What Data in This Category Looks Like
Data for this category originates from software asset management systems, procurement systems, terminal scan results, and vendor authorization documents. Commonly processed documents include license ledgers, software usage lists, procurement contracts, and compliance audit reports. Updates occur monthly or quarterly. Most document structures contain fields such as authorization number, software name, authorized quantity, number of active terminals, and expiration date. Units include sets, units, pieces, and others, which must align with standard measurement methods authorized by vendors.
Constraints Imposed by These Characteristics on Document Parsing and Chunking
License ledger fields have strong interrelationships. Authorized quantity and number of active terminals must be strictly correlated. Parsing processes must retain these field relationships to avoid splitting critical information across separate chunks. Procurement contracts and compliance audit reports are typically long texts with compliance clauses. Chunking must preserve context integrity to prevent loss of authorization scope recognition after splitting. Monthly updated ledger data must meet batch parsing efficiency requirements. Timeouts or overly large chunks may cause data synchronization delays. Software usage lists from terminal scans have inconsistent formats. Parsing workflows must support multi-format flexibility while ensuring accurate extraction of authorization status fields.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the field density of license ledgers and context integrity of long-text contracts, avoiding splitting critical authorization-related information |
chunkSplitter | Semantic chunking | Aligns with the clause structure of compliance audit reports and procurement contracts, preserving contextual logic |
customPdfParse | Enable high-precision mode | Targets the PDF format of vendor authorization documents, extracts embedded authorization numbers and stamped information, and avoids OCR errors |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports batch uploads of quarterly updated compliance audit reports, preventing single upload size limits from being exceeded |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Matches the parsing duration of long-text compliance audit reports, preventing mid-process timeout interruptions |
chunkSettingMode | Manual configuration | Adjusts chunking parameters flexibly for different types of license management documents, adapting to the differentiated needs of ledgers and contracts |
Three Common Errors
- Uploading a license ledger PDF results in no search results, with a parsing error displayed in the interface. The cause is that the
customPdfParsehigh-precision mode was not enabled, leading to failure to correctly extract embedded authorization number fields. - The corresponding parsing function cannot be found after an upgrade. The cause is that FastGPT v4.17.0 disables the legacy parsing plugin by default. Switch to the compatible parsing engine in the
customPdfParseconfiguration. - An OCR Error is triggered when parsing large compliance audit reports. The cause is that the
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted, causing the OCR process to interrupt due to long-text parsing timeout.
How to Verify Configurations Are Correctly Set
- Upload a single license ledger PDF, check if the parsed text includes core fields such as authorization number and software name, and confirm field integrity.
- Upload a batch of quarterly updated software usage lists, check the completion status of parsing tasks, and confirm no timeout errors occur.
- Trigger a search test, enter a query related to authorized quantity verification, and check if the returned results include chunked related information, confirming that context is not split.
- Adjust the
chunkSizeparameter to the boundary values of its range, verify that the number of parsed chunks matches expectations, and confirm that the configuration takes effect.
The values listed are common starting points and should be evaluated against the user’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.