What this industry’s data looks like
Film and theater industry research data has two core source types. First, structured reports from theater operations, including fields such as daily screening schedules, single-day box office, per-screen attendance, and regional distribution of theaters. Second, unstructured content, including film project filing and public notice documents, long professional film reviews, and industry public opinion briefings.
Data update cadences fall into three categories: Screening and box office data updates daily, public opinion data updates weekly, and film project filings, copyright announcements and similar materials are updated irregularly.
Documents primarily take the form of structured tables, PDF reports, and long plain text articles. Fields include film ID, release date, single-screen box office, screening share and other metrics, with multi-dimensional statistical units attached.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering?
The mixed structured and unstructured nature of film and theater data requires clear distinction between processing logic for structured data queries and unstructured public opinion analysis in multi-turn dialogue, to avoid context confusion.
Frequently updated box office and screening data requires explicit specification of data time ranges in prompt engineering, to prevent models from using outdated information.
The presence of long film reviews and large-volume reports requires adaptation of multi-turn dialogue context windows for long-content retrieval, to avoid loss of critical information due to context truncation.
Structured data with multiple fields requires explicit definition of field mapping rules in prompt engineering, to prevent models from confusing statistical data for similar films.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Film and theater data includes long film reviews and multi-field reports, requiring sufficient context to support cross-field associated queries |
Recall count | Top 6–8 entries | Structured screening and box office data has many entries, requiring sufficient retrieved entries to match multi-dimensional query needs for user research |
Similarity threshold | 0.72–0.80 | Differentiate similar titles of film series, to avoid retrieving historical data for unrelated films |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Parsing filing public notice PDFs and long film review documents takes significant time, requiring extended timeout periods |
UPLOAD_FILE_MAX_SIZE | 500 MB | Support uploading large-volume Excel files for annual theater box office summaries |
Rerank result count | Top 3–4 entries | Prioritize returning core data most relevant to research queries, to avoid redundant information interfering with multi-turn dialogue logic |
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 targeted analysis. It is recommended to conduct testing with local test datasets before finalizing configuration.
Three Common Implementation Errors
- Dialogue results fail to link to knowledge base documents. Phenomenon: After initiating a dialogue via API, generated replies do not reference film data within the knowledge base. Cause: Failing to explicitly require priority use of retrieved knowledge base content in the
prompt_template, or setting the retrieval threshold too high, resulting in valid data not being retrieved. - Workflow AI dialogue component errors. Phenomenon: An "uncaught exception" prompt appears on the interface. Cause: In a private deployment environment, the storage path in the configuration file does not correctly point to the minio object storage, or MongoDB connection parameters are configured incorrectly.
- Dialogue page or knowledge base page crashes. Phenomenon: When using version 4.8.20 or other specific versions, pages become unresponsive after loading or crash directly. Cause: The built-in context caching logic of the version does not match the context length after parsing long film and theater documents, resulting in memory overflow.
How to Verify Correct Configuration
- Initiate a test dialogue with a structured data query, verify that returned results include daily box office data for the specified theater and film, and that sources are marked as corresponding to knowledge base documents.
- Upload a single film filing PDF document under 500 MB, verify that parsing progress completes within 120 seconds, with no parsing failure prompts.
- Launch 3 consecutive progressive queries, such as first querying the box office of a specific film, then associating contemporaneous film reviews, and finally counting regional proportions, verify that dialogue context is correctly retained, with no context loss or confusion.
- Check system logs, confirm that the number of retrieved knowledge base documents falls within the range configured for
Recall count, with no abnormal 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.