What the data for this category looks like
This data is typically produced by project managers, implementation consultants, and customer archive administrators. Common types include completion documents, configuration manuals, training records, acceptance certificates, and similar materials. Data sources include project document libraries, configuration lists, meeting minutes, and test reports. Updates are triggered at the end of each project phase or upon full project completion. Most documents are a mix of structured and semi-structured content, containing fields such as project number, delivery milestone, version number, and signature status. There is no unified fixed format, and some documents include embedded tables and attachments.
What constraints do these characteristics impose on document parsing and chunking
Mixed-structured documents require adaptation to multi-type content parsing. When splitting embedded tables, cell association relationships must be preserved to avoid information misalignment after archiving. Data from multiple sources need unified metadata tagging to prevent information fragmentation after chunking, which disrupts subsequent archive verification. Phased updates require chunking logic to support incremental parsing, avoiding repeated processing of historical documents. Key fields such as signature status and version number must be accurately extracted. Otherwise, the acceptance process will fail. Additionally, document lengths vary widely, from a few pages for acceptance certificates to dozens of pages for configuration manuals. Chunking boundaries must be adjusted flexibly.
How to configure the settings
The following configurations apply to the document parsing and chunking functions of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the paragraph length of completion documents and configuration manuals, avoiding overloaded or overly fragmented single chunks |
chunkSplitter | Split by paragraph + heading hierarchy | Preserves document structure to facilitate chapter-based categorization during subsequent archiving |
customPdfParse | Enabled | Enables accurate parsing of embedded tables and signatures to meet the document integrity requirements of archiving |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Covers the upload requirements of large completion documents with dozens of pages, matching the size range of project documents |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Prevents parsing timeout for large configuration manuals, aligning with the parsing time patterns of project documents |
chunkSettingMode | Manual configuration | Allows adjustment of chunking rules for different document types to support unified processing of multi-source data |
Three common configuration errors
- A 413 Request Entity Too Large error occurs when uploading large completion documents. The cause is failure to adjust the
UPLOAD_FILE_MAX_SIZEconfiguration. The system default upper limit does not cover the size range of documents in this category. - Parsing of large configuration manuals times out before completion. The cause is that the
PARSE_FILE_TIMEOUT_SECONDSsetting is shorter than the actual time required for document parsing. - Only the first two columns of content are recognized when importing CSV-format acceptance certificate data. The cause is failure to adapt chunking rules for structured tables and enable the corresponding parsing mode.
How to confirm the configuration is correct
- Upload the largest single project document, check the upload progress and parsing status to confirm no 413 error is triggered.
- Start a PDF parsing task, wait for the duration specified in
PARSE_FILE_TIMEOUT_SECONDS, then check if the parsing result is complete with no mid-task interruption. - Import a CSV-format configuration list, check if the number of parsed data columns matches the source file.
- Adjust the
chunkSizeparameter, then parse a single training record document, check if the length of the chunked content meets expectations.
The values provided 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.