What the Data for This Category Looks Like
Game industry intelligent due diligence reports draw data from multiple sources. These include game license approval public documents, manufacturer R&D progress documents, monthly revenue settlement reports, user retention analysis reports, and compliance filing materials.
The update rhythm of data varies by document type. Approval batches trigger updates for license documents. Project milestones drive iterations of R&D documents. Monthly or quarterly cycles update revenue and user data. Annual revisions update compliance materials.
Two types of document structures exist. The first is table-based documents with structured fields. These fields include project ID, license code, R&D cycle, monthly revenue, DAU value, and more.
The second type is long-text research reports. These reports intersperse charts and compliance notes. Their field units include ten thousand yuan, person-times, natural day, and natural month, among others.
What Constraints Do These Characteristics Impose on Document Parsing and Chunking
The two document structures for game due diligence reports create multiple parsing and chunking constraints.
For structured table documents, teams must accurately identify table headers and corresponding data rows. They must avoid splitting table titles into independent content chunks. They must bind fields to their matching units to prevent separation of numerical values and units.
For long-text research reports interspersed with compliance notes and data charts, teams must retain annotation text linked to charts. They must avoid breaking logical connections across charts.
When documents with different update cycles are uploaded in batches, teams must distinguish each document’s timeliness identifier. This prevents confusion between old R&D data and new revenue data.
Game-specific terms such as "closed beta" and "iterative version" must be fully identified. Term splitting will cause information loss.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_TABLE_STRUCTURE | Enabled | Game due diligence reports contain large volumes of structured revenue and user data tables. Retaining row and column correspondence avoids information misalignment |
CHUNK_SIZE | 800–1200 characters | Adapts to the logical paragraph length of game research reports. It balances contextual coherence and information integrity during retrieval |
PARSE_INCLUDE_CHART_CAPTION | Enabled | Compliance notes and data annotations linked to charts in game documents are core analytical content. These must be extracted alongside charts |
UPLOAD_FILE_MAX_SIZE | 500 MB | Covers the size upper limit for multiple game due diligence attachments uploaded in a single batch |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapts to parsing times for large revenue tables and long-text research reports. It prevents mid-process interruptions |
METADATA_EXTRACT_FIELDS | Project ID, license code, update date | Extracts core identifier fields for game due diligence. This facilitates subsequent retrieval by project and timeliness |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- After uploading an Excel-format game revenue document, the parsing result only extracts plain text and loses row and column structure. Cause: The
PARSE_TABLE_STRUCTUREconfiguration item is not enabled, and the default parsing mode only extracts plain text content. - After calling a third-party document parsing tool, game-specific terms such as "closed beta" and "stress test" are incorrectly split. Cause: A parsing model adapted to game industry terms is not specified, or parsing rules adapted to game industry terms are not bound in the configuration.
- Retrieved content chunks do not include pre-configured metadata, and cannot be associated with project IDs or update dates. Cause: Corresponding fields are not extracted via the
METADATA_EXTRACT_FIELDSconfiguration, or the extracted metadata is not bound to content chunks during the chunking process.
How to Confirm the Configuration Is Correct
- Upload a test game revenue Excel document, and check whether the parsing result retains complete row and column structure and field units.
- Upload a game research report PDF with charts, and check whether the parsing result includes chart titles and associated annotations.
- Initiate a retrieval test, enter keywords, and check whether returned results include pre-configured metadata fields.
- Upload multiple documents with different update cycles, and check whether each document’s update date identifier is retained after parsing.
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.