What the Data for This Category Looks Like
Film and theater industry research report data is sourced from public research reports of financial investment institutions, industry consulting firm reports, theater operation backend data, official disclosure information from film studios, and statistical materials from industry associations. Update frequency adjusts based on new film release cycles and quarterly operation nodes, with higher update rates around new film launches. Most documents combine structured tables and paragraphs, including fields such as per-film box office, screening sessions, audience age distribution, and average daily revenue per theater. Units include person-times, ten thousand yuan, screening sessions, and others.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompting
High proportions of structured data and scattered update nodes require precise matching of the time range and specific theater or film studio referenced in inquiries during multi-turn dialogue. Prompts must clearly specify the data time interval and query dimensions. Variations in units for fields such as per-film box office and screening occupancy rate can lead to confusion, so prompts must mandate clear unit matching. Frequent updates require the knowledge base to sync on a regular basis. During multi-turn dialogue, the dialogue system must guide end users to confirm the latest data update node to prevent reliance on outdated information. The table structure of structured data requires the dialogue system to identify row and column associations, so prompts must guide the model to link answers to table fields.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
CHUNK_SIZE | 800–1200 characters | Film research reports contain long paragraphs and table blocks; segment length matches the splitting granularity of structured data |
RECALL_TOP_K | Top 6–10 results | Covers multi-dimensional research report data including per-film box office, screening sessions, and audience demographics to avoid missing key information |
SIMILARITY_THRESHOLD | 0.72–0.85 | Filters research report content from outdated release windows or non-target theaters that are irrelevant to the current query |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Reserves sufficient processing time when parsing large research report files with multiple-page tables |
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports uploading complete film research report documents with attachments and multi-page content |
PROMPT_TEMPLATE | Prioritize recalled data matching user-specified time intervals and theater entities | Adapts to the time and entity constraints of film research reports, clarifies context association rules during multi-turn dialogue |
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 individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- The conversation interface fails to display configured input guidance prompts. The root cause is that the input guidance trigger rules are not bound to the classification tags of the film research report knowledge base, and the global-only configuration does not adapt to the query requirements of this scenario.
- After uploading an XLSX-format research report, the model cannot restate the file content. The root cause is that the table parsing function is not enabled, or table data fails to be written to the vector database due to a parsing timeout.
- The model does not prioritize reference to the specified research report file during multi-turn dialogue. The root cause is that the number of recalled entries is set too low, failing to cover key data in the specified file, or the prompt does not clearly limit the content scope of the current knowledge base.
How to Confirm Proper Configuration
- Upload an XLSX file of a film and theater industry research report, enable table parsing, and check the vector database preview to confirm that table fields and content have been correctly split and stored.
- After configuring input guidance, access the conversation interface to test triggering, and confirm that preset guidance prompts display normally.
- Initiate a query containing a specific theater name and time range, and check whether the recalled results include relevant data from the corresponding research report.
- Test multi-turn dialogue by consecutively asking about box office data for different release windows, and confirm that the model can adjust answer dimensions by associating context.
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.