What the data for this category looks like
Game industry intelligent due diligence report data primarily comes from game license approval documents, R&D progress documents, revenue monitoring data, user behavior reports, and compliance filing materials. The data update rhythm changes with project phases: license information is updated quarterly, R&D progress is updated in real time alongside version iterations, and revenue and user data are updated monthly.
The structure of a single report document includes fields such as license number, full name of the R&D entity, online filing time, core gameplay description, revenue forecast range, and registered user scale. User scale is measured in ten thousand units, revenue is measured in ten thousand yuan units, and license numbers follow the unified format of the national press and publication authority.
What constraints these characteristics impose on deployment and upgrade
Game due diligence report data sources are scattered and have widely varying update frequencies. The deployment link must support batch access configuration for multiple data sources, to avoid full report generation being impacted by a single data source failure.
Document lengths are generally long, with some including R&D document attachments, so resource configuration must support large file uploads and long text parsing. There is a need for custom extension of field structures, so the platform must support storage and retrieval configuration for custom fields.
The update link must support incremental synchronization, to avoid excessive resource occupancy caused by full re-imports. It must also be compatible with field format changes across different versions, to prevent data parsing exceptions after upgrades.
Additionally, compliance data for game projects requires real-time verification. During deployment, a load balancing strategy for real-time interface calls must be configured to ensure request response speed.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 2000 MB | Game due diligence reports may include large attachments such as R&D documents and compliance filing scans |
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Long text parsing for game due diligence reports takes significant time, so the timeout threshold must be extended |
Chunk size | 1000–1200 characters | Game due diligence reports have many fields, so segmentation must cover complete field groups to ensure retrieval completeness |
LOAD_BALANCER_ENABLED | Enabled | Requests for due diligence report generation across multiple model channels must be distributed evenly to ensure concurrent processing capacity |
split_mode | paragraph_first | Compatible with the paragraph-first splitting mode introduced in v4.9.10; this parameter must be specified when creating a text collection via the interface |
ARM64_DEPLOY_SUPPORT | Enabled | Compatible with arm64 architecture deployment environments for Huawei openEuler Linux |
The parameter values provided on this page are all common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Scenario: Container startup fails with the "unsupported platform" error when deployed on an arm64 architecture environment of Huawei openEuler Linux. Cause: The arm64 architecture deployment configuration item is not enabled; the default setting only supports x86 architecture.
- Scenario: After upgrading from v4.9.0 to v4.9.10, calling the interface to create a knowledge base collection returns a 400 status code. Cause: The
split_modeparameter is not passed as required by the new version; the old version interface parameter format is not compatible. - Scenario: Request timeout occurs when generating game due diligence reports, returning a 504 status code. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted; the default timeout threshold is insufficient to cover long text parsing time.
How to confirm configurations are correct
- Run the
docker pscommand to check that all FastGPT-related containers are in a running state, confirming that the arm64 deployment configuration is active. - Call the interface for creating a knowledge base collection, passing the
{"split_mode": "paragraph_first"}parameter. A 200 status code indicates that the parameter configuration is correct. - Upload a test file containing R&D document attachments, check the parsing logs to confirm that the uploaded file size does not trigger the
UPLOAD_FILE_MAX_SIZElimit. - Check the model request forwarding logs to confirm that requests across multiple model channels are evenly distributed, and the load balancing configuration is active.
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.