What the data for this category looks like
Due diligence data for film theater operations comes from theater management systems, cinema POS terminals, the National Film Bureau filing and public announcement platform, and copyright holder authorization documents. Structured data includes show schedules, daily box office reports, and cinema operation reports. Unstructured data includes paper and electronic authorization agreements, and film filing and public announcement documents. Show schedule data is entered 7 to 30 days in advance, and daily session information is refreshed each day. Box office data updates the previous day’s settlement figures after 24:00 daily. Copyright documents are added when new films are approved, contracts are renewed, or authorization changes occur, with no fixed update cycle. Structured document fields include unique cinema identifiers, film names, session times, daily box office amounts, and number of patrons. Time uses ISO 8601 format, and amounts are measured in Chinese yuan.
What constraints these characteristics impose on multiturn conversations and prompting
Configuration for multiturn conversations and prompting must adapt to constraints imposed by the data characteristics of film theater operations. First, data updates frequently and mixes structured and unstructured formats. Multiturn conversations must support dynamic retrieval of the latest show schedule and box office data. Prompts must explicitly specify the time range for data to avoid returning outdated information. Second, structured and unstructured data coexist. Conversation flows must separate logic for field extraction and clause interpretation. Prompts must separately define matching rules for structured data and key element extraction logic for unstructured authorization documents. Additionally, due diligence reports require association across multiple data sources. Multiturn conversations must support gradual association of show schedule, box office, and copyright information. Prompts must specify information association steps for each conversation turn to avoid disconnected information. Finally, data volumes are large. The length of document fragments returned per conversation turn must be limited to prevent context overflow.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Adapts to the conversation context length required for associating multiple data sources in film theater operations, preventing lost associated information due to context overflow |
ragTopK | Top 8–12 results | Film theater data includes multi-dimensional content such as show schedules, box office figures, and copyright information. Too many recalled results increase context pressure, while too few fail to cover associated information |
rerankTopN | Top 3–5 results | Prioritizes results most relevant to due diligence questions, including show schedule, box office, or copyright data, for relevance ranking across multi-dimensional data |
dataSourceRefreshInterval | 3600 seconds | Aligns with the daily update rhythm of box office and show schedule data, ensuring the data called during conversations uses the latest settlement or scheduling information |
fileParseChunkSize | 1000–1500 characters | Adapts to the paragraph length of film authorization agreements and filing documents, avoiding damage to the complete semantics of clauses after splitting |
conversationHistoryMaxLength | 10 conversation turns | Limits the number of historical turns in due diligence conversations to prevent model inference delays or information confusion caused by overly long context |
The parameter values provided on this page are common recommendations that serve as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Issue: Garbled text is returned when parsing film copyright authorization PDF files. Cause: Correct splitting parameters for Chinese PDF text extraction were not configured, leading to damaged semantic block integrity during file parsing.
- Issue: Built-in like interaction buttons do not display in the conversation interface for version v4.8.10. Cause: FastGPT’s conversation like function requires implementation via a custom front-end component. The feature will not display by default if the corresponding plugin is not loaded.
- Issue: Conversation logs are not automatically saved for local deployment versions. Cause: The
conversationLogAutoSaveconfiguration item was not enabled, or the permissions for the local storage directory were configured incorrectly.
How to Verify Proper Configuration
- Initiate a query for that day’s theater show schedules. Verify that the time range of the returned data matches the current natural day.
- Upload a film copyright authorization document, initiate a clause extraction request. Verify that the returned results include exclusive fields such as authorizing party and validity period.
- Start a local deployment instance, initiate consecutive multi-turn associated queries. Verify that conversation history is correctly saved to the specified storage path.
- Call the conversation interface for version v4.8.10. Verify that the response content includes the output format specified by the configured prompt.
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.