What the data for this category looks like
Primary sources of film theater revenue rate data include national theater scheduling management systems, theater box office settlement systems, and third-party film data aggregation platforms. Daily box office and revenue data is updated at midnight each day. Next-day scheduling data is archived and released three working days in advance. Monthly theater revenue data is aggregated by natural month. The basic data unit is single film - single theater - single day. Included fields are: theater code, film name, session number, show start time, standard per-seat ticket price, daily ticket sales volume, total daily revenue, average per-session audience count. Session number is a pure numeric string. Show start time uses ISO format timestamps. Standard per-seat ticket price and total daily revenue are measured in RMB yuan. Daily ticket sales volume and average per-session audience count are integers.
What constraints these characteristics impose on multi-turn dialogues and prompt engineering
Dispersed data sources and differing update rhythms require multi-turn dialogues to clearly distinguish call logic between real-time daily revenue and future scheduling forecast data. Prompts must specify time range validation rules for data. The field structure uses a multi-dimensional associated format. Multi-turn dialogues must support filtering layer by layer by film, theater, and date. Prompts must define valid formats and mapping relationships for filter parameters. No percentage-type fields exist in the data. Prompts must clarify that revenue rate calculations only use the absolute difference between revenue and costs, to avoid non-compliant format outputs. When querying across time ranges, user-specified filter conditions must be retained between sessions. Otherwise, data matching deviations will occur.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | First 15-20 turns of conversation context | Multi-turn dialogues for film theater data need to retain filter conditions; excessive context will interfere with field mapping and logical judgment |
systemPromptTemplate | Template that explicitly specifies data source fields, time range validation, and prohibition of percentage output | Matches the field structure and update rules of film theater data, avoids returning non-compliant formatted content |
dataSourceRefreshInterval | 86400 seconds (1 day) | Matches the daily update rhythm of daily box office data, ensures the latest revenue data is called |
filterMaxDepth | 3-layer filtering (film - theater - date) | Matches the multi-dimensional associated structure of film theater data, avoids excessive filtering leading to no valid results |
AI_NODE_OUTPUT_MODE | Return structured results only | Avoids redundant dialogue content during debugging, meets the requirement of only obtaining task results |
globalVariableEnable | Enabled | Retains filter parameters and data source identifiers across multi-turn sessions, enabling session memory functionality |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: After cross-session multi-turn dialogues, user-specified filter conditions such as theater and film are empty. Cause: The
globalVariableEnableconfiguration is not enabled, and filter parameters are not stored in global variables, resulting in lost state between sessions. - Phenomenon: After uploading a film theater revenue data file via the API, the parsed fields do not match expectations. Cause: No field mapping rules are specified in the prompt, or the
PARSE_FILE_FIELD_MAPPINGconfiguration does not match the field format of theater data. - Phenomenon: When the AI dialogue node executes a task, debug logs and business results are mixed in the output. Cause: The
AI_NODE_OUTPUT_MODEconfiguration is not set to return structured results only, and the default dialogue output mode is retained.
How to Confirm Configuration is Correct
- Initiate cross-date multi-turn queries, verify that filter conditions remain consistent across sessions, and confirm that the global variable configuration is effective.
- Upload a film theater revenue data file, check that the parsed fields match the preset theater, film, and revenue fields, and confirm that the file parsing configuration is correct.
- Call the AI dialogue node to execute a revenue rate calculation task, check that the returned results only contain business data with no redundant dialogue content, and confirm that the output mode configuration is correct.
- Initiate queries across different time ranges, verify that the update time of the returned data matches the data source refresh rhythm, and confirm that the data source refresh interval configuration is correct.
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.