What data for this use case looks like
Data for this use case originates from incident timelines, operational logs, monitoring metrics, and code commit records. Updates occur after each major incident or once per month. Commonly processed documents include RCA reports, incident tickets, post-incident review minutes, and rectification tracking forms. Document structures typically include fields such as incident occurrence time, root cause analysis conclusions, rectification tracking items, and associated monitoring data snippets. No unified fixed format exists. Some documents embed log screenshots or code snippets. Data volume fluctuates in line with incident occurrence frequency.
What constraints do these characteristics impose on document parsing and chunking
Data sources are scattered and document formats are inconsistent, so the parsing process must support multiple attachment types and unstructured text. Update frequency is low, but individual documents have high content relevance. Chunking must retain contextual associations to support root cause correlation analysis. Documents such as rectification tracking forms include clear responsibility fields and time nodes, so chunking must avoid splitting key associated information. The number of batch-processed post-incident review documents fluctuates with incident frequency. The system must adapt to sudden large-file parsing demands while preventing task interruptions caused by parsing timeouts that delay compliance review progress.
How to Configure Settings
In FastGPT v4.17.0, the following configurations can be completed through the system settings' document parsing module:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of associated text such as root cause descriptions and rectification items in RCA reports, avoiding splitting critical logic |
chunkSplitter | Paragraph-first | Matches the natural paragraph logic of post-incident review minutes and incident tickets, retaining contextual associations |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to parsing time for large batch log documents and review reports with embedded screenshots |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports uploading incident review packages containing multiple log attachments in a single operation |
customPdfParse | Enabled | Supports parsing of compliance review PDF documents with embedded charts and screenshots |
chunkSettingMode | Manual configuration | Adjust chunking parameters for different document types such as rectification tracking forms and RCA reports |
Three Common Misconfigurations
- Parsing interrupts after uploading a large batch of documents, and parsing cannot resume after container restart. This occurs when a reasonable duration for
PARSE_FILE_TIMEOUT_SECONDSis not configured, causing the process to exit abnormally after large-file parsing times out without retaining parsing breakpoints. - No parsing progress is displayed when uploading a file via the chat interface, and no errors appear in the backend. This occurs when
customPdfParseis not enabled, preventing correct identification and parsing of compliance documents with complex embedded formats. - Setting
chunkSizetoo small splits the context required for root cause analysis. This occurs when chunking parameters are not adjusted based on the text length of RCA reports, breaking the complete logic of root cause descriptions.
How to Verify Proper Configuration
- Upload a single 100 MB RCA report PDF, and confirm that parsing completes within 600 seconds with no interrupt errors.
- Upload a post-incident review minute containing multiple log segments, and check that chunking results retain complete incident timeline paragraphs.
- Call the knowledge base addition API to verify that chunked content falls within the preset
chunkSizerange. - Check that the
UPLOAD_FILE_MAX_SIZEconfiguration matches the current maximum allowed document size to avoid uploads being blocked due to exceeding limits.
The values provided are common starting points and should be evaluated against samples from the specific deployment.
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.