Data Characteristics for This Category
This category of data typically originates from level editors, art resource libraries, player test data, and defect tracking systems. Update cycles follow weekly or per-version submission cycles. Daily produced documents include level design drafts, resource requirement sheets, test feedback forms, and version submission records. Most document fields include level ID, resource path, test case number, submission version number, and similar items. Some documents include specific numerical values with units such as coordinates and resource quantities.
Constraints Imposed on Document Parsing and Chunking
Documents for level content creation have diverse types, including structured tables, long text descriptions, and structured records. Some documents contain numerical fields with units such as coordinates and resource quantities. The high-frequency update feature requires chunking logic to adapt to rapidly iterating document content. This avoids context breaks caused by overly coarse chunking, or increased retrieval redundancy caused by overly fine chunking. Compliance review requires retention of complete fields. Chunking must not truncate key compliance-related fields such as level ID and version submission records. It must also adapt to multi-source documents of different formats to ensure parsing consistency.
Configuration Settings
The following configurations are based on the document parsing and chunking logic of FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapt to long text paragraphs in level design drafts and structured row groups in resource requirement sheets, avoid truncating key fields containing resource paths |
chunkSplitter | Split by paragraph + heading | Match the chapter structure of level documents, retain contextual associations such as level paths and resource placements, adapt to Excel-formatted resource requirement sheet row groups |
customPdfParse | Enabled | Adapt to exported level design draft PDFs, retain original document table and heading hierarchy, support field integrity required for compliance reviews |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Accommodate batch resource requirement sheets and test feedback document collections submitted in a single version, adapt to multi-attachment upload scenarios |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Avoid parsing timeouts for large level design drafts or batch Excel files, adapt to full parsing duration for multi-format files |
chunkSettingMode | Manual configuration | Adjust chunking rules for different document types, adapt to mixed scenarios of structured and unstructured documents |
Three Common Configuration Mistakes
- Resource path fields are empty in post-parsing chunks. This occurs because
chunkSizeis set too small, truncating complete row groups containing resource paths, which prevents subsequent compliance reviews from matching corresponding level content. - Timeout errors are returned when parsing large Excel-formatted resource requirement sheets. This occurs because
PARSE_FILE_TIMEOUT_SECONDSis set lower than the actual required parsing duration, and does not adapt to the processing rhythm of batch data. - Undesensitized player test data is identified during compliance review. This occurs because sensitive field filtering configuration in
customPdfParseis not enabled, causing sensitive data to enter the knowledge base along with chunks.
How to Verify Correct Configuration
- Upload a single typical level design draft, view the parsed chunk list, confirm each chunk contains complete chapters and key fields.
- Upload a batch of resource requirement sheet Excel files, check the completion status of parsing tasks, confirm no timeout errors occur.
- For test feedback forms containing sensitive fields, verify parsed chunks do not include undesensitized test data.
- Adjust the
chunkSizeparameter, upload the same document, compare chunking results, confirm chunking logic matches expected configuration.
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.