What the Data for This Category Looks Like
Core data for this category comes from ad platform backends, customer qualification upload systems, internal contract libraries, and regulatory public rule libraries. Daily processed documents fall into four categories: ad review forms, qualification attachments, delivery records, and rectification notices. Ad review forms include delivery identifiers, compliance check fields, and associated attachment IDs. Qualification attachments are mostly stamped scanned PDFs or image files. Delivery records are tabular documents organized by campaign batches. Data updates are triggered by individual marketing campaigns. Monthly cumulative update volume fluctuates with campaign scale, with no fixed batch cycle.
Constraints Imposed on Document Parsing and Chunking by These Data Characteristics
The data characteristics of this category impose multiple constraints on the document parsing and chunking process. Multi-source heterogeneous document formats (scanned items, tables, plain text) require parsing tools to support structured extraction and scanned text restoration. Updates triggered per individual marketing campaign require parsing workflows to support batch triggering per campaign, adapting to non-fixed update cycles. The non-missing requirement for compliance evidence requires chunking processes to retain field association relationships, avoiding loss of binding information between delivery identifiers and compliance numbers. Error consequences directly relate to regulatory penalties and customer accountability, so parsing processes must include failure retry and breakpoint resume capabilities, while supporting compatibility with output differences across parsing engine versions.
Configuration Settings
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Ad review forms and delivery records mostly use short paragraph + table structures. Excessively long chunks will damage the associative integrity of compliance fields, while excessively short chunks will increase the difficulty of evidence chain splicing. |
chunkSplitter | Split by paragraph + header and footer | Delivery records and rectification notices often have campaign batch headers. Retaining headers can associate chunked content with the corresponding marketing campaign, avoiding evidence confusion. |
customPdfParse | Enable table structured parsing | Qualification attachments and delivery records mostly contain tabular data. Structured parsing can fully extract key compliance fields such as qualification numbers and delivery time periods. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | The collection of delivery records for a single campaign may contain multiple batches of data. Sufficient time is required to complete full parsing, avoiding compliance evidence loss due to timeout interruptions. |
UPLOAD_FILE_MAX_SIZE | 100 MB | The collection of delivery records for large-scale marketing campaigns may exceed conventional file size limits, adapting to batch storage and parsing requirements for compliance archiving. |
chunkSettingMode | Manual configuration | Adjust chunking rules individually for different document types such as ad review forms, qualification attachments, and delivery records, adapting to the heterogeneous document structure of this category. |
Three Common Misconfigurations
- Phenomenon: File parsing tool returns timeout error or no response, with a 504 status code. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted. The parsing time for large delivery record collections exceeds the default threshold, causing task interruption. - Phenomenon: Parsed chunked content loses stamped area text or table fields from qualification attachments. Cause: Structured scanning parsing for
customPdfParseis not enabled, only default plain text extraction is used, which cannot restore tables and stamped text from scanned documents. - Phenomenon: Local deployment version cannot parse complex formulas or table structures, while the online version returns results normally. Cause: Mismatch of PDF parsing engine versions due to FastGPT version differences. The local older parsing module is not adapted to complex layout structures.
How to Confirm Configurations Are Properly Set
- Upload a single ad review form PDF, check if the parsed chunks include core fields such as delivery time period and compliance number, with no missing or garbled text.
- Upload a collection file containing multiple batches of delivery records, confirm that the parsing process does not experience timeout interruptions, and all batch record contents are fully output.
- Upload a stamped scanned qualification attachment, check if the structured parsing results retain key compliance information such as qualification number and validity period, with no missing fields.
- Test calling the file parsing tool, confirm that parsing tasks can be triggered normally, with no call failure error prompts.
The values listed above are common starting points. Adjust them based on local sample data.
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.