What this category of data looks like
This category of data comes from raw datasets, ETL logs, annotation platforms, and sampling results. Commonly processed documents include cleaning rules, quality inspection sampling tables, abnormal data lists, and delivery reports. Most documents are in structured or semi-structured formats, containing rule entries, sampling fields, anomaly markers, delivery metrics, and other content. Data is updated daily or per batch delivery task. New abnormal data lists and delivery reports are generated each day or per batch.
What constraints these characteristics impose on the "document parsing and chunking" link
Daily documents contain structured rule entries and tabular sampling results. This requires the document parsing process to accurately identify title, table, and paragraph boundaries, and avoid splitting critical information. Multi-source data input requires the parsing process to support raw data and log files in different formats. Data updated daily or per batch has a large volume. Chunking configurations must adapt to batch processing rhythms, and avoid parsing failures caused by overly large single chunks. Duplicate data identification and abnormal value processing are core time-consuming steps. Chunks must retain unique data identifiers and anomaly marker fields, to ensure subsequent cleaning and quality inspection processes can accurately locate target content.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of single segments of cleaning rules and abnormal lists, avoids splitting across critical rules or anomaly markers |
chunkSplitter | Split by paragraph + title | Matches the structured title hierarchy of daily documents, retains the table row structure of quality inspection sampling tables |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Adapts to daily batch-processed ETL logs and datasets, avoids timeouts caused by overly large single batch files |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports uploading entire batches of raw datasets or bulk files exported from annotation platforms in a single request |
customPdfParse | Enabled | Adapts to the PDF format of quality inspection sampling tables, retains the original typesetting of table and field alignment |
Three Common Mistakes
- A request error is prompted when uploading a PDF, or the file parsing content fails. The cause is that the
customPdfParseconfiguration is not enabled, or theUPLOAD_FILE_MAX_SIZEsetting is smaller than the actual size of the uploaded file. - Parsed tables appear misaligned. The cause is that the
chunkSplitteris not used to split by table separators, or thechunkSizesetting is too small, causing table content to be forcibly split and destroying the structure. - Timeouts occur during batch parsing of ETL logs. The cause is that the
PARSE_FILE_TIMEOUT_SECONDSsetting is lower than the parsing time of a single batch of logs, or files are not split by daily update batches.
How to Confirm the Configuration Is Properly Set
- Upload a single cleaning rule document, and check if the parsed chunks retain complete rule entries without forced splitting.
- Upload a quality inspection sampling table PDF containing tables, and check if table content is aligned without misalignment or missing content.
- Upload a single batch of ETL log files, and confirm that the parsing time does not exceed the duration set by
PARSE_FILE_TIMEOUT_SECONDS. - In the FastGPT v4.17.0 test environment, batch upload multiple daily documents, and confirm that all files are parsed completely without error prompts.
The values listed here are common starting points, and should be evaluated 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.