What the data for this category looks like
Daily documents for game planning and numerical design include gameplay design plans, numerical tables, version requirement sheets, and balance test records. Data sources include player behavior data, public competitor materials, battle logs, and version feedback. Document update frequency is weekly or per version cycle. Some core numerical tables are fine-tuned based on battle test results. Document structures include structured parameter fields and unstructured rule descriptions. Field units include level, percentage, second, damage value and other category-specific identifiers.
What constraints do these characteristics impose on the document parsing and chunking link?
Documents in this category combine structured numerical tables and unstructured rule text. Large version requirement sheets and balance test records often span hundreds of pages. Parsing processes must avoid splitting that breaks numerical associations and formula integrity. Frequent version updates require stable parsing workflows. These workflows must prevent errors in subsequent numerical simulation and rule breakdown caused by chunking mistakes. Specialized units for segmented fields require precise identification during parsing. Confusing numerical content with different units will increase correction costs for subsequent processing.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the field length of numerical tables and paragraph length of gameplay design plans, avoids splitting battle formulas and core rules |
PARSE_FILE_TIMEOUT_SECONDS | 1200 seconds | Covers parsing time for hundreds of pages of large documents, prevents parsing interruptions due to timeout |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Accommodates the file size of single large version requirement sheets or balance test records |
customPdfParse | Enabled | Retains table structure and text layout in PDF documents, prevents loss of row and column information for numerical tables |
chunkSplitter | Split by paragraph + table blocks | Distinguishes rule paragraphs in design plans and independent data blocks in numerical tables, ensures chunking logic matches document structure |
chunkSettingMode | Manual mode | Adjusts chunking parameters for different document types, adapts to the characteristics of specialized category documents |
Three common configuration errors
- Phenomenon: Parsing failures occur when processing numerical table documents containing nested JSON formats. Cause: The default chunking rule splits the JSON key-value pair structure, breaking the integrity of numerical associations.
- Phenomenon: After deploying FastGPT v4.17.0 locally, the parsing function fails when uploading PDF documents with more than 300 pages. Cause: The
UPLOAD_FILE_MAX_SIZEandPARSE_FILE_TIMEOUT_SECONDSparameters are not adjusted, exceeding default configuration thresholds. - Phenomenon: Errors trigger when using doc2x to parse hundreds-page balance test record PDFs; documents with tens of pages parse normally. Cause: doc2x parsing time for large files exceeds the default timeout limit, and no chunking retry mechanism is configured.
How to confirm configuration correctness
- Upload a single numerical table PDF with fewer than 100 pages, verify that parsed chunks retain the complete row and column structure and numerical associations of the table.
- Upload a single large gameplay design plan PDF with 500 pages, verify that parsing time falls within the range set by
PARSE_FILE_TIMEOUT_SECONDSand no timeout errors occur. - After enabling
customPdfParse, upload a document containing JSON-formatted numerical data, verify that the parsed result completely retains the key-value pair structure and field units. - After configuring a queuing mechanism, upload three large documents simultaneously, verify that parsing completes in sequence with no concurrent timeouts or parsing interruptions.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.