What the data for this category looks like
Core data for this category comes from CI pipelines, change management systems, test reports, and monitoring dashboards, maintained by version managers and R&D and operations teams. Daily documents include release plans, launch checklists, acceptance reports, and rollback plans, mostly in structured or semi-structured formats. These contain fixed fields such as version number, launch time, and rollback trigger conditions. Data is updated weekly or per version release cycle, with each update synchronizing multiple associated documents. Overall volume accumulates gradually with version iterations.
What constraints do these characteristics impose on the "document parsing and chunking" link
The structured nature of category documents requires that chunking must not destroy the integrity of fixed fields, otherwise subsequent Q&A will fail to accurately match version information and rollback rules. The high-frequency update feature requires the parsing process to adapt to rapidly iterating document versions, avoiding parsing delays that affect release window coordination. The multi-source data aggregation feature requires chunking rules to be compatible with export files in different formats, such as CSV exported from CI, PDF generated by change management systems, etc. Large-volume full-version summary documents require the parsing process to have sufficient timeout fault tolerance and performance adaptation capabilities, avoiding task interruptions caused by oversized files.
How to set configurations
| Configuration Item | Recommended Value | Basis for This Setting |
|---|---|---|
chunkSize | 800–1200 characters | Most documents for this category are structured content with fixed fields. This length can cover a single release item or a single segment of acceptance logic, avoiding split breakage |
chunkSplitter | Split by newline + field identifier | Category documents have clear field separation rules, which can retain the integrity of core fields such as version number and launch time |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Parsing large-volume full-version launch checklists requires a long time. This value range can avoid task failure caused by timeout |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Documents for this category may include release packages summarizing multiple attachments, requiring support for uploading and parsing large files |
customPdfParse | Enabled | Most category documents are exported in PDF format. Custom parsing can retain the original table and field structure, avoiding format confusion |
Three common mistakes
- Phenomenon: After importing an Excel-format launch checklist, multiple lines of content are merged into one chunk and cannot be split by row. Cause:
chunkSplitteris not configured to adapt to the row separation rules of Excel tables, or table parsing mode is not enabled. - Phenomenon: Parsing a Word-format release plan larger than 10MB takes too long, or times out and fails directly. Cause:
PARSE_FILE_TIMEOUT_SECONDSis set too low, failing to adapt to the actual time required for large file parsing. - Phenomenon: After importing a rollback plan document, the complete description of rollback trigger conditions is split into multiple chunks. Cause:
chunkSizeis set too small, splitting a single complete logical description into two segments, leading to breakage of core information.
How to confirm that the configuration is correct
- Upload a single structured release plan document, check whether the chunking results retain complete core fields such as version number and launch time.
- Upload a test document of around 10MB, check whether the completion status of the parsing task complies with the preset timeout rules.
- Upload an Excel-format launch checklist, check whether each row corresponds to an independent chunk content.
- Check the knowledge base file type support list for FastGPT v4.17.0, confirm that it covers the PDF, Word, and Excel formats commonly used for this category.
The values given 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.