What this category of data looks like
The administrative consultation and response data for advanced chemical materials primarily originates from internal enterprise Confluence knowledge bases, industry compliance documents, annual inspection reports, PDF annual reports, and Excel batch data. The data update rhythm varies by business scenario: project documents are updated irregularly alongside R&D progress, annual compliance reports are updated per the calendar year, and batch inspection data is updated in sync with production plans. Document formats include PDFs with professional parameter tables, Excel files with massive row datasets, and Word documents with process descriptions. Most fields contain performance parameters such as material grade, tensile strength, and heat deflection temperature, with supporting professional units including MPa and ℃.
What constraints do these characteristics impose on the document parsing and chunking stage
Intranet Confluence serves as the core internal data source. Parsing tools must support intranet environment access configuration; otherwise, internal workflow and compliance descriptions cannot be extracted. Long PDF annual reports and inspection reports can reach thousands of pages per copy, leading to extended parsing times. Parsing logic must accommodate large file processing. Excel files include headers and over 10,000 rows of batch data. Default chunking can break the association between parameters and units, reducing consultation matching accuracy. For document structures where professional parameters are bound to units, chunking must retain complete semantic units, avoiding splitting of professional content across fields.
How to configure the settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the paragraph density of advanced chemical material documents, retaining complete associations between individual performance parameters and their corresponding units |
chunkSplitter | Split by paragraph + filter headers and footers | Avoids splitting professional tables and parameter content across pages, adapting to long document structures |
customPdfParse | Enable OCR mode | Processes scanned inspection reports and non-editable PDF documents, fully extracting text and table content |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Supports uploading single copies of thousand-page PDF annual reports and large-capacity Excel files |
PARSE_FILE_TIMEOUT_SECONDS | 1200 seconds | Adapts to parsing times for long and large-capacity documents, preventing mid-parsing timeout interruptions |
chunkSettingMode | Manual configuration | Adjusts chunk granularity for Excel files with over 10,000 rows of data, preventing semantic breaks caused by overly large default chunk sizes |
Three common configuration mistakes
- Intranet Confluence pages fail to return parsed content. The cause is failure to configure an intranet access proxy or permissions, and failure to adjust parsing parameters for intranet resources.
- Thousand-page PDF annual reports trigger timeout errors or return empty results after parsing. The cause is
PARSE_FILE_TIMEOUT_SECONDSbeing set too short, and failure to enable long document optimization mode. - Excel file parsing results lose the association between headers and corresponding row data. The cause is failure to manually adjust the
chunkSizeparameter, using default chunking logic to split professional data.
How to confirm the configuration is successful
- Upload a single 500-page PDF annual report, check that the parsing status completes within a reasonable time frame, with no timeout-related errors.
- Upload an Excel file containing over 10,000 rows of data, verify that the chunking results retain complete binding between headers and corresponding rows, with no cases of parameter and unit splitting breaks.
- Test an intranet Confluence page link, confirm that complete workflow descriptions and compliance document content can be extracted after parsing.
- Adjust the
chunkSizeparameter, verify that chunked text fully contains individual professional performance parameters and their corresponding units, with no semantic splitting errors.
The values listed are common starting points and should be evaluated against the user’s own 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 2026-09-28.