What This Category of Data Looks Like
Daily documents for this category include cluster change tickets, namespace quota tables, deployment records, and incident reports. Data originates from Kubernetes API, image registries, monitoring systems, and CI pipelines. Update frequency is daily or after each cluster change. Document structure combines structured fields and text descriptions. Fields include change time, impact scope, resource quota values, and abnormal node IDs. Resource-related fields use units such as cores and GiB. Incident reports include error log snippets and troubleshooting steps.
Constraints on Document Parsing and Chunking
This category has a high proportion of structured fields with clear units. Parsing processes must avoid splitting numerical values from their associated units, otherwise resource quota judgments will be incorrect during compliance reviews. Short text log snippets make up a large share of content. Chunking processes must avoid truncating complete error messages or troubleshooting steps. Frequently updated documents require low-latency parsing processes to match real-time compliance review requirements for cluster changes. Documents from multiple sources have significant format differences. Unified parsing rules must be implemented to adapt to content with different structures, and avoid missing key approval fields.
Configuration Settings
The following configurations are formulated based on the parameter rules of FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the mixed length of structured fields and short logs in this category of documents, and avoids truncating critical information |
chunkSplitter | By paragraph + semantic boundary | Adapts to the paragraph structure of cluster change tickets and incident reports, and preserves contextual associations |
PARSE_FILE_TIMEOUT_SECONDS | 60–120 seconds | Matches the parsing speed requirements for frequently updated documents, and avoids parsing failures due to timeouts |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to the maximum file size limit for batch importing cluster change records and incident report archives |
chunkSettingMode | Manual configuration | Adjust chunking parameters for different document types (quota tables/change tickets) to meet the accuracy requirements of compliance reviews |
customPdfParse | Disabled | Most documents in this category are structured text or log formats, no need for custom PDF parsing rules, which reduces parsing overhead |
Three Common Mistakes
- Phenomenon: Boolean fields parsed from the Kubernetes API become empty after subsequent processing. Cause: No type mapping rules for structured fields are configured, so the original field type is not preserved during parsing, triggering a type conversion exception.
- Phenomenon: A "Cannot redefine property: toString" error is thrown when parsing monitoring logs. Cause: Custom parsing rules incorrectly modify the prototype properties of built-in objects, triggering system security check interception.
- Phenomenon: After importing batch cluster change records, only some documents are successfully indexed. Cause: The number of chunks for a single document exceeds the system default limit, and the configuration item for automatic chunk splitting is not enabled.
How to Verify Proper Configuration
- Upload a typical namespace quota table, and check whether the parsed fields cover the core content required by the business.
- Trigger the parsing process for an incident report, confirm that the parsing time does not exceed the preset timeout threshold, which is calibrated according to the business scenario.
- Import a single document matching the category characteristics, check that the chunked content does not truncate critical contextual associations, and that chunking rules are adjusted according to document type.
- View system operation logs, and confirm that there are no abnormal errors caused by custom parsing.
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.