What Data for This Category Looks Like
This content is written based on the functional logic of FastGPT v4.17.0. Data for this category originates from enterprise ITSM systems, phone or online chat logs, asset systems, and internal knowledge bases. Commonly processed documents include service request tickets, incident tickets, troubleshooting records, and user satisfaction feedback forms. Data updates follow a daily or real-time cadence. Each single document typically includes fields such as ticket number, submission time, problem description, processing flow, responsible person, and SLA time limit. Fields have strong inter-field correlation. There are no complex nested formats, but core business information must be fully retained.
What Constraints Do These Characteristics Place on the Document Parsing and Chunking Step
Multi-source heterogeneous data sources require the parsing module to support multiple file formats. It must adapt to different input types such as tickets, chat logs, and scanned documents. Real-time updated data sources require controlling parsing duration. This avoids delays that impact ticket processing efficiency. Ticket documents have strong inter-field correlation. Chunking must retain contextual connections. This prevents splitting from breaking the binding between problem descriptions and solutions. Single document lengths vary significantly. Flexible chunking parameters are needed to cover different scenarios. Key business fields such as SLA time limits and responsible person information must be fully retained in chunks. This avoids loss of core business content.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the typical length of tickets and processing records, retains contextual connections between problems and solutions |
chunkSplitter | Semantic chunking | Avoids breaking inter-field correlation of tickets via fixed-character splitting, ensures core business information is complete |
customPdfParse | Enable high-precision OCR mode | Adapts to scanned service request tickets or paper tickets converted to electronic files, improves text recognition accuracy |
UPLOAD_FILE_MAX_SIZE | 100 MB | Matches the maximum size of ticket attachments uploaded daily by enterprise IT service desks, avoids parsing failures due to oversized files |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapts to real-time requirements of multi-source batch parsing, avoids delaying ticket processing due to parsing timeout |
chunkSettingMode | Manual configuration | Supports adjusting chunking parameters for different types of ticket documents to adapt to inter-field correlation characteristics |
Three Common Misconfigurations
- Uploading Word or Excel format ticket attachments results in parsing failure, with an on-screen prompt indicating unsupported file format. This occurs because the parsing adaptation configuration for the corresponding format is not enabled, and common attachment formats of IT service desk tickets are not matched.
- Uploading PDF files related to tickets results in no search results, only an error prompt. This occurs because the high-precision mode of
customPdfParseis not enabled, orPARSE_FILE_TIMEOUT_SECONDSis set too short, causing parsing to not complete. - Enabling high-precision OCR results in an OCR Error alert. This occurs because the uploaded PDF file has too low resolution, or
UPLOAD_FILE_MAX_SIZEis set too small, causing the file to be truncated before parsing fails.
How to Confirm Proper Configuration Setup
- Upload a typical IT service desk ticket document, check if the parsed text content fully retains core fields such as ticket number, problem description, and processing flow.
- Run a batch parsing test, upload multiple different types of ticket documents, confirm that parsing time meets business real-time requirements, and no timeout errors occur.
- Trigger a knowledge base search test, enter key problem descriptions from the ticket, confirm that search results match the complete context of the corresponding document.
- Check the parsing module logs, confirm there are no unsupported format or OCR abnormal error records.
The values listed here are common starting points, and should be evaluated 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.