What the data for this category looks like
Film and theater industry investment research data comes from multiple sources. These include theater scheduling systems, box office statistics platforms, industry public opinion monitoring tools, copyright cooperation contracts, and theater operation daily reports. Scheduling information updates daily. Box office data updates per screening session or daily. Public opinion information is pushed in real time. Contract documents are static, but updated when cooperation terms change. Document structures include three main types. Structured tables such as scheduling sheets and financial reports. Semi-structured long text such as industry analysis reports and public opinion press releases. PDF-format compliance documents. Available fields include single theater daily box office, revenue share ratio, theater auditorium capacity, copyright validity period, and more. Units include ten thousand yuan, person-times, hours, and others.
What constraints these characteristics impose on deployment and upgrade
The multi-source access, high-frequency updates, and mixed document structure of film and theater industry data create clear constraints for deployment and upgrade workflows. High-frequency updated scheduling and box office data require incremental sync task configuration. This avoids full syncs consuming excessive system resources. Mixed documents of structured tables and long text need adapted multi-format parsing and segmentation strategies. This prevents key data from being truncated or parsing failures. Parsing of compliance documents needs additional permission check configuration. This ensures sensitive data is not leaked. Upgrades must support new data source formats, such as return fields from new theater APIs. This prevents sync task interruptions.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Film and theater industry long documents such as copyright contracts and annual box office reports require extended parsing timeout to avoid task interruptions |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Supports uploading large-volume theater box office database backups and long-term public opinion archive files |
Segment Length | 800–1200 characters | Adapts to semantic splitting requirements for film industry public opinion long text, preventing truncation of key plot or box office analysis content |
Recall Count | Calibrated via actual testing for investment research scenarios | Covers retrieval needs for multi-source investment research data, avoiding missing key information due to insufficient recall count |
Similarity Threshold | Calibrated via actual testing for investment research scenarios | Balances filtering of semantically similar content and recall of valid data, adapting to differentiated retrieval for scheduling and public opinion data |
maxContext | 16000 characters | Integrates context information from multiple long documents, supporting comprehensive investment research analysis across scheduling, box office, and public opinion data |
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. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: After deploying version 4.9.0, the Chat model functions normally, but the Embedding model cannot connect to OneAPI. A
500 Internal Server Erroris returned. OneAPI backend logs show that the model route does not exist. Cause: The FastGPT Embedding request address was not pointed to the corresponding model node in OneAPI, or the access key for the Embedding model was not added in OneAPI. - Symptom: After locally deploying version 4.8.22, uploading a theater revenue share PDF or scheduling Excel results in a parsing failure status. Backend logs show
parse task failed. Cause: The required parsing dependency packages for the corresponding format were not installed, or theUPLOAD_FILE_MAX_SIZEconfiguration value is smaller than the actual size of the uploaded file. - Symptom: After setting the
Similarity Thresholdto an excessively high value, the retrieved investment research results only cover a single data source. Cross-theater scheduling or cross-cycle box office comparison information cannot be obtained. Cause: The threshold setting exceeds scene requirements, filtering out valid data that is semantically similar but has different actual content.
How to Confirm Configurations Are Correct
- Upload a standard theater scheduling Excel document. Check if preset fields such as theater ID, session time, and ticket price are extracted after parsing. Verify field completeness and accuracy.
- Call the Embedding model interface. Pass a long text segment of film industry public opinion content. Check that the returned vector results are not empty, and no error logs appear in the OneAPI backend.
- Initiate an investment research query, such as "This week's national theater scheduling status". Check that the retrieved results include multi-source data and match the expected count.
- After upgrading to a new version, restart the service. Check that all configuration environment variables are loaded successfully, and no startup error logs appear.
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.