What the Category’s Data Looks Like
Core documents for this category include change request forms, risk assessment reports, launch window schedule plans, and rollback plan documents. Data sources include internal enterprise change management systems, CI pipelines, production monitoring data, and business calendars. Update frequency is weekly, or each time a change is executed. Most documents use mixed formats, containing structured fields such as change ID, window time period, and risk level, alongside unstructured text such as risk descriptions and rollback steps. Some documents include script snippets or configuration file snippets. Overall content has strong interconnections.
How These Characteristics Impact Document Parsing and Chunking
Since data sources include mixed-format documents from multiple systems, the parsing link must support mixed input of structured fields and unstructured text. Document update frequency is high. Single parsing tasks must control duration to match the approval rhythm of change processes, and avoid delaying change submissions. Some documents include script or configuration snippets. Chunking logic must avoid splitting related content into different chunks, which would disrupt subsequent risk correlation analysis. Key compliance-related content such as risk descriptions and rollback conditions must not be truncated by chunk boundaries. This ensures the compliance review link can fully obtain necessary information.
Configuration Settings
This guidance applies to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Change management documents mostly use mixed formats. This range balances the completeness of single-chunk content and subsequent retrieval efficiency |
chunkSplitter | Split by semantic segments | Change documents include semantically coherent content such as risk descriptions and step instructions. Splitting by semantic segments avoids damaging logical connections |
customPdfParse | Enabled | Most change documents are submitted in PDF format. Enabling custom parsing preserves the accuracy of structured field extraction |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Single change document parsing needs to cover multi-system associated data. 600 seconds adapts to medium-scale document parsing requirements |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Change documents may include batch configuration files or monitoring log snippets. This upper limit covers conventional submission scales |
chunkSettingMode | Manual configuration | Change documents have large differences in format and content. Manual configuration allows flexible adjustment of chunking rules to adapt to different scenarios |
Three Common Configuration Mistakes
- Uploading a Java interface file with a modified file extension results in no parsing output. The default parsing rules only recognize preset document formats. Files with modified extensions do not trigger the corresponding parsing logic.
- Using chunk mode to call the
pushdata apifor upload always shows indexing in progress. ThePARSE_FILE_TIMEOUT_SECONDSconfiguration value is too low, causing large change documents to enter the indexing stage before parsing is complete. - Monitoring screenshots embedded in PDF change documents are not extracted. The
customPdfParseconfiguration is not enabled. The default parsing rules ignore non-text embedded content.
How to Verify Proper Configuration
- Upload a typical change request form document, view the parsed chunked content, and confirm that core fields such as change ID and window time period are not truncated.
- Upload a test document at the preset volume upper limit, verify that no timeout errors occur during parsing tasks, and confirm that the
PARSE_FILE_TIMEOUT_SECONDSconfiguration meets actual requirements. - Call the parsing interface, view the returned chunk list, and confirm that related content such as script snippets and configuration descriptions is not split into different chunks.
- Upload a PDF change document containing embedded images, confirm that the parsing results include the associated information corresponding to the images, and meet the content requirements for compliance reviews.
The values provided are common starting points and should be evaluated against in-house sample data.
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.