What the data for this category looks like
The data for this category mainly comes from WAF, IDS, firewalls, and threat intelligence platforms. Daily processed documents include rule change tickets, block logs, false positive analysis reports, and policy rollback records. Data updates follow a daily batch schedule or are triggered by events. Each single document typically contains fields such as rule identifiers, associated IP ranges, trigger thresholds, and change times. Some log documents arrange multiple independent records in time sequence.
What constraints these characteristics impose on the document parsing and chunking process
The mixed document types, event-driven update cadence, and structured field features of this category create multiple constraints for the document parsing and chunking process. Documents with mixed formats, including rule change tickets and block logs, require support for general parsing logic and structured field extraction. High-frequency updates on a daily or event-driven basis require the parsing link to have stable timeout handling capabilities. Log documents contain a large number of time-ordered records. Chunking must retain contextual association between adjacent records to avoid splitting rule-related information. Single documents with significant field differences require flexible adjustment of chunking parameters to adapt to varying content lengths.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the single-chunk content length of block logs and false positive reports, avoiding splitting rule-related fields |
chunkSplitter | Split by line breaks + semantic segmentation | Adapts to time-series records in log documents, retaining the integrity of single event records |
PARSE_FILE_TIMEOUT_SECONDS | 60–120 seconds | Addresses parsing time for batch log files, matching the processing cadence of daily updated data |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports upload of batch block log packages, adapting to full data imports for daily updates |
chunkSettingMode | Manual configuration | Adjust chunking parameters for different document types (change tickets, logs) to meet mixed scenario requirements |
customPdfParse | Disabled | Most documents for this category are structured logs and text reports, so custom PDF parsing logic is not needed |
Three Common Mistakes
- Phenomenon: Context reference areas display raw Markdown tags without formatted rendering. Cause: The parsing link did not enable format retention configuration, and unprocessed raw text was directly extracted during chunking.
- Phenomenon: Chunked content cannot be retrieved normally after switching vector models. Cause: Chunking did not retain the original document's field association information, or chunking parameters do not match the input length requirements of the new vector model.
- Phenomenon: Key rule ID fields are split into different chunks after chunking. Cause:
chunkSplitteris configured to split by fixed characters, and does not adapt to the natural boundaries of structured fields.
How to Confirm Proper Configuration
- Upload a single block log document, check if the chunked result retains complete rule ID and associated IP fields.
- Upload a batch log package, confirm that the upload progress and parsing time fall within the configuration range of
UPLOAD_FILE_MAX_SIZEandPARSE_FILE_TIMEOUT_SECONDS. - Switch the vector model, perform a retrieval test, and verify that the chunked content can be retrieved normally.
- Check if the format rendering of the context reference area meets expectations, and confirm that raw Markdown tags are not displayed.
The values given 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.