What this category of data looks like
This category of data comes from internal systems of central enterprise internal control departments, audit departments, financial departments, subordinate enterprises, and external audit institutions. It includes documents such as internal control self-evaluation reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification cancellation certificates. The data is updated annually. Most documents are a mix of structured tables and unstructured text, with fields including issue number, rectification responsible person, completion deadline, supporting material link, and some documents contain multi-level approval process node information.
What constraints do these characteristics impose on the "document parsing and chunking" link
Annual batch-updated document collections require the parsing process to have stable batch processing capabilities. Documents with mixed structures need to distinguish between table fields and text paragraphs to avoid losing key column information during parsing. For multi-field rectification ledgers and issue lists, chunking must not destroy field relevance. For documents with nested process nodes, chunking must retain contextual relevance to ensure the accuracy of policy basis Q&A. Heterogeneous data from multiple systems must be unified into a consistent parsing format before chunking to avoid field misalignment during retrieval.
How to set the configuration
FastGPT v4.17.0 applies the following configuration items:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of single records in rectification ledgers and issue lists, avoiding splitting key policy basis paragraphs |
chunkSplitter | Split by paragraph + table header | Adapts to mixed-structure audit documents, retaining the relevance between table headers and corresponding content |
customPdfParse | Enable structured parsing mode | Accurately extract table fields from audit notices and rectification ledgers, avoiding text garbling and field omission |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to annual audit document collections uploaded in batches, avoiding parsing timeouts for large files or batch documents |
chunkSettingMode | Manual configuration | For multi-field audit documents, chunking boundaries can be flexibly adjusted to ensure the integrity of policy basis |
Three common mistakes
- After uploading an Excel-format rectification ledger, search results only return two columns of content. The cause is that the structured table parsing mode of
customPdfParseis not enabled. The default parsing only extracts text columns and fails to recognize multi-column fields. - Parsing timeout errors occur when uploading annual audit documents in batches. The cause is that the
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted. The default timeout duration is insufficient for processing large batch files. - The retrieved policy basis fragments after chunking are incomplete and cannot be associated with corresponding rectification requirements. The cause is that
chunkSizeis set too small, splitting a single policy basis paragraph into multiple fragments and destroying contextual relevance.
How to confirm the configuration is correct
- Upload a single rectification ledger Excel file, check if the parsed text contains all column fields to confirm that structured parsing is active.
- Upload a batch audit document package, check if the parsing progress completes within the duration set by
PARSE_FILE_TIMEOUT_SECONDS. - Enter an audit question related to policy, check if the search results contain complete policy basis paragraphs and corresponding rectification requirements.
- After adjusting the
chunkSizeparameter, compare the completeness of search results under different values to confirm that the chunking boundaries meet business needs.
The values given 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.