What the Data for This Category Looks Like
Cultural and entertainment products investment research data comes from multiple sources: China Toy and Infant Products Association, publicly released prospectuses and annual reports of domestic cultural and entertainment product brands, sales backend data from mainstream e-commerce platforms, the National Intellectual Property Administration’s industrial design patent database, and contract fulfillment records from industry supply chains. Update frequencies cover multiple dimensions: Industry association reports are updated quarterly, e-commerce sales data is updated daily, patent data is synchronized in real time, and supply chain order data is updated weekly. Document structures include four categories: brand basic profiles, SKU sales ledgers, supply chain cost details, and public opinion monitoring records. Fields include SKU code, unit selling price, weekly sales volume, patent application number, factory qualification level, and more. Some documents include product description text in multiple languages.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
The multi-source, heterogeneous data characteristics of the cultural and entertainment products category create clear constraints for multi-turn dialogue and prompt configuration. Multi-dimensional update frequencies require dialogue contexts to distinguish real-time sales data from historical industry reports, to avoid using outdated information. Varied units and coding rules for multiple fields require prompts to clearly define standard field expressions. For example, use "yuan per unit" consistently for unit selling price to avoid vague references. Multilingual documents require multi-turn dialogue to maintain consistent language across contexts, to avoid parameter confusion caused by language switching between conversation turns. Cross-document linked SKU and sales data require dialogue contexts to retain the binding relationship between SKU codes and corresponding sales metrics, to ensure subsequent questions can accurately associate with the correct data.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 12000–15000 characters | Covers SKU association information for 3-4 turns of multi-turn dialogue and core content from multiple data sources, prevents context truncation that causes lost associations |
recallTopK | Top 8–10 entries | Matches representative entries from the four core data types: brand profiles, sales ledgers, supply chain details, and public opinion records, covers all dimensions of investment research needs |
similarityThreshold | 0.85–0.90 | Accurately matches high-identification fields such as SKU codes and patent numbers, filters low-match irrelevant cultural and entertainment products data |
promptTemplate | Strictly follow the format: first list the corresponding SKU code, then note the unit selling price (yuan per unit) and weekly sales volume, finally add associated supply chain or public opinion information | Clarifies standard expressions and output formats for fields, avoids unit confusion or missing information during multi-turn dialogue |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to parsing durations for long-cycle supply chain ledgers and multi-SKU sales documents, prevents knowledge base loading failures caused by parsing timeouts |
enableContextLock | Enabled | Meets the requirement to fix the process after dialogue starts, avoids investment research logic confusion caused by process switching during multi-turn dialogue |
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. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- The first system response delay exceeds 3 seconds when initiating a conversation for the first time, and subsequent dialogue speeds return to normal. The cause is failure to configure
similarityThresholdto filter low-match data, which retrieves excessive redundant cultural and entertainment products documents and increases initial loading time. - After configuring automatic questioning in the workflow, the dialog box does not automatically trigger preset questions, or fails to associate with corresponding knowledge base content after triggering. The cause is failure to bind the
autoSendInitQueryconfiguration item to the workflow's start node, and failure to specify automatic questioning trigger conditions in the prompt. - When uploading a single long supply chain document, a
PARSE_FILE_TIMEOUTerror code is returned. The cause is failure to setPARSE_FILE_TIMEOUT_SECONDSto a value adapted to long document parsing, which causes parsing to time out and interrupt.
How to Verify Correct Configuration
- Initiate a first conversation, observe the first response time, adjust the values of
maxContextandrecallTopKto ensure the time meets business expectations. - Initiate two or more turns of multi-turn questioning, input questions related to different SKUs in sequence, check whether the output retains the binding relationship between SKUs and corresponding fields.
- Trigger the workflow start node, confirm whether the dialog box automatically sends preset questions, verify the binding status of
autoSendInitQueryand workflow nodes. - Upload a single cultural and entertainment products supply chain document exceeding 5000 characters, check whether parsing completes, verify the effectiveness of the
PARSE_FILE_TIMEOUT_SECONDSconfiguration.
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.