What data for this category looks like
It typically serves security operations, internal training teams, and external security service providers. Daily produced and used documents include training courseware, drill plans, phishing email templates, and drill result reports. Data sources include employee contact lists, email gateways, internal training platforms, and user click statistics systems. Document update cycles are mostly quarterly or annual. Some drill templates are temporarily adjusted to comply with regulatory requirements. Most document structures include structured fields such as drill audience scope and email sending time, and unstructured paragraphs such as training content and review summaries. Some templates include fixed-format statistical tables.
What constraints these characteristics impose on document parsing and chunking
Documents contain structured fields and unstructured paragraphs. Parsing logic must adapt to mixed content to avoid splitting that damages business-related structured information. Data formats vary widely across sources. Custom parsing rules must be supported to adapt to files in different export formats. Low update frequency means parsing cache must be set reasonably. The system must also support temporary re-parsing to handle emergency adjustments. Accurate chunking must match the granularity of business fields. This enables subsequent quick retrieval and use of drill audience screening and review data.
How to set configurations
This document adapts to the document parsing and chunking functions of FastGPT v4.17.0. Specific configurations are listed below:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the typical paragraph length of training courseware and drill plans, avoiding splitting that damages the integrity of business logic |
chunkSplitter | Split by paragraph + title | Adapts to document structures with title hierarchies, such as chapter divisions of drill plans and fixed fields of phishing email templates |
customPdfParse | Enabled | Processes fixed-format content in phishing email templates and drill reports, retaining structured fields and table information |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to the upload requirements of large training courseware and annual drill reports, calibrated based on actual testing |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Prevents timeout during large document parsing, matching the average processing duration of a single document |
chunkSettingMode | Manual mode | Allows adjustment of chunking parameters for different document types, such as email templates vs review reports |
Three common mistakes
- After uploading a phishing email template PDF, the data processing result is empty. The cause is that the
customPdfParseconfiguration is not enabled, resulting in failure to correctly extract structured template fields. - After uploading an annual drill report, the vectorization processing takes too long. The cause is that
chunkSizeis not adjusted to a reasonable range, and oversized chunks lead to excessive processing load per chunk. - When initiating a security consultation conversation, the question content is incorrectly included in the document parsing process. The cause is that
trainingTypeis not configured correctly, causing non-document inputs to trigger parsing logic.
How to confirm configurations are properly set
- Upload a single phishing email template document, view the parsed chunk results, and confirm that structured fields such as sender and subject are fully retained in a single chunk.
- Upload a large training courseware, wait for parsing to complete, check the task status, and confirm that no timeout-related errors are triggered.
- Initiate a non-document security consultation question, confirm that the conversation process does not trigger the document parsing link, and directly return a natural language response.
- Upload a drill report larger than the default size, confirm that the system prompts that the file size meets the configured requirements.
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.