What Data for This Category Looks Like
Data sources for this category include cloud billing statements, monitoring systems, business forecasting tools, and stress test reports. Update frequency is mostly weekly or monthly. Typical documents include capacity planning reports, scaling application forms, resource utilization reports, and budget calculation sheets, with structures that mix structured tables, numerical metrics, and text analysis content. Fields typically include resource type, usage amount, predicted peak value, budget amount, and more, with corresponding units involving compute cores, storage capacity, monetary amounts, and other relevant units.
What Constraints These Characteristics Impose on the Document Parsing and Chunking Link
The mixed nature of data sources requires the parsing process to adapt to both structured reports and unstructured analysis content, to avoid missing key metrics. The weekly or monthly update frequency means high-frequency parsing is not required, and chunking does not need to account for real-time synchronization requirements. Numerical values and their associated descriptions in documents are often scattered across continuous paragraphs; chunking must retain logical connections, and avoid splitting content that contains complete resource analysis. The presence of specific fields and units requires parsed data to retain the original format, to avoid separating units from their associated numerical values.
How to Set Configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Capacity planning documents mostly contain mixed numerical and text content. This length can retain complete information for a single set of resource forecasts, and avoid splitting key metrics |
chunkSplitter | Split by paragraph + semantics | Documents contain structured reports and unstructured analysis content; semantic splitting can retain logical connections |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Large stress test reports and monthly cloud billing reports take longer to parse. This range covers conventional parsing requirements and avoids mid-process interruptions |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Monthly cloud billing summary files may be large in size. This upper limit covers conventional batch upload requirements |
customPdfParse | Enabled | Capacity planning documents mostly contain custom tables and charts; custom parsing can adapt to non-standard formats |
chunkSettingMode | Manual configuration | The structure of different types of capacity planning documents varies significantly. Manual configuration can accurately adapt to business requirements |
Three Common Mistakes
- Phenomenon: Document parsing API calls fail or return empty results due to incorrect image parameter passing. Cause: Image parameter format and transmission method were not configured correctly per the official API documentation.
- Phenomenon: Key resource metrics are split in post-parsing chunked content. Cause: The
chunkSizesetting is too small, cutting off a text paragraph that contains complete analysis content. - Phenomenon: A timeout error is triggered when batch uploading large capacity planning reports. Cause: The configured value of
PARSE_FILE_TIMEOUT_SECONDSis lower than the actual time required for parsing.
How to Confirm the Configuration Is Correct
- For FastGPT v4.17.0, upload a typical monthly capacity planning report, view the post-parsing chunk list, and confirm that each chunk contains complete resource analysis or forecast content.
- Call the document parsing API, check whether the returned chunked data includes core fields from the document, such as resource type, usage amount, predicted peak value, and more.
- Upload a single test file with a size close to the configured
UPLOAD_FILE_MAX_SIZEvalue, confirm that the parsing process does not trigger upload restrictions or timeout errors. - After enabling the
customPdfParseconfiguration, upload a scaling application document containing custom tables, confirm that the parsed table structure does not appear disordered.
The values provided are common starting points and should be evaluated against the user’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.