What this category of data looks like
This use case typically serves regulatory departments including market supervision, housing and urban-rural development, ecological environment, cultural and tourism, human resources and social security, and others. Common daily processed documents include inspection plans, inspection target lists, inspection item checklists, on-site inspection records, rectification notices, and inspection result announcements. Data is primarily sourced from random inspection and public disclosure regulatory platforms, enterprise credit information disclosure systems, industry regulatory systems, and law enforcement officer directories. Update frequency is monthly. Most documents contain structured fields such as unified social credit codes, inspection items, and law enforcement officer identification numbers. Some on-site records are semi-structured entered text.
Constraints on document parsing and chunking from these characteristics
The monthly batch update feature creates pressure from large single-parsing data volumes. Structured list documents have fixed headers and large quantities of row data, which may cause issues where chunking breaks field associations across headers. Semi-structured on-site record text has significant fluctuations in length; overly long single records may exceed the input length limit of vector models. Data formats from multiple sources are inconsistent, including Excel spreadsheets and Word documents, requiring adaptation to different parsing logic to avoid field misalignment or content truncation. Additionally, regulatory data has high accuracy requirements; chunking must retain the association between inspection targets and inspection items without disrupting business logic.
Configuration settings
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the input length limits of most vector models, while covering the core content of a single inspection record or single-page announcement document, and avoiding chunking that breaks the association between inspection targets and items |
chunkSplitter | Split by header + semantic segmentation | For structured lists in Excel and inspection records in Word, first split data blocks by header rows, then segment long text according to semantic logic to retain business associations |
customPdfParse | Enable structured parsing mode | For PDF format documents such as rectification notices and announcement documents, extract table and paragraph structures from text to avoid losing field alignment information from plain text parsing |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to large-volume data from monthly batch updates, avoiding task interruption due to timeout during single parsing |
chunkSettingMode | Manual configuration | Adjust chunking parameters separately for different source document types to adapt to Excel row data and Word long text scenarios |
Three common mistakes
- When parsing an Excel inspection target list with over 15,000 rows, chunked content crosses headers, making it impossible to associate inspection targets and inspection items during subsequent matching. Cause: Did not enable header-based chunking mode; the default fixed character length chunking disrupts field associations for structured data.
- When uploading a Word on-site inspection record containing 100,000 Chinese characters in a single operation, the parsing task times out and fails, returning a timeout status code. Cause: Did not adjust the
PARSE_FILE_TIMEOUT_SECONDSparameter; the default timeout duration is insufficient for processing long text parsing. - The generated knowledge base chunked content contains redundant header comments or blank lines, leading to irrelevant results during vector matching. Cause: Did not enable structured filtering for
customPdfParse, retaining non-business meaningful format content from documents.
How to confirm proper configuration
- Upload a single Excel inspection target list with fewer than 1,000 rows, check whether chunked results associate each row of data with the corresponding header, with no cross-block field misalignment.
- Upload a single Word on-site inspection record containing 50,000 Chinese characters, confirm that the parsing task does not time out, and that chunking duration is within a reasonable range.
- Extract a portion of chunked content, verify that it contains core business fields such as inspection target unified social credit codes and inspection items, with no redundant format content.
- Adjust the
chunkSizeparameter to different ranges, test the completeness of chunked content, and confirm that no core business information is truncated.
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.