What data in this category looks like
Data for this category typically comes from customer HR systems, identity directories, product backends, and audit logs. Update cadence is per individual request or irregular. Daily produced documents include permission matrices, organizational structure tables, role configuration records, and permission change forms. Most fields include department identifiers, role codes, permission scopes, and effective nodes. There is no unified fixed format. Some documents are updated frequently alongside customer organizational adjustments.
What constraints do these characteristics impose on the document parsing and chunking stage
In FastGPT v4.17.0, scattered data sources and inconsistent formats require the parsing process to support multiple document structures. This prevents accidental splitting of field combinations linked to permissions. Irregular update cadence means chunking logic must adapt to both incremental and full-scenario processing. This stops repeated parsing of already processed permission configurations. Errors in permission-related documents can lead to issues such as unauthorized access. Chunking must retain the binding relationship between permissions and their corresponding roles and departments. Business data integrity must not be compromised.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Permission-related documents have dense fields. This range preserves the binding relationship between permissions and roles, avoiding splitting critical associated units |
chunkSplitter | Semantic grouping chunking | Adapts to the hierarchical structure of organizational structure tables, ensuring each chunk contains complete department or role permission configurations |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Covers the parsing duration of large permission change forms, preventing processing interruptions due to timeouts |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Meets the upload requirements for large organizational structure export files, avoiding interception due to oversized files |
trainingType | Incremental training | Matches the irregular update cadence of this category, reducing repeated parsing of already processed permission configurations |
customPdfParse | Enabled | Adapts to the fixed header format of permission matrix PDFs, accurately extracting permission configuration fields within tables |
Three Common Mistakes
- Data processing returns empty after uploading permission-related documents. The cause is that
customPdfParseis not enabled. This misjudges PDF headers as content separators, making it impossible to extract valid permission configuration fields. - Vectorization takes too long after uploading documents to the knowledge base. The cause is that
chunkSizeis set too small. This generates an excessive number of chunks, increasing vectorization computation load. - Ordinary question text triggers the document parsing process. The cause is that
trainingTypeis configured as full training. This covers all input types, and does not distinguish between question requests and document parsing requests.
How to Confirm Configurations Are Correct
- Upload a test permission matrix PDF. Review the field list after data processing to confirm it includes core fields such as department, role, and permission scope.
- Submit an organizational structure table file larger than conventional size. Confirm no upload size limit error message is triggered.
- Wait for parsing to complete. Review chunk logs to confirm each chunk does not split the binding content between permissions and their corresponding roles, and that character counts fall within the 800–1200 character range.
- Initiate an ordinary text question. Confirm the conversation process does not trigger logs related to document parsing.
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.