What the data for this category looks like
Film theater investment research data sources include official theater scheduling systems, third-party box office monitoring platforms, official film promotion and distribution press releases, and on-site theater operation logs. Update rhythms follow these rules: box office data updates daily, scheduling plans update 7 to 14 days in advance, public opinion data is captured in real time, and promotion materials are updated according to project milestones. Document structure falls into two categories: structured and unstructured. Structured data includes fields such as theater code, theater hall number, session start time, single-ticket price, and number of attendees. Unstructured data includes long film reviews, promotion plan documents, and theater operation reports, with significant differences in paragraph length. Field units use concrete metrics such as yuan, number of attendees, and sessions.
What constraints do these characteristics impose on model access and configuration
The mixed structured and unstructured nature of film theater investment research data requires model access to support both structured field recognition and unstructured text parsing. Daily updated box office data and real-time public opinion require model call latency to match data update rhythms, preventing data from being excluded from the knowledge base due to timeouts. Scheduling plans updated in advance follow fixed cycles, so incremental sync trigger thresholds must be configured to trigger knowledge base refreshes only after scheduling updates. Structured data with multiple fields requires clear field mapping rules, preventing models from confusing codes, ticket prices, and other fields across different theaters, which would cause deviations in investment research results.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Unstructured documents for film theaters include long film reviews and operation reports. This range covers the complete semantics of most long texts, avoiding truncation of key information |
chunkSize | 800–1200 characters | Single records in structured scheduling tables are short. Splitting unstructured film reviews and reports to this length ensures semantic completeness while reducing redundancy in single-item recalls |
recallTopK | Top 6–8 items | Film investment research needs to cover multi-dimensional data including box office, scheduling, and public opinion. Too many recalls increase model inference load, while too few will miss key information |
similarityThreshold | 0.72–0.78 | The semantics of fields and texts in film data vary widely. This range filters low-relevance public opinion or scheduling data while retaining valid associated information |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing large theater operation reports or promotion plan documents takes a long time. This duration avoids parsing failures |
toolChoice | auto | Investment research scenarios require models to independently select tool calls, such as box office data tools or public opinion analysis tools. Auto mode adapts to multi-tool linkage requirements |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: A large number of unrelated films appear in film name semantic matching results. Cause: The
similarityThresholdparameter is not adjusted to a reasonable range. An overly low threshold causes low-relevance content to be recalled. - Phenomenon: The reranking model returns a
400 Bad Requesterror. Cause: The input text length of the reranking model exceeds platform limits, or the incoming document list contains empty session time fields. - Phenomenon: The model cannot automatically select the box office data tool during tool calls, and only returns general responses. Cause:
toolChoiceis not configured asauto, and tool call scenarios are not clearly marked in the system prompt.
How to confirm the configuration is complete
- Upload a theater operation report and scheduling table, check if the parsed structured fields match the original data, and adjust relevant parameters until they match.
- Initiate an investment research query, observe the number of recalled documents and content relevance, and adjust recall and similarity-related parameters based on query results.
- Trigger a tool call test, confirm that the model can automatically select the corresponding tool and return expected results, and check if tool configurations fit the scenario.
- Wait for the end of a data update cycle, check if the knowledge base automatically incrementally syncs new scheduling or box office data, and confirm that the parsing timeout parameter meets requirements.
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.