What data for this use case looks like
Typical data sources include regulatory rule repositories, higher-level law databases, departmental policy document repositories, and administrative regulatory document filing systems. Update frequency is irregular. Daily processed documents include regulatory document drafts, drafting explanatory notes, legality review opinions, summary of solicited comments, and filing reports. Document structures include fields such as chapter divisions, article numbers, and attachment descriptions. Some documents include supplementary information such as official letterheads and filing numbers. There is no uniform fixed word count range for individual documents.
What constraints do these characteristics impose on the document parsing and chunking link?
Document sources cover multiple official databases. Supported formats include PDF, Word, structured tables and other types. The parsing link must adapt to different layout formats. A single document may contain dozens of articles and supplementary explanations. Chunking must retain the contextual association of articles to avoid truncating core review basis. Supplementary information is mixed with business articles. Core content and non-essential metadata must be distinguished before chunking. Update frequency has no fixed cycle. Fast parsing of new documents must be supported to avoid affecting review processes due to parsing timeouts.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting/Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Regulatory document articles mostly use short sentence structures. This range retains complete semantics for a single article or adjacent related articles, avoiding context loss during retrieval |
chunkSplitter | By chapter + paragraph | Documents include built-in chapter divisions and article numbers. Splitting by chapter preserves logical units required for review, preventing semantic confusion from cross-chapter splicing |
customPdfParse | Enable header and footer filtering | Officially filed documents often contain irrelevant content such as administrative agency letterheads and filing numbers. Filtering this content reduces interference from non-core data during retrieval |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | A single regulatory document may include multiple attachments such as drafting explanations and comment summary documents. Parsing takes longer, and this duration covers most scenarios |
chunkSettingMode | Manual configuration | Different document types (drafts, opinions, filing reports) have large structural differences. Manual configuration allows adjustment of chunking parameters for individual document types |
Three Common Mistakes
- Symptom: Auxiliary data such as filing numbers and drafting units appears before core articles in retrieval results, with abnormal priority. Cause: Core business content and supplementary metadata were not distinguished during chunking, and no content weight rules were configured.
- Symptom: After uploading documents chunked using a local vector model, server-side retrieval recall rate drops significantly. Cause: Semantic encoding logic varies between vector models. Failing to retain uniform semantic boundaries during chunking leads to reduced encoding matching accuracy.
- Symptom: Knowledge base chunk previews display correctly, but context references are not rendered in Markdown format and only show plain text. Cause:
customPdfParseformat retention configuration was not enabled, and original document format markers were lost during chunking.
How to Confirm Correct Configuration
- A single regulatory document draft is uploaded, the chunk preview interface is examined, and each chunk is verified to contain complete article paragraphs with no truncated short sentences.
- A PDF document with an administrative agency letterhead and filing number is uploaded, and the parsed text is checked for removal of irrelevant header and footer content.
- Two documents with distinct structures (drafting explanation and comment summary) are uploaded, and chunk lengths are confirmed to match the pre-configured value range.
- Parsing task run logs are reviewed, and the absence of timeout errors is confirmed.
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.