What the data for this category looks like
Game intelligent due diligence report data mainly comes from the National Press and Publication Administration version number publicity platform, game manufacturers' public financial reports, mainstream app store rankings, community public opinion databases, and game filing systems. Data update rhythms fall into two categories: batch and real-time. Version number information updates per approval batch. Financial reports update quarterly or annually. App store download and public opinion data sync in real time.
Document structure includes fixed fields: version number, R&D and publishing entity name, revenue composition fields, user profile tags, and compliance filing status. Field units are standardized statistical units such as ten thousand downloads, ten thousand yuan in revenue, and number of natural persons.
What constraints do these characteristics impose on model access and configuration
The multi-source heterogeneous nature and batch update rhythm of due diligence data impose multiple constraints on model access and configuration.
Multi-source data includes PDF version number files, structured financial report tables, and unstructured public opinion text. Model access parameters must be adapted for different formats to avoid parsing failures.
Version number information updates per approval batch. Scheduled task trigger logic for batch model calls must be configured to match batch data processing volume.
Compliance-related fields such as filing status and version number require high recognition accuracy. The model temperature parameter and context window length must be adjusted to avoid field recognition deviations.
Real-time public opinion data sync requires low-latency model call channels to match real-time data processing rhythms.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Game due diligence reports contain long texts spliced from multiple sources, so long context processing capability is needed to avoid truncating critical information |
rerank_model | Select from the preset reranking model list, prioritize domain-adapted models | Game due diligence data includes professional texts such as rankings and public opinion. Adapting to domain models can improve the relevance of recall results |
model_group | Create a dedicated group named game_due_diligence and bind the corresponding model key | Separating scenarios avoids resource conflicts between different tasks and resolves errors where no available channel exists for the group |
parse_chunk_size | 1000–1500 characters | The single-paragraph text length of game due diligence reports is moderate. This chunk size balances model token utilization and context coherence |
timeout | 300–600 seconds | Parsing batch version number data and model calls require long processing times. This range reduces task timeout failures |
temperature | 0.1–0.3 | Game due diligence requires high-accuracy field recognition. Lower temperature reduces hallucinations and improves the accuracy of key information such as version numbers and compliance status |
The parameter values provided on this page are all common recommendations used as a starting point 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
- Symptom: A prompt appears when calling the model stating "No available channel for model gpt-4o-mini under current default group". Cause: No dedicated model group has been created for the game due diligence scenario, or the target model has not been bound to the corresponding group.
- Symptom: Reranking results do not match expectations, with a high proportion of non-game-related texts being recalled. Cause: No game-domain-adapted reranking model has been selected, and a general reranking model is still used to process professional texts.
- Symptom: Batch version number parsing tasks frequently experience timeout failures. Cause: The
timeoutconfiguration item has not been adjusted, and the default short timeout period is used, which cannot cover the time required for multi-document processing.
How to confirm the configuration is complete
- Upload a single game version number PDF document, trigger model calls, and verify whether key fields such as version number and R&D entity are accurately extracted from the returned results. Adjust parameters based on scenario requirements.
- Initiate a batch test of batch data, check whether the model call group normally assigns tasks, and there are no errors of "no available channel".
- Initiate a real-time public opinion data sync test, verify whether the model call delay meets scenario requirements, and adjust related configurations based on real-time performance requirements.
- View the recalled results after reranking, verify whether texts strongly related to game due diligence are returned first, and adjust the reranking model configuration based on recall accuracy.
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.