What the Data for This Category Looks Like
Typical data sources for this category include special equipment safety supervision systems, inspection and testing institution systems, user ledgers, and operator certificate databases. Daily documents include use registration certificates, periodic inspection reports, maintenance records, hidden danger rectification notices, accident reports, operator certificates, and more. These documents include structured fields and unstructured text. Structured fields include equipment numbers, inspection validity periods, operator certificate numbers, and other items. Unstructured text includes rectification descriptions, accident processes, and other content. Data updates occur monthly.
What Constraints These Characteristics Impose on Document Parsing and Chunking
Multi-source data has inconsistent formats. Single structured operator certificates exist alongside long text reports with hierarchical headings and tabular inspection data. Batch documents updated monthly must support periodic import workflows. If chunking logic does not match document structure, the link between equipment numbers and inspection periods will be broken. This prevents subsequent retrieval from connecting key business information. Improper splitting of long text reports and tabular documents will lose business context. This reduces retrieval accuracy for subsequent office scenarios including hidden danger rectification and qualification verification. Large monthly ledger files imported in batches require reasonable parsing timeout and upload limits. This avoids task interruptions.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Ensures semantic integrity for long texts such as special equipment inspection reports and accident reports. Prevents breaking key associated content like inspection periods and equipment numbers |
chunkSplitter | Split by paragraph + heading | Matches the document structure of maintenance records, hidden danger rectification notices, and other documents with hierarchical headings. Retains business logic context |
customPdfParse | Enable table recognition mode | Adapts to equipment parameter tables in periodic inspection reports. Avoids chaotic table content and misaligned rows and columns after parsing |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Covers parsing time for batch imports of multiple large inspection reports. Prevents timeout interruptions for single parsing tasks |
UPLOAD_FILE_MAX_SIZE | 100 MB | Adapts to the maximum file size of single annual maintenance ledgers. Meets batch monthly document import requirements |
Three Common Mistakes
- Phenomenon: The error
Cannot redefine property: toString at Object.definePropertyappears in the production environment. Cause: A custom parsing script conflicts with FastGPT's built-in parsing logic. Variable scopes are not properly isolated. - Phenomenon: The number of chunks after knowledge base document splitting exceeds 3000. Cause: A reasonable
chunkSizeparameter is not configured, or the heading truncation logic forchunkSplitteris not enabled. This causes excessive splitting of long documents. - Phenomenon: CSV file chunking fails after a platform version upgrade. Cause: CSV parsing rules from the old version are incompatible with the new version's field mapping logic. The CSV parsing mode in
customPdfParseis not reconfigured.
How to Confirm the Configuration Is Correct
- Upload a single periodic inspection report. Review the parsed chunk list. Confirm the chunk count falls within a reasonable range, with no obvious breaks in business content.
- Import a batch of monthly maintenance records. Check the completion status of parsing tasks. No timeout error records should be present.
- Upload a use registration certificate PDF that includes a table. Confirm the parsed text retains the table's row and column structure, with no disordered content.
- Adjust the
chunkSizeparameter. Compare the chunk count of the same document to verify the parameter takes effect.
The values provided are common starting points. They 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 2026-09-28.