What the Data for This Category Looks Like
Data sources for film and theater investment research include official theater scheduling systems, box office settlement systems, National Film Bureau filing and publicity platforms, third-party industry public opinion databases, and professional research reports. Update rhythms vary significantly: Theater scheduling lists and same-day box office data are updated daily, theater operation weekly reports are updated weekly, filing information and crew data are updated around film releases, and public opinion data is updated in real time. Document structures include structured tables (such as single theater daily scheduling, tiered city box office statistics), semi-structured research reports (such as schedule analysis), and unstructured film review content. Core fields include theater ID, session time, film title, box office revenue (unit: yuan), release date, and film genre.
Constraints Imposed on Knowledge Base Retrieval and Recall by These Characteristics
The characteristics of film and theater data impose multiple constraints on the retrieval and recall link. Hybrid vector and keyword retrieval is required for structured scheduling and box office data to avoid missing precise session information when using only vector recall. The retrieval system must support incremental updates for frequently updated scheduling data, to avoid performance loss from full reindexing. Adaptive chunking strategies are needed for highly varied document lengths, which range from tens of characters for single scheduling entries to thousands of characters for schedule research reports. Clear specification of retrieval scopes is required for multi-field structured data, to prevent irrelevant public opinion content from mixing into retrieval results.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
retrieval count | Top 10-15 entries | Film and theater data includes a large number of short scheduling entries and long research reports. Too many retrieved results will cause context overload, while too few will miss critical session information. |
similarity threshold | 0.65-0.75 | It is necessary to distinguish similar film titles and same-schedule screenings. A threshold that is too low will mix in irrelevant content, while a threshold that is too high will miss matching detailed session information. |
chunk length | 800-1200 characters | Adapt to the paragraph length of theater research reports, while avoiding over-splitting short scheduling entries. |
incremental update trigger frequency | Every hour | Match the real-time update rhythm of scheduling and box office data. |
retrieval field specification | Film title, release date, theater ID, box office revenue | Focus on core investment research fields to avoid retrieving irrelevant public opinion content. |
reranked return count | Top 5 entries | Prioritize returning the most matching core scheduling or research report content. |
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. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Retrieval test returns a score exceeding 1000, causing abnormal interface display. The cause is that normalization processing for the similarity threshold is not configured, and original vector similarity scores are used directly instead of normalized values in the 0-1 range.
- The agent editing page becomes unresponsive after loading. The cause is that the number of documents associated with the knowledge base is too large, and pagination loading is not enabled, leading to front-end rendering timeout.
- The knowledge base ID cannot be correctly passed to the retrieval node in the workflow. The cause is that the global parameter for the knowledge base ID is not bound in workflow variables, or the correct field name is not specified during parameter passing.
How to Confirm the Configuration Is Complete
- Run a knowledge base retrieval test, and verify that the returned result fields include core investment research information such as films, screenings, and box office data.
- Check the score range of retrieval results, and confirm that scores have been normalized to the 0-1 range.
- Simulate the incremental update process, and confirm that newly uploaded scheduling data can be retrieved within the set trigger cycle.
- Test the knowledge base retrieval node in the workflow, and confirm that incoming parameters can correctly match the target knowledge base.
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.