Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Core data processed daily by judicial administrative departments, departmental legal affairs offices, and policy and legal affairs positions in government offices comes from regulatory and rule databases, higher-level law databases, departmental policy document repositories, and administrative normative document filing systems. Update frequency is irregular. Daily documents include draft normative documents, drafting explanations, legality review opinions, solicited opinion summaries, filing reports, and similar content. Most use standard official document formats, with fields such as document number, drafting entity, review conclusion, and opinion items. Data is mostly unstructured text, with a small amount of structured form content.
What constraints these characteristics impose on document parsing and chunking
Multi-source heterogeneous data sources require the parsing step to support multiple formats including native PDF and structured forms exported from systems, to avoid losing key fields such as document numbers and review opinions due to format mismatches. Irregular updates mean fixed-cycle preprocessing for chunking is not feasible, so real-time parsing of new documents must be supported. Documents contain itemized solicited opinion summaries and rights and obligation provisions; automatic chunking may split items from their context, so the original chapter hierarchy and item associations must be preserved to ensure complete context for subsequent legality comparison, adapting to item-by-item review requirements. Additionally, reviews must strictly match higher-level legal bases, so chunking must retain the binding relationship between provisions and their corresponding bases, to avoid losing associative retrieval after splitting.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
customPdfParse | Enabled | Adapts to the standard official document format of normative documents, retains key metadata such as document numbers and chapter titles, and prevents core fields required for review from being lost during general parsing |
chunkSize | 800–1200 characters | The provisions and review bases of normative documents are mostly coherent paragraphs; this range preserves the complete context of a single review item, avoiding overly fragmented or overly long chunks that impact retrieval |
chunkSplitter | Split by title and paragraph | Matches the chapter hierarchy of official documents, treats content within the same chapter as a single chunk unit, ensuring complete contextual association of rights and obligations and review opinions |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | A single draft normative document usually contains multiple pages; this duration covers the parsing time of conventional documents, preventing parsing failures due to timeout |
chunkSettingMode | Manual configuration | For multi-item documents such as solicited opinion summaries, manual adjustment of chunk boundaries can avoid automatic chunking breaking up single opinion content |
UPLOAD_FILE_MAX_SIZE | 100 MB | Adapts to the conventional size range of normative documents and supporting attachments, preventing upload failures due to oversized files |
Three Common Misconfigurations
- Uploading multi-column structured forms such as solicited opinion summary tables results in messy automatic chunking results, unable to split single-line content as independent units. Cause: Custom chunking mode is not enabled, and the general chunking logic does not recognize the row boundaries of structured tables.
- Timeout errors occur when parsing large-volume normative documents. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and the default duration is insufficient to complete parsing of long documents. - Generated search results include extra punctuation, leading to inaccurate matching of higher-level legal provisions during subsequent legality comparison. Cause: The parsing step does not clean redundant format punctuation at the end of official documents, and invalid characters are not filtered during chunking.
How to Confirm Proper Configuration
- Upload a typical draft normative document, check whether the parsed metadata includes key information such as document number and drafting entity, to confirm that the
customPdfParseconfiguration is effective. - Upload a multi-column structured form document, manually adjust the chunk boundaries, then verify if the chunking results split content by single line, to confirm that the
chunkSettingModeconfiguration meets requirements. - Upload a single large-volume document, check whether the parsing task duration meets expectations, to confirm that the
PARSE_FILE_TIMEOUT_SECONDSparameter is set appropriately. - Extract the parsed chunk content, check for redundant punctuation or formatted garbled text, to confirm that the parsing cleaning logic works correctly.
The values provided here are common starting points and should be measured 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.