What the data for this category looks like
Game investment research data mainly comes from public game license announcement documents, manufacturer quarterly and annual financial reports, community player behavior data, channel revenue reports, industry research reports, and game development logs. Update cadences include scheduled (license announcements, financial report releases), monthly (revenue data), and real-time (community updates). Document structures fall into three categories: single game lifecycle metrics, track analysis reports, and policy and regulatory documents. Fields include DAU, revenue, license number, update time, with units of person-times, currency unit, string, and date format respectively.
Constraints These Characteristics Impose on Context and Token Management
The multi-source, varied update cadence, and complex document structure of game investment research data create multiple constraints for context and token management. Long multi-source documents such as industry research reports and complete financial reports consume large amounts of token quota. Context windows must be split reasonably to avoid exceeding model limits. Real-time community data and monthly revenue data require frequent index refreshes to prevent outdated information from being included in context and harming query accuracy. Differences in fields across documents, such as short metric documents for single games and long analysis reports for tracks, cause large fluctuations in recalled context length. Unified segmentation rules must be used to control token consumption. Consistency of professional terms and abbreviations must be maintained in context to avoid loss of critical information due to truncation.
How to Set the Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000-12000 characters | Adapts to the context association requirements of long game investment research documents, retaining sufficient field association information |
recallChunkSize | 1000-1500 characters per chunk | Matches the field density of game data documents, avoiding token waste caused by chunks that are too long or too short |
maxRecallCount | Top 6-8 entries | Covers the cross-reference requirements of game track research reports and single game data, avoiding redundant recalled content |
tokenLimitPerRequest | 16000-24000 total tokens | Adapts to the context window limits of mainstream large models, avoiding token overflow errors during requests |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapts to the parsing time of long documents such as complete financial reports, preventing unexpected termination during parsing |
similarityThreshold | 0.75-0.85 | Filters low-relevance redundant recalled content in the game domain, accurately matching investment research query requirements |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require on-site analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Model service restarts continuously after internal deployment, with logs showing
failed. Cause: Offline deployment model dependency package paths are not configured correctly, preventing necessary model files from being loaded during startup. - Symptom: Knowledge base search return result count does not match the set
maxRecallCount, and single-round request token consumption exceeds the preset limit. Cause:recallChunkSizeis not adjusted for long game documents, causing the total length of segmented recalled text to exceed the context window and triggering additional token consumption. - Symptom: After configuring a custom
openaiToken, the knowledge base function still incurs additional fees. Cause: The platform's default billing channel is not disabled in system settings, causing the custom token to not be called correctly.
How to Confirm Proper Configuration
- Upload the quarterly financial report document for a single game, check the number of parsed segments, and confirm that the segment length matches the value of
recallChunkSize. - Initiate a query that includes multi-source game data, check the
total_tokensfield in the request logs, and confirm that it does not exceed the preset value oftokenLimitPerRequest. - Check the model call logs, and confirm that the model version used in the returned results matches the configured
modelparameter. - Restart the service in the internal network environment, check the console logs, and confirm that there are no
failed-type errors and the service is running normally.
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.