Reference only; verify against the target FastGPT release and deployment documentation.
What data for this category looks like
The data for this category primarily comes from the national basic catalog of government service items, administrative licensing item lists, power responsibility lists, and local government service item databases. The typical update cycle is once per month. Daily processed documents include basic government service item catalogs, implementation lists, service handling guides, standard material directories, flowcharts, and frequently asked questions. Most document structures have fixed fields such as item code, implementing entity, acceptance conditions, application materials, and handling procedures. Some documents contain multi-level nested directories or embedded doc-format attachments and images.
What constraints these characteristics impose on the document parsing and chunking step
Data sources mostly use official standardized templates, but format variations exist across regions. The monthly batch update requirement means the parsing process must support batch adaptation. Documents contain multi-level directories, nested doc attachments, and images. Multi-format parsing and retention of hierarchical structure must be supported. Documents include fixed fields. Field associations must be preserved during chunking to avoid splitting that breaks item logic. Some documents have complete procedure descriptions spanning multiple pages. Chunking must maintain contextual coherence to prevent damage to semantic integrity for party building learning Q&A.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the field and paragraph lengths of government service documents, avoiding splits that break item logic |
chunkSplitter | Split by headings and paragraphs | Matches the structure of multi-level directories and fixed fields in documents, preserving semantic coherence |
customPdfParse | Enabled | Supports parsing nested doc attachments and embedded images, adapting to multi-format content in service handling guides |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Covers large-volume catalog files and multi-attachment packaged documents during batch updates |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to processing time for multiple documents during batch parsing, preventing early timeout interruptions |
chunkSettingMode | Manual configuration | Flexibly adjusts chunking rules to accommodate format differences in government documents across regions |
Three common configuration errors
- A 413 error occurs when uploading files. The cause is failure to adjust the
UPLOAD_FILE_MAX_SIZEparameter to a value suitable for batch documents, resulting in large-volume files being blocked. - Not all content within Feishu multi-level directories can be parsed and retrieved. The cause is failure to enable the multi-level directory adaptation mode for
chunkSplitter, only top-level directory content is captured. - No parsing results are returned after calling the API to upload a document. The cause is failure to configure a reasonable duration for
PARSE_FILE_TIMEOUT_SECONDS, with empty results returned early when batch parsing has not completed.
How to confirm correct configuration
- Upload a single service handling guide document under 1000 MB, check that parsing completes normally without 413 errors.
- Import a test directory file containing multi-level directories and doc attachments, confirm that the parsing result includes all hierarchical content and attachment text.
- Adjust
chunkSizeto the 800–1200 character range, retrieve split chunk content, confirm that unreasonable splits across fields do not occur. - Call the API to upload a test document, wait no longer than 600 seconds, then check that parsing results are returned completely with no empty results.
The values listed here are common starting points and should be evaluated against the user’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.