruption prompts.
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.