What data in this category looks like
Data processed in this category originates from raw datasets, ETL logs, annotation platforms, and sampling results. Updates occur daily or per batch. Common documents include cleaning rules, quality inspection sampling tables, abnormal data lists, and delivery reports. Most document structures are structured tables, formatted rule entries, or detailed lists. Fields include data identifiers, exception types, sampling ratios, verification rules, and more. No unified mandatory units exist, and some fields must match the business units of the corresponding dataset.
Constraints imposed by these characteristics on document parsing and chunking
The data characteristics of this category create multiple constraints for the document parsing and chunking process. Multi-source data results in mixed parsing formats, requiring support for multiple document structures including structured tables, free-text rules, and detailed lists. High-frequency update requirements demand that parsing and chunking support fast batch processing, to avoid slowing data flow. Documents such as cleaning rules and abnormal lists must retain complete logical units; cross-associated rule entries or abnormal associated records cannot be split. Delivery reports must fully retain conclusions and supporting data, and chunking must not break the report’s logical integrity.
How to set configurations
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the logical unit length of documents such as cleaning rules and abnormal lists, avoiding splitting complete verification rules or abnormal associated records |
chunkSplitter | Semantic segmentation | Adapts to mixed-format documents in this category, retaining the semantic integrity of rules and abnormal records |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Matches the parsing duration of batch documents, avoiding timeout interruptions in high-frequency update scenarios |
customPdfParse | Enabled | Adapts to PDF format quality inspection reports exported from annotation platforms, retaining the correspondence between tables and text, and supporting image content extraction |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to the upload requirements of batch delivery reports and raw dataset documents |
chunkSettingMode | Manual configuration | Adapts to the diverse document structures of this category, flexibly adjusting chunking rules to match the logical units of different documents |
Three common misconfigurations
- When a document with a Java interface file suffix modified to .txt is uploaded, the parsing result contains no valid data. This occurs because custom parsing rules are not configured, so the format logic of code text cannot be handled.
- When using chunk mode to call the pushdata API to upload documents, the interface continuously displays the indexing status. This happens because the parsing timeout configuration is insufficient, and batch documents enter the indexing process before parsing is completed.
- After uploading a PDF-format quality inspection sampling table, images within the document are not parsed or extracted. This occurs because image extraction-related configuration is not enabled, and only plain text content in the PDF is parsed.
How to confirm the configuration is valid
- Upload a single cleaning rule document, verify that the parsed chunks retain complete verification rule entries, with no cross-logical unit splitting.
- Batch upload the abnormal data list for the same batch, confirm that parsing time does not exceed the configured timeout threshold, and no timeout error logs are generated.
- Upload code documents with modified suffixes, check that the parsing result includes valid content such as code fragments or interface definitions.
- Upload a PDF delivery report containing images, confirm that the parsing result includes text descriptions or extracted content corresponding to the images.
The values provided are common starting points and should be measured 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.