What the data for this category looks like
Data sources for film theater intelligent due diligence include theater scheduling management systems, national film box office public platforms, film copyright cooperation contracts, theater-side operation reports, advertising placement investment ledgers, and similar sources. Update rhythms vary across sources. Scheduling information is updated weekly. Daily box office data is updated each natural day. Contract documents have no fixed update cycle. Theater operation data is aggregated daily. Documents are divided into two categories: structured reports and unstructured documents. Structured reports include fields such as theater hall number, session number, single-ticket price, number of viewers, and daily revenue, with units: number, session, yuan, person, yuan. Unstructured documents include fields such as cooperating entity name, cooperation period, revenue share tier, and advertising placement type.
What constraints these characteristics impose on deployment and upgrade
The multi-source nature, varied update frequencies, and mixed document structure of film theater due diligence data impose specific constraints on deployment and upgrade workflows. Structured reports require enabling a dedicated table parsing module to prevent loss of field information. Long-text contracts need appropriate chunk length settings to avoid semantic fragmentation. Differences in data update frequencies require configuring differentiated scheduled synchronization tasks to align with box office and scheduling release windows. Deployments must specify a private storage path to avoid data leakage risks for copyright-sensitive film data. Individual film contracts and operation ledgers typically have large file sizes. Administrators must relax upload file size limits to accommodate these files.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_TABLE_ENABLE | Enabled | Film theater data includes structured box office reports and scheduling tables. Table parsing must be enabled to preserve field structure |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Film copyright contracts and theater operation ledgers typically have large individual file sizes, so upload limits must be relaxed |
INCREMENTAL_SYNC_CRON | 0 2 * * *,0 3 * * 0 | Daily box office data is released before 1 AM, and scheduling information is updated before 2 AM on Sunday. Synchronization tasks must run after release windows |
MAX_CHUNK_LENGTH | 1000–1200 characters | Film due diligence reports include long-text contracts and session descriptions. This length preserves semantic integrity while avoiding excessive chunking |
RECALL_TOP_N | Top 8 entries | Film due diligence requires associating multi-dimensional data including scheduling, box office, and contracts. Enough recall entries are needed to cover associated information |
UPLOAD_FILE_STORAGE_PATH | Custom local directory | Film data involves copyright-sensitive information. A private storage path must be specified to avoid public network leakage |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- When deploying ollama locally and integrating it with FastGPT, model connection fails after startup, and the console returns a 503 status code. Cause: The
OLLAMA_API_BASEparameter is not configured correctly, or the model startup port does not match the FastGPT configuration. - After private deployment, users cannot view the storage location after uploading film data files, and a prompt indicates that the data has been uploaded to the public network. Cause: The
UPLOAD_FILE_STORAGE_PATHwas not modified to a private local directory, and the default public storage path was used. - When calling the due diligence report question-and-answer function, the returned result is duplicated twice. Cause: The FastGPT version is V4.8.10-fix2, and the default context recall output switch is not turned off, causing recall results to overlap with AI responses and produce duplicate output.
How to confirm the configuration is complete
- Perform a local file upload test: upload a single film contract file that meets the volume limit, confirm that the upload progress is normal and no errors occur, and verify that the file exists in the specified storage directory.
- After configuring the incremental synchronization task, wait for one synchronization cycle, and check whether data entries for the corresponding time period are added to the vector database, with fields matching the source data structure.
- Initiate a query for a film scheduling table, and confirm that the returned results contain complete structured fields with no truncation or loss.
- Check the console logs, and confirm that the model connection status is normal, with no 503 or timeout errors.
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.