Applicable to FastGPT v4.17.0
What Data for This Category Looks Like
Data sources for this category include raw datasets, ETL operation logs, annotation platform output results, and sampling quality inspection results. Data update frequency is daily or triggered per batch task. Daily processed documents include cleaning rule documents, quality inspection sampling tables, abnormal data lists, and delivery reports. Data fields include data batch identifiers, abnormal type tags, processing status, and update timestamps. No non-standard units are used. Most field values combine structured entries and text descriptions.
Constraints on Document Parsing and Chunking
Raw dataset scale fluctuates per batch. ETL operation logs contain multiple lines of sequential text. Quality inspection sampling tables are mostly structured tables with fixed headers. Document parsing must distinguish between structured tables and unstructured cleaning rule text. Chunking must adapt to length differences across documents. Daily updated datasets require avoiding truncation of batch-related information during chunking. Abnormal data lists for each batch task must retain logical connections between entries. Chunk length must balance matching accuracy of subsequent cleaning rules and retrieval efficiency. Timeout handling must adapt to long-text parsing requirements of ETL logs.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the text matching granularity of cleaning rules, avoids truncating batch-related information |
chunkSplitter | Segment by semantic and paragraph boundaries | Adapts to the entry connection logic of abnormal data lists, retains context coherence |
customPdfParse | Enable table recognition mode | Adapts to structured content extraction from quality inspection sampling tables, avoids losing the correspondence between headers and data |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to upload requirements for batch raw datasets, prevents parsing failures for large files |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to long-text parsing duration of ETL logs, prevents task interruption due to timeout |
chunkSettingMode | Auto-adapt by document type | Balances chunking requirements for different documents such as cleaning rules and sampling tables, reduces manual adjustment costs |
Three Common Misconfigurations
- Phenomenon: After uploading compliance materials, the parsing node has no task execution logs, and the interface shows the file in pending parsing status beyond the preset duration. Cause: The
PARSE_FILE_TIMEOUT_SECONDSconfiguration value is set too low, orUPLOAD_FILE_MAX_SIZEis not adapted to large raw datasets, causing the parsing process to be terminated by the system. - Phenomenon: When fetching data from database tables, only partial entries are returned, and fields are missing. Cause: The
chunkSizeconfiguration value is too small, truncating table-related data across chunks, orchunkSplitteris not configured to segment by tables, causing structured content to be split and lost. - Phenomenon: When parsing compliance documents containing Markdown format tables, the output result does not retain the table structure, only plain text paragraphs. Cause: The table recognition mode of
customPdfParseis not enabled, orchunkSettingModeis not adapted to the parsing rules of Markdown documents, causing table content to be mistakenly split into ordinary text.
How to Verify Correct Configuration
- Upload a single test cleaning rule document, check if the parsing result retains complete rule entries and formatting.
- Upload a quality inspection sampling table containing structured tables, verify that the parsed table structure matches the original content.
- Upload a single ETL log file, confirm that parsing duration does not exceed the
PARSE_FILE_TIMEOUT_SECONDSconfiguration value, with no timeout errors. - Adjust
chunkSizeto different character ranges, test recall results of cleaning rule matching, confirm that the chunk granularity fits business requirements.
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.