What the Data for This Category Looks Like
The core documents for this category include capacity planning reports, expansion application forms, resource utilization reports, and budget calculation sheets. Data is sourced from cloud bills, monitoring systems, business forecasting models, and stress test reports. The update cycle is weekly or monthly. Documents typically include fields such as resource utilization values, peak load thresholds, and procurement budget details. Units include CPU cores, memory in GB, storage in TB, and currency units. Most content structures are split by business module, with attached historical monitoring curves and forecast data.
What Constraints Do These Characteristics Impose on Document Parsing and Chunking
Documents split by business module and budget item require chunks to retain content relevance. Do not split resource thresholds and explanatory text from the same paragraph. Differences in data formats across multiple sources require the parsing process to support multiple formats, including PDF reports and spreadsheet documents. The low update frequency and highly rigorous content mean chunks must not lose critical data such as peak forecasts and budget details. Loss of this data will disrupt subsequent resource configuration decisions. Content tied to fields and units must be parsed without separating numerical values from their corresponding units.
How to Configure Settings
The following configurations are based on FastGPT v4.17.0, and are adapted to the document parsing requirements of capacity planning and resource scaling scenarios:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the average length of business module paragraphs in capacity planning documents, avoids splitting resource thresholds and their associated explanations |
chunkSplitter | Split by heading | Adapts to the document structure split by business module and budget item, retains content relevance |
customPdfParse | Enabled | Supports PDF formats such as stress test reports and monitoring reports, extracts tables and chart-associated text |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Accommodates single quarterly capacity planning reports and attached stress test data files |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to parsing time for large-volume reports, prevents parsing interruptions due to timeout |
chunkSettingMode | Manual configuration | Adjusts chunking rules for different document types such as budget sheets and monitoring reports to meet field association requirements |
Three Common Mistakes
- Parsing documents linked to image addresses returns empty results. The cause is that parsing rules adapted to external image links are not configured, so external image document content cannot be pulled and parsed.
- Enabling the enhanced PDF parsing feature fails to call the local MinerU service. The cause is that the local API address and port of MinerU are not specified in the FastGPT configuration, breaking the service call chain.
- Resource utilization values and their corresponding thresholds are lost after chunking. The cause is that
chunkSizeis set too small, splitting values and explanatory text from the same paragraph into different chunks and destroying content relevance.
How to Verify Configuration is Complete
- Upload a single typical capacity planning PDF document, check the content integrity of parsed chunks, confirm that business modules and budget details are not split.
- Review system logs to confirm no timeout errors occurred during parsing, verify that the
PARSE_FILE_TIMEOUT_SECONDSconfiguration matches the current document size. - Enable the enhanced PDF parsing feature, run a connectivity test for the local MinerU service, confirm that parsing requests are properly forwarded to the locally deployed MinerU instance.
- Upload a test file exceeding the default threshold, check if upload is blocked, verify that the
UPLOAD_FILE_MAX_SIZEconfiguration takes effect.
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.