What the data for this category looks like
Daily documents for this category include basic government service item catalogs, implementation lists, service guidelines, and standard material catalogs. Data sources include the national basic government service item catalog, administrative licensing item list, and local government service item databases. Updates occur once per month. Most documents have standardized fields such as item name, acceptance conditions, application material list, and service process. Some documents must balance national unified templates and local custom formats.
What constraints these characteristics impose on the document parsing and chunking link
Documents updated in batches each month have a large overall scale, and field integrity must be preserved. The parsing link must support batch processing and structured retention. Cross-level format differences require parsing configurations to have custom adaptation capabilities. This prevents field loss caused by inconsistent templates. The association between standardized fields must be retained during chunking. Otherwise, data fragmentation will appear in subsequent verification and comparison links. Most documents are in PDF and Excel formats, so parsing rules must adapt to different file types.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Government documents contain both short fields and long paragraphs. This range avoids splitting critical information while controlling the context length of individual chunks |
chunkSplitter | Split by heading hierarchy + paragraph | Government documents have clear item-level headings. Splitting this way preserves the complete logic of individual items |
customPdfParse | Enabled | Adapts to the fixed-format headings, tables, and field layouts of government documents, retaining structured parsing results |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports batch import of multiple item list PDFs and Excel collections, avoiding interception due to oversized files |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Matches the time requirements of large-file or batch parsing, avoiding errors where the parsing service succeeds but the FastGPT platform times out |
chunkSettingMode | Manual mode | Allows adjustment of chunking rules for government documents of different formats, adapting to cross-level format differences |
Three common mistakes
- Symptom: Timeout occurs when enabling
customPdfParsefor private deployment. The parsing service returns success but the FastGPT platform reports an error. Cause:PARSE_FILE_TIMEOUT_SECONDSis set too short, failing to match the parsing time required for large government files. - Symptom: After uploading an Excel-format item list, the parsed content is empty or cannot read specified columns. Cause: The parsing configuration for Excel files is not adapted, or
UPLOAD_FILE_MAX_SIZEis set lower than the actual size of the uploaded file. - Symptom: After parsing is complete, the model cannot answer service-related questions based on the document content. Cause:
chunkSizeis set improperly, and the association information of core fields such as acceptance conditions and application materials is lost during splitting.
How to confirm the configuration is correct
Reference only; verify against the target FastGPT release and deployment documentation.
- Upload a single standardized service guideline PDF, and check if the parsed text fully retains core fields such as item name, application material list, and acceptance conditions.
- Upload a batch collection of government documents, verify that there are no timeout errors during parsing, and that all files have completed parsing.
- Import an Excel-format item list, confirm that the parsing result can extract the content of preset columns such as item code and implementation department.
- Launch a question-and-answer test based on the parsed documents, confirm that the model can reference specific content within the documents to respond.
The values listed here 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 2026-09-28.