What this category of data looks like
Data for this category originates from social insurance management systems, tax social insurance premium collection systems, market entity registration information, and shared public security and civil affairs data. Commonly processed documents include enrollment registration forms, payment base declaration forms, personnel adjustment forms, social insurance premium verification forms, and personal benefit records. Most documents use structured table or fixed-header form formats. They contain fields such as the unit’s unified social credit code, personnel identity information, payment verification base, and verified amount. Data is updated daily.
Constraints on the document parsing and chunking stage
The characteristics of this data category impose multiple constraints on the document parsing and chunking stage. For structured form documents with fixed headers and field binding relationships, parsing must retain the correspondence between fields and content. Chunking must not break complete data units for individual personnel or units. Documents updated in daily batches require parsing to support multi-file batch processing, to avoid single-file parsing timeouts. Documents from multiple sources have format differences. Parsing must adapt to header layouts of different templates, and chunking boundaries must be adjusted based on document type. Fields containing sensitive identity information require chunking to retain the integrity of associated data, to avoid splitting that breaks the link between personnel and payment verification information.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the field and paragraph length of enrollment registration documents, avoids splitting linked personnel payment data |
chunkSplitter | Split by header + row blocks | Adapts to the layout of structured form documents, retains the integrity of individual records |
customPdfParse | Enable structured parsing mode | Adapts to the fixed field layout of social insurance PDF forms, improves parsing accuracy |
UPLOAD_FILE_MAX_SIZE | 100 MB | Adapts to large files such as personnel adjustment forms submitted by batch units, avoids upload failures |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Covers the time required for batch parsing of multiple documents, avoids mid-process timeout interruptions |
chunkSettingMode | Manual configuration | Flexibly adjusts chunking parameters for different types of social insurance documents, adapts to diverse form formats |
Three Common Mistakes
- Phenomenon: Embedded images in PDF documents cannot be read by the large model. Cause: The image extraction option for custom PDF parsing is not enabled, and only text content is extracted.
- Phenomenon: Importing a publicly shared Yuque link fails during parsing. Cause: The link is not set to publicly accessible permissions, or the link format does not meet the requirements for knowledge base parsing.
- Phenomenon: Parameter parsing works normally when calling a custom HTTP tool, but the corresponding operation is not executed. Cause: The request trigger logic for the tool is not configured, or the interface does not open the corresponding operation permissions.
How to Verify Proper Configuration
- Upload a single enrollment registration form PDF, check if the parsed text retains the binding relationship between headers and corresponding rows, and verify that chunking does not break individual personnel data.
- Batch upload multiple social insurance documents, check the completion status of parsing tasks, and confirm that no timeout or upload limit error occurs.
- After configuring the custom PDF parsing mode, upload a personal benefit record document containing embedded images, and verify that the parsing result includes image-related descriptions.
- Test calling the HTTP tool, pass preset parameters, and check the interface log to confirm that parameters are correctly passed and execution is triggered.
These values are common starting points, and should be validated against samples used in actual deployments.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.