What data in this category looks like
The data for this category primarily comes from database monitoring, audit logs, SQL execution plans, and ticketing systems. Daily processed documents include database change orders, slow SQL reports, backup records, and permission application forms. Most documents contain structured business fields and operation records, such as change order numbers, execution durations, instance IDs, and applied permission scopes. Update frequency is daily or triggered by each database change; some archived documents are aggregated in batches weekly or monthly.
What constraints do these characteristics impose on the document parsing and chunking link
The structured nature of database operations documents requires that chunking preserves the integrity of business fields, and avoids splitting content across multiple tickets into the same chunk. Document update frequency is high, so the parsing process must be able to adapt to the rhythm of batch task processing. Some documents include numerical fields with units, such as execution duration in seconds and backup capacity in MB. Parsing must retain the original format and unit information to avoid impacting subsequent retrieval and analysis. Additionally, different types of operations documents have significant structural differences: change orders focus on approval workflows, while slow SQL reports focus on time-series data. Chunking rules must be adjusted accordingly.
How to configure the settings
This configuration rule applies to FastGPT v4.17.0. The following are recommended configurations for the database operations scenario:
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the typical content length of a single change application or slow SQL analysis in database operations documents, to avoid splitting critical business fields |
chunkSplitter | Split by headings and line breaks | Adapts to the structure of database documents where change order numbers and SQL statement blocks act as independent units |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses the parsing needs of large-volume files such as batch backup records and slow SQL reports |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports uploading large archived database audit log files |
chunkSettingMode | Custom rule mode | Allows adjusting chunking logic for different document types such as permission application forms and change orders |
Three common misconfigurations
- Phenomenon: After uploading a database change order in Word format, clicking the referenced internal ticket link returns the error "Only support .txt, .m". Troubleshooting step: Verify that the parsing adaptation rules for non-text files are configured correctly.
- Phenomenon: When uploading a slow SQL report in Excel format to a knowledge base, the large model response does not include the execution duration chart from the report. Cause: By default, FastGPT only extracts plain text content when parsing non-text files, and no image parsing adaptation configuration is enabled.
- Phenomenon: After setting a custom chunk length, the parsed chunks still contain content across multiple tickets. Cause: The chunking rules were not configured with the title prefixes specific to database documents, such as change order numbers. Splitting only by fixed length results in stitching across business units.
How to confirm the configuration is correct
- The environment variable configuration in the App/Admin backend should be reviewed to confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare filled correctly, and that no duplicate configurations exist in the workflow, application API, or model management pages. - A single typical database document, such as a single change application form, should be uploaded, and the parsed chunk results reviewed to confirm that critical business fields are not split.
- The largest archived database audit log file should be uploaded, and parsing verified to ensure no timeout errors occur during the process.
- A batch document parsing task should be triggered, and all uploaded operations documents verified to have been split according to the configured chunking rules.
The values provided 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.