What the data for this category looks like
This category of data primarily comes from databases, data dictionaries, masking tools, and access approval systems. Commonly processed documents include masking plans, field mapping tables, masking validation reports, and approval records. Data is updated in batches per task or individual project. Data structures vary across different projects. Core fields include sensitive field identifiers, masking rule codes, validation result statuses, approval workflow nodes, and similar items. There are no unified fixed units, and values adjust based on document type.
What constraints these characteristics impose on the document parsing and chunking link
Data sources and document structures for this category vary widely. They include structured tables exported from databases, formatted reports generated by masking tools, and plain text records from approval workflows. This requires the document parsing process to support multiple formats. Data is updated in batches per project. Field mappings and sensitive field distributions differ across projects. Chunking must retain the contextual association between fields and their corresponding rules. Sensitive field identifiers are scattered across different locations in documents. Chunk length must match the business logic linked to fields. This avoids splitting complete information for critical rules and validation results.
The following configurations are based on FastGPT v4.17.0.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of single-segment information in masking plans and field mapping tables, avoiding splitting associated content of sensitive fields and their corresponding rules |
chunkSplitter | Chunk by paragraph and semantic chunking | Supports structures of different documents, retaining logical integrity of approval records and validation reports |
customPdfParse | Enabled | Adapts to the PDF format of masking validation reports, preventing the default parser from losing sensitive field information within tables |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Matches the parsing time of single-project documents, preventing parsing interruptions for large masking plan documents |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to batch document upload requirements for single projects, preventing upload failures due to oversized files |
chunkSettingMode | Manual configuration | Adapts to differences in document structures across projects, allowing flexible adjustment of chunking parameters |
Three common mistakes
- Phenomenon: PDF parsing tasks return format errors or empty content logs. Cause: The
customPdfParseparameter is not enabled. The default parser cannot adapt to the complex table structure of masking validation reports. - Phenomenon: Chunking results split the associated content of sensitive fields and their corresponding masking rules. Cause: The
chunkSizevalue is too small, and does not match the full length of single-segment business information. - Phenomenon: Uploaded structured files cannot retain field mapping relationships. Cause: Manual configuration of
chunkSettingModeis not enabled, and default fixed chunking rules are used.
How to confirm the configuration is complete
- Upload a masking validation report PDF file. Check if the parsed text retains field mappings and sensitive field identifiers within tables. This confirms the
customPdfParseconfiguration is effective. - Adjust the
chunkSizeparameter. Confirm that the single-segment length of chunking results meets expectations, and no critical business associated content is split. - Upload a single document under 1000 MB. Confirm that the parsing task completes within the time set by
PARSE_FILE_TIMEOUT_SECONDS. - Check the variables output by the node. Confirm that they include parsed chunked content and the association identifier of the original file.
The values provided are common starting points and should be evaluated against samples relevant to the specific use case.
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.