This document is compatible with FastGPT v4.17.0’s document parsing and chunking feature.
What Data for This Category Looks Like
Data for this category comes from monitoring systems, ticketing systems, formal service contracts, and customer communication records. The update cycle is monthly or quarterly. Commonly processed documents include standardized SLA clause text, monthly service summary reports, system availability statistical reports, and customer service review minutes. Document fields include service cycle range, service availability agreements, abnormal event records, customer request classification and corresponding processing results. Units are mostly natural time units and event counting units.
What Constraints Do These Characteristics Impose on Document Parsing and Chunking
The document structures of SLA and service reports vary significantly. Standardized clauses form long, coherent text structures. Monthly reports contain mixed content with tables, numerical statistics, and paragraph descriptions. Dispersed data sources require integrating heterogeneous files exported from multiple systems before parsing. Update frequency requirements mean the parsing process must adapt to batch processing of monthly archived files. SLA indicator fields in documents must be extracted accurately. Chunking must avoid breaking the association between clauses and corresponding values. It must also support parsing of statistical reports in different formats, which places high flexibility requirements on chunking rules.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of long paragraphs and nested tables in SLA clauses and service reports, avoiding breaking the association of key indicators |
chunkSplitter | Split by paragraph + semantic meaning | Adapts to mixed text structures, retaining complete semantic units of SLA clauses and statistical paragraphs in reports |
customPdfParse | Enabled | Adapts to tables and numerical statistical content in monthly service reports, improving the accuracy of heterogeneous document parsing |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Accommodates multi-document packaged upload scenarios for quarterly summaries, meeting batch archived file processing needs |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Adapts to parsing large statistical reports integrated from multiple systems, avoiding timeout failures due to complex content |
chunkSettingMode | Manual configuration | Flexibly adjust chunking rules and thresholds for different types of SLA documents and service reports |
Three Common Mistakes
- Uploading a PDF results in parsing failure, with a timeout status code returned in the interface. The cause is failure to adjust the
PARSE_FILE_TIMEOUT_SECONDSparameter. The parsing duration of large monthly service reports exceeds the default threshold. - Chunking results break the association between SLA clauses and corresponding compensation clauses. The cause is that
chunkSplitteris configured for fixed character splitting, without retaining semantic association units. - Some files fail to be parsed when uploading monthly reports in batches. The cause is that
UPLOAD_FILE_MAX_SIZEis set too small, exceeding the actual size of quarterly summary files.
How to Confirm Proper Configuration
- Upload a single standard SLA clause document. Verify that the chunking result retains the complete content of a single SLA clause, with no mid-way breaks.
- Upload a monthly service report containing tables. Confirm that the parsed text retains the association between numerical values in the table and corresponding explanatory paragraphs.
- Upload a packaged archived file within 1000 MB. Confirm that all files are identified and complete the parsing process.
- Adjust
PARSE_FILE_TIMEOUT_SECONDSto 600 seconds. Upload a large statistical report. Confirm that the parsing process has no timeout errors.
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.