What This Category of Data Looks Like
Data for this category originates from sources including engineering quality and safety supervision systems, on-site real-name management platforms, supervision logs, test reports, and similar systems. Daily generated documents include supervision plans, supervision inspection records, hidden danger rectification notices, quality problem handling forms, and completion acceptance supervision reports. Data is updated weekly. Most document structures combine structured tables and free text. Fields cover supervision subjects, inspection items, rectification requirements, acceptance conclusions, and other relevant content. There is no unified fixed format template. Some documents contain non-standard content transcribed from handwritten scanned documents.
What Constraints Do These Characteristics Impose on Document Parsing and Chunking?
Batch documents updated weekly require parsing to support batch tasks and broken upload resumption. The non-standard mixed format of structured content and free text requires adaptation to multiple types of document parsing logic. This avoids parsing rules that only support plain text or fixed tables. Documents contain hand-transcribed content from on-site inspections. Fuzzy recognition and format repair must be supported. Mixed uploads of documents with different formats in batch tasks require automatic adaptation of parsing rules. This prevents a single abnormal document format from interrupting the entire task. Some documents come from scanned files. Pre-processing via OCR must be supported to ensure accurate text extraction.
Configuration Settings
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value Range/Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | This category of documents mixes structured tables and free text. This range balances context completeness and retrieval accuracy |
chunkSplitter | Split by paragraph + title | Adapts to the format of this category's documents, which include supervision plans, rectification notices and other materials with clear title hierarchies, and avoids breaking the association between titles and their corresponding content |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Meets time requirements for parsing single large test reports or batch documents |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports uploading large-volume documents such as single completion acceptance supervision reports |
customPdfParse | Enabled | Adapts to OCR parsing requirements for scanned PDF documents, and improves extraction accuracy for non-standard documents |
Three Common Configuration Mistakes
- File uploads via the dialog box never complete parsing, and no error logs appear in the background. This occurs when the parsing plugin for the corresponding document format is not enabled, or
UPLOAD_FILE_MAX_SIZEis set smaller than the uploaded file's size. - Batch uploads of large numbers of documents interrupt halfway, and restarting Docker does not resume parsing. This occurs when the broken upload resumption parameter is not configured, or
PARSE_FILE_TIMEOUT_SECONDSis set too short, causing timeout during parsing of large documents. - Document content is incomplete after parsing, and the model cannot answer questions using the document. This occurs when
chunkSizeis set too large or too small, causing key context to be lost during chunking, orcustomPdfParseis not enabled, preventing text extraction from scanned PDF documents.
How to Verify Correct Configuration
- Upload a single typical document, such as a supervision inspection record, and check if the parsed text content fully covers all fields and paragraphs.
- Batch upload 3 to 5 documents of different types, and verify that the parsing task completes normally without interruptions.
- Test uploading a scanned PDF test report, and confirm that hand-transcribed text content can be extracted after parsing.
- Adjust the
chunkSizeparameter, compare chunking results across different values, and confirm that the result matches the document's paragraph and title hierarchy.
The configuration values listed here are common starting points. Adjust them based on your own specific samples and use cases.
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.