What data in this category looks like
Core data for this category comes from enterprise IAM systems, HR systems, business system logs, and approval systems. Commonly processed documents include account application forms, permission matrix tables, resigned account recovery forms, and annual security audit reports. Document structures are mostly structured tables and process-based text, with fields including account ID, permission scope, approval node, effective date, and no unified fixed format. Data update frequency is triggered by personnel changes, with a daily synchronization cycle for routine updates.
What constraints these characteristics impose on the document parsing and chunking workflow
Since data sources are scattered and document types vary, structured tables and unstructured process text require separate parsing rule adaptations. Permission matrix tables have strong field relevance, so chunking must retain complete field integrity and avoid splitting cross-row and cross-column permission entries. Approval process nodes on resigned account recovery forms must be kept in order, and logical sequence cannot be disrupted. Data has a high update frequency, so incremental parsing must be supported to prevent repeated processing. Long text in audit reports must be split by paragraph or chapter to avoid overly long single chunks that reduce retrieval accuracy.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the single-chunk field and paragraph lengths of permission matrices and audit reports, and avoids splitting critical permission entries |
chunkSplitter | Split by paragraph + title | Adapts to the logical structure of process-based documents and structured tables, and retains approval node and field relevance |
customPdfParse | Enabled | Optimizes cell recognition accuracy during PDF parsing for the table format of permission matrices |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the full parsing duration of large-volume documents such as audit reports, and avoids mid-parsing timeout interruptions |
UPLOAD_FILE_MAX_SIZE | 100 MB | Matches the common file size limits of enterprise audit reports and batch permission lists |
chunkSettingMode | Manual configuration | Allows separate adjustment of chunking rules for different document types, and adapts to non-standard formats exported from IAM systems |
Note: The configurations on this page are based on FastGPT v4.17.0
Three Common Misconfigurations
- After uploading a permission matrix PDF, search results do not display table content. The cause is that the
customPdfParseconfiguration is not enabled. The default parsing does not adapt to table cell recognition, resulting in lost table data. - After setting a fixed
chunkSizevalue, content truncation occurs when batch importing resigned account recovery forms. The cause is thatchunkSplitteris not used to split by title, and long process text is forcibly split by character count, which disrupts approval node logic. - After importing an Excel-format permission list, search tests return empty results and prompt a parsing error. The cause is that no parsing rule is configured for the corresponding file type, and the default parsing engine has insufficient recognition of structured fields in Excel.
How to Verify Configurations Are Correct
- Upload a single permission matrix PDF, and view the parsing preview on the knowledge base management page to confirm that table cells are fully displayed.
- Import a batch of resigned account recovery form documents, and check whether the chunking preview retains the sequential logic of approval nodes, with no cross-node splitting.
- Upload an audit report with a size under 100 MB, wait for parsing to complete, and check the task status to confirm no timeout errors.
- Perform a knowledge base search test, enter a specific account ID from the permission matrix, and confirm that chunks containing the ID can be retrieved.
The values provided are common starting points and should be evaluated 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.