Context and Token for Film Theater Investment Research

What the data for this category looks like

Film theater investment research data primarily comes from public theater operation reports, third-party box office monitoring platforms, theater scheduling systems, and film project filing and public announcement documents. Update cadences cover daily (box office, occupancy rate), weekly (schedule adjustments), and irregular (industry policies, new film release dates) updates. Each single document includes fields such as theater code, film name, release period, average per-venue attendance, daily box office, public opinion mention volume, and more. Units include person-times, ten thousand yuan, screenings, and keyword frequency.

What constraints do these characteristics impose on the context and token workflow

Film theater investment research data has many fields with high correlation. The token consumption of a single document is higher than that of general industry data. Daily updated high-frequency box office and occupancy rate data requires frequent recall. Insufficient context window size will lead to loss of key information about recent industry fluctuations. Document formats vary significantly across different data sources, including structured reports and unstructured public opinion text. Chunking rules must adapt to the token proportion of different fields. Dense keywords in public opinion data easily trigger single-segment token limit violations, which impacts recall completeness.

How to set the configurations

Configuration Item	Recommended Value	Rationale
`chunkSize`	`800–1200 characters`	Film theater documents include structured fields and dense keywords. This range balances chunking completeness and token utilization
`recallCount`	`top 10–15 entries`	Film industry data has many frequently associated fields. Too many recalls will exceed token limits, while too few will lose key linked information
`similarityThreshold`	`0.75–0.85`	Film industry data has high keyword overlap. This threshold filters low-value duplicate recall content
`maxContextTokens`	`12000–15000`	Adapts to context window reserved space for mainstream large models, balancing token consumption from multi-field associations
`contextOverflowStrategy`	`truncate earliest historical context`	Recent box office and schedule data for film theaters has stronger timeliness, so prioritize retaining the latest information
`chunkOverlap`	`100–150 characters`	Structured report fields are closely linked. Overlapping chunking avoids key information being split apart

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.

Three common configuration mistakes

Phenomenon: The maxContext setting in the workflow is 0, but chat history is still passed to the API during conversations. Cause: The "keep conversation history" switch for the workflow was not turned off, and the parameter settings do not cover the global context configuration.
Phenomenon: Knowledge base chunks are displayed in Markdown format, but the context reference area cannot render the content. Cause: The original document's Markdown syntax was retained during chunking, and the large model's context parsing did not enable the format rendering switch, or the document format was not standardized.
Phenomenon: The displayed context recall count is 30, but the actual count sent to the API is 310. Cause: The recallCount parameter in the recall phase and the front-end display context count configuration are not synchronized, or the rerankTopN parameter in the reranking phase does not limit the display count.

How to confirm the configuration is successful

View the knowledge base chunking preview to confirm that the length of each segment matches the configured chunkSize range, with no obvious key information breaks.
Initiate an investment research query, and verify that the context token count in the API call log matches the configured maxContextTokens value.
Check the content format in the context reference area to confirm that structured report fields and values are displayed normally.
Adjust the recallCount parameter, compare the recall count changes between the two queries before and after, and confirm that the configuration takes effect.

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.