What the data for this category looks like
This category of data primarily comes from CDN consoles, DNS resolution records, public network access logs, and edge node monitoring alerts. Daily produced documents include domain name access forms, cache rule configuration tables, refresh and preheating operation records, and fault analysis reports. Covered fields include domain names, cache rule priority, node IP addresses, request peak bandwidth, and more. Update frequency is daily or real-time as configuration changes are made.
What constraints do these characteristics impose on document parsing and chunking
Document structures for this category vary widely. There are structured domain name access forms and cache rule tables, as well as unstructured fault analysis reports. This requires document parsing and chunking to adapt to multi-format inputs. Data updates occur at high frequency and trigger in real time when configurations change, so the parsing process must support rapid response to updated documents. Additionally, data sources include structured files exported from consoles and semi-structured text from access logs. Chunking logic must balance field extraction and natural semantic segmentation to avoid breaking configuration associations. A single document may also contain hundreds of node monitoring records, so parsing boundaries for large-volume files must be supported.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the semantic length of cache rules and fault descriptions in CDN documents, avoiding chunking that breaks configuration associations |
chunkSplitter | Split by paragraph + title | Adapts to structured cache rule tables and unstructured fault reports, preserving document hierarchy logic |
customPdfParse | Enabled | Adapts to PDF format configuration reports and fault analysis documents exported from CDN consoles, improving parsing accuracy |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Covers large-volume document scenarios such as multi-page access log PDFs and batch domain name access forms |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Prevents parsing timeouts for large-volume documents, matching the time expectations for CDN log batch processing |
chunkSettingMode | Manual mode | Allows separate adjustment of chunking parameters for structured configuration tables and unstructured reports, adapting to multiple document types |
Three common mistakes
- JSON parsing errors occur when calling the parsing API. The cause is incorrect configuration of the
customPdfParseparameter, which prevents non-standard PDF format files exported from CDN consoles from being parsed normally. - The file parsing function fails in locally deployed FastGPT v4.17.0. The cause is failure to enable the dependency service corresponding to
customPdfParse, or theUPLOAD_FILE_MAX_SIZEsetting being smaller than the size of the document to be parsed. - Timeout errors occur when parsing multi-page CDN access logs. The cause is
PARSE_FILE_TIMEOUT_SECONDSbeing set too low, failing to match the parsing time required for large-volume documents, and no queuing mechanism being configured to handle concurrent parsing requests.
How to verify a successful configuration
- Upload a typical CDN cache rule PDF document, check the parsed chunking results, and confirm that chunks do not damage the integrity of core fields such as cache priority and domain names.
- Adjust
PARSE_FILE_TIMEOUT_SECONDS, then upload a simulated access log file exceeding the preset threshold, and verify that the parsing process does not experience timeout interruptions. - Upload multiple CDN documents of different formats in batch, confirm that the
UPLOAD_FILE_MAX_SIZElimit does not trigger upload blocking, and that parsing results have no format loss. - View parsing logs in the FastGPT backend, confirm that the splitting logic of
chunkSplittermatches the title and paragraph structure of the document, with no abnormal chunking.
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.