What this category of data looks like
Typical data for this category comes from the daily work output of operations and maintenance on-call teams. It includes four core document types: trouble tickets, event timelines, emergency response plans, and fault notifications. Data sources cover monitoring alerts, log platforms, APM tools, and user feedback channels. Updates occur in real time or after each incident. Documents include fields such as event ID, impact time, impacted service scope, fault mitigation operation steps, and notification recipients. Units are primarily time-based and quantity-based. No fixed unified format template exists, and adjustments are made dynamically based on the incident scenario.
What constraints these characteristics impose on document parsing and chunking
The real-time or event-triggered update rhythm requires the parsing link to have low latency, to avoid impacting fault response efficiency. The multi-source, unstructured document structure requires the parsing engine to have strong format adaptation capabilities, to avoid parsing failures caused by differences in document formats. The strong timeliness of incident documents requires that chunking does not destroy core logical connections. For example, front and back steps of an event timeline must not be split into different chunks. At the same time, documents in fault handling scenarios often contain operation step content. Chunking must retain the integrity of steps, to avoid impacting subsequent retrieval and execution reference.
How to Configure Settings
The following configurations are based on FastGPT v4.17.0, and are adapted to the document parsing needs of incident response and troubleshooting scenarios.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to documents with moderate single-item length such as trouble tickets and emergency response plans, avoids overly fragmented chunks that lose context, or overly long chunks that reduce retrieval accuracy |
chunkSplitter | Chunk by paragraph + semantic meaning | Adapts to documents with logical order such as event timelines and fault notifications, retains event connection logic |
customPdfParse | Enable structured parsing | Adapts to PDF documents such as emergency response plans and fault notifications, extracts text and metadata, and supports image link association |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to real-time updated incident documents, avoids timeouts during parsing of large log files or batch processing of multiple documents |
chunkSettingMode | Manual mode | Adapts to custom document formats in operations and maintenance scenarios, flexibly adjusts chunking rules to match the document structure of different incident types |
Three Common Configuration Mistakes
- Symptom: Some MCP service parsing fails, only Gaode MCP can be called normally. Cause:
customPdfParseis not enabled to adapt to the document format of non-standard MCP services, causing the parsing engine to fail to identify metadata of specific protocols. - Symptom: The knowledge base contains image links, but the Q&A test cannot return image content. Cause: The image parsing switch of
customPdfParseis not enabled, only text content is extracted without associating image metadata. - Symptom: After uploading a PDF-format fault instruction manual, the parsed fields are empty. Cause:
chunkSettingModeis not adjusted to adapt to the embedded metadata format of the PDF, causing the parsing engine to fail to extract key fields.
How to Verify Proper Configuration
- Upload a typical trouble ticket document, view the parsed chunk results, confirm that the chunk length matches the preset range.
- Submit an emergency response plan document containing image links, initiate a Q&A test, verify that image-related content can be correctly associated and returned.
- Upload a log file under 100 MB, confirm that the parsing process does not trigger a timeout error, and the generated chunk content is complete.
- Test the document parsing of non-standard MCP services, confirm that the parsing status is successful and no core fields are lost.
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.