What the data for this category looks like
Intelligent due diligence data for film theater operations originates from theater operation ledgers, daily box office settlement reports, theater equipment parameter sheets, film copyright cooperation contracts, schedule plans, and audience survey questionnaires. Update cadence follows: schedule plans are updated weekly, box office data is settled daily, and copyright contracts are updated only when cooperation changes occur. Document formats include structured tables, long-text contracts, and official PDF reports. Fields cover the number of screening halls, seats per hall, daily box office amount, copyright validity period, revenue sharing ratio, and more. Units include person-times, ten thousand yuan, and hours.
Constraints Imposed on Vector Models and Indexing by These Characteristics
Mixed structured and unstructured document types require differentiated segmentation rules for different formats. High-frequency updated box office and schedule data require incremental indexing to avoid resource consumption from full index reconstruction. Multi-field structured reports need core business fields specified for vector generation, rather than full-document word segmentation. Long-text copyright contracts need adjusted segment lengths to preserve contextual associations of professional terms. Cross-data-source, multi-type data requires indexes to support multi-field combined retrieval.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the semantic integrity of long sentences in film contracts and schedule documents, avoiding splitting of professional terms |
chunk_overlap | 100–150 characters | Preserves contextual associations of professional terms across segments, such as the context of "revenue sharing ratio" across paragraphs |
incremental_index_interval | 1 hour | Matches the update cadence of theater schedule and box office data, balancing index latency and resource consumption |
recall_top_k | 8–12 results | Adapts to the multi-dimensional reference needs of due diligence reports, covering data from box office, schedule, copyright and other sources |
similarity_threshold | 0.72–0.80 | Filters low-relevance general text from the film industry, retaining professionally matched results |
parse_structured_fields | "Daily box office, screening sessions, copyright validity period" | Extracts specified fields from structured reports for vector generation, improving retrieval accuracy |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, 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 Errors
- Phenomenon: The exported knowledge base backup only supports full export, and cannot be split by business categories such as box office or schedule. Cause: No directory-based indexing rules are configured, and all film theater data is grouped into a single knowledge base dimension.
- Phenomenon: Vector retrieval responses time out after deployment, returning status code 504. Cause: No external API is used to call the vector model, and a local vector model is deployed directly on an ARM soft router. The hardware performance is insufficient to support real-time retrieval.
- Phenomenon: Retrieval results contain a large amount of general film information, and do not hit core due diligence data. Cause: No structured fields are specified for vector generation, and full-document indiscriminate word segmentation is used instead. This leads to insufficient retrieval weight for professional fields.
How to Confirm Configurations Are Properly Set
- Upload a single structured box office report, check if the extracted fields after parsing include "Daily box office, screening sessions", to confirm that the
parse_structured_fieldsconfiguration takes effect. - Submit an updated weekly schedule data set, check if the index task completes incremental updates within 1 hour, to confirm that the
incremental_index_intervalconfiguration matches the update cadence. - Enter the query "2024 summer revenue sharing ratio", check if the similarity scores of returned results fall within the preset range, to confirm that the
similarity_thresholdconfiguration is reasonable. - Export the current knowledge base, check if subdirectory backups sorted by business category are generated, to confirm that the directory-based indexing rules take effect.
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.