Data Profile for This Category
This category serves security operation centers, emergency response engineers, and customer security leads. Daily processed documents include emergency response reports, forensic records, response plans, and notification materials. Data sources include SIEM, EDR, firewall logs, and host images. Update frequency is real-time or generated per single incident. Document structures mostly mix semi-structured and unstructured content, with fields such as incident ID, timestamp, host IP, response actions, and evidence file hash. Log data is arranged in time series. Report data includes clear section headings and operational steps.
Constraints on Document Parsing and Chunking
Data sources include both structured logs and unstructured reports. The parsing process must adapt to plain text logs and formatted document content. Real-time updated log data requires low-latency parsing workflows. Long chunks will cause retrieval delays. The core value of emergency documents depends on evidence chain integrity. Chunking operations must not destroy the logical relevance of a single incident. Lost logical relevance will harm the accuracy of attack path restoration and regulatory reports. Large host image files have significant size. The parsing process must balance completeness and time control. Unreasonable chunking rules may cause key evidence loss.
Configuration Settings
This configuration is based on FastGPT v4.17.0. Specific configuration items and recommendations are listed below:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Emergency documents contain long paragraphs of response steps and traceability details. This range preserves complete single-step logic, and adapts to search recall accuracy |
chunkSplitter | Split by paragraph + heading | Emergency documents have clear section headings and time nodes. Splitting this way preserves incident logical relevance |
customPdfParse | Enabled | Forensic records and notification materials mostly use PDF format. Enabling this preserves text hierarchy and table structures |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large host image files takes significant time. This duration covers the full parsing process |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Covers upload requirements for single large host image files, and avoids interception due to oversized files |
chunkSettingMode | Custom mode | Chunking rules require adjustment for different document types (logs/reports). Custom mode supports flexible configuration |
Three Common Configuration Errors
- After uploading PDF-format forensic records, no results appear in knowledge base searches, and parsing errors are displayed. Cause: The
customPdfParseconfiguration is not enabled. Tables and embedded text in the PDF cannot be fully extracted, and key fields are missing from chunked content. - An overly small chunk length splits a single response step across multiple chunks. Cause: The
chunkSizevalue is not adjusted for the long paragraph characteristics of emergency documents. Using only the default short chunk length destroys response logic integrity. - Parsing large Excel-format firewall logs times out and fails. Cause:
PARSE_FILE_TIMEOUT_SECONDSis not set to a sufficient duration. The default timeout duration cannot complete parsing and chunking of large log files.
How to Confirm Configurations Are Correct
- Upload a single PDF-format emergency response report. Enter the knowledge base search test interface. Confirm returned results include the report’s response steps and attack traceability content.
- Upload multiple documents in different formats, such as Word forensic records and Excel firewall logs. Check that all parsing statuses show completed, with no error prompts.
- Adjust the
chunkSizeconfiguration. Compare chunked results from the same document. Confirm long paragraphs are not forcibly split. - Upload a host image file larger than 100 MB. Wait for parsing to complete. Check for no timeout-related errors.
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.