Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Bodies such as audit and reform offices and government service data administration bureaus typically maintain this data. Data sources include the national basic catalog of government service items, administrative licensing item list, and local government service item databases. Daily documents include basic government service item catalogs, implementation lists, service guides, standard material directories, flowcharts, and frequently asked questions. Most documents are tabular documents with structured headers or long text documents, with an update frequency of once per month.
Constraints for Document Parsing and Chunking
Mixed structured tables and long text documents require preserving the correspondence between fields and content during parsing, to avoid splitting that breaks core associated information such as item acceptance conditions and application materials. Monthly updates and large single-document scale require chunks to not be too large, to prevent exceeding model context limits, while adapting to timeout and file size constraints for batch parsing. Government service items have many closely related fields, so chunks need to retain contextual association between adjacent items to avoid caliber deviation during classification matching.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of a single core piece of government service item information, avoids splitting that breaks item association logic, and controls single-chunk token consumption |
chunkSplitter | Split by paragraphs and header groups | Targets mixed structured table and long text documents, retains the binding relationship between headers and corresponding row data, and avoids field misalignment after splitting |
customPdfParse | Enable structured parsing mode | Adapts to the multi-format layout of government service guides, accurately extracts fields and table content, and reduces post-parsing information loss |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to document scales of up to 100,000 Chinese characters or 15,000 rows of Excel, avoids blocking valid data during upload |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing time of batch government documents, avoids forced interruption of the parsing process due to timeout |
chunkSettingMode | Manual custom configuration | Targets the structured characteristics of government service items, flexibly adjusts chunking rules, and adapts to different types of item documents |
Three Common Misconfigurations
- When parsing Excel documents, an incomplete request prompt is returned. The cause is failure to adjust chunking rules for structured tables, with single chunk data exceeding the model's processing range.
- Large-volume document parsing times out. The cause is failure to adjust the
PARSE_FILE_TIMEOUT_SECONDSparameter to a duration suitable for batch government documents, leading to forced interruption of the parsing process. - Caliber deviation occurs in knowledge base classification matching. The cause is failure to retain the contextual association between items and their corresponding acceptance conditions during chunking, resulting in disconnection between fields and content after splitting.
How to Verify Correct Configuration
- Upload a single Excel document with 15,000 rows, check that the upload is not blocked, confirming that the
UPLOAD_FILE_MAX_SIZEconfiguration takes effect. - Parse a single service guide document with 100,000 Chinese characters, check that the number of post-parsing chunks and single-chunk length conform to the preset
chunkSizerange. - Test the structured table parsing result, confirm that headers are fully bound to corresponding row data, with no field misalignment.
- Launch a classification matching test, check that the matching results do not show inconsistent item calibers, confirming that the chunking rules are suitable for business data.
The values provided are common starting points and should be measured 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.