What data for this category looks like
Game industry research report data mainly comes from public reports released by game industry associations, quarterly financial reports of game manufacturers, data from third-party game research institutions, public version number information from the National Press and Publication Administration, and user behavior data from game communities. There are two update cycle types: macro industry data is updated quarterly, manufacturer operation data is synchronized with financial report cycles, and real-time community data is refreshed daily. The document structure includes fields such as version number qualifications, core gameplay descriptions, user retention rate, revenue scale, and competitive benchmarking analysis. The core quantitative fields are user retention rate, DAU, and ARPU, with units of percentage, ten thousand, and yuan respectively.
What constraints do these characteristics impose on workflow orchestration
The multi-source and scattered nature of game research report data requires configuring multiple parallel nodes in the workflow to pull data from different sources, while adding format conversion nodes to unify field structures. Data sources with different update cycles need to be configured with scheduled trigger rules to distinguish full updates and incremental pulls. Macro industry data is synchronized quarterly, and real-time community data is refreshed daily. Long documents with multiple chapters require split processing by chapter to avoid context overflow. Enumeration fields such as version number and launch status need to add verification nodes to filter invalid data. Quantitative field unit conversion nodes need to adapt to industry-specific units such as ten thousand and yuan.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000-12000 characters | Single game research report usually contains multiple chapters, this value adapts to the context length after segmented recall and avoids exceeding model limits |
recall_top_k | 10-15 entries | Core information of game research reports is scattered across different document paragraphs, sufficient recall volume can cover key content such as version numbers and user data |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Game research reports contain a large number of tables and charts for parsing, which takes longer than ordinary text documents, this duration ensures complete parsing |
UPLOAD_FILE_MAX_SIZE | 200 MB | Game research reports often come with high-definition financial report screenshots and complete industry report attachments, this size adapts to common file sizes |
max_web_search_concurrent | 6 concurrent requests | Data sources for the game industry are scattered, limiting the number of concurrent pulls from multiple sources avoids system overload |
custom_system_prompt | Set to "Please accurately answer relevant questions based on the provided game research report content, prioritize citing quantitative fields such as version numbers, DAU, and ARPU" | The core value of game research reports lies in industry and operation quantitative data, which requires clearly guiding the model to focus on key information |
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 case-by-case analysis, and it is recommended to conduct tests using local samples before finalizing the configuration.
Three common mistakes
- Phenomenon: After configuring
max_web_search_concurrentto a value greater than 6, the workflow still experiences loading timeouts or node blocking. Cause: The system-level concurrent configuration and workflow node parameters were not updated synchronously, only modifying the workflow configuration cannot break through the default limits. - Phenomenon: When different users use the same workflow, data crossover occurs in global variables with the same name. Cause: The configuration item for user isolation of global variables was not enabled. The default scope of global variables covers the entire workflow, and isolation is not performed by user ID.
- Phenomenon: After the AI generates a reply, only the system preset recommended follow-up questions are displayed, and custom guide text cannot be shown. Cause: No custom text output node was added to the post-reply node of the workflow, and only the system default recommended follow-up question configuration logic was relied on.
How to confirm the configuration is complete
- Trigger the workflow to pull game research report data, check whether the parsed document fields include the preset core fields, and whether the field units conform to industry specifications.
- Initiate multiple parallel research report retrieval requests, observe the execution status of workflow nodes, and confirm that no blocking or timeouts occur.
- Test the workflow initiated by different user IDs, check whether the values of global variables with the same name are independent and no data crossover occurs.
- Trigger the AI reply, check whether custom guide text is displayed below the reply content, which conforms to the preset guide logic.
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.