This document applies to the document parsing and chunking features of FastGPT v4.17.0.
What the data for this category looks like
Daily documents for this category include requirement research notes, PRD documents, user story lists, and requirement change forms. Data mainly comes from customer interview records, internal ticketing systems, public competitor pages, and business tracking dashboards. Update rhythms typically follow weekly iterations or align with product version releases. Most documents use structured paragraphs as their main format, with some containing nested acceptance criteria and priority fields. There is no unified mandatory format. Some documents also include scattered unstructured remark content.
What constraints these characteristics impose on document parsing and chunking
Document formats vary widely across different sources. These range from structured PRD table paragraphs to scattered interview note text. Parsing tools must support multi-format input. Frequently updated documents require fast parsing workflows to avoid slowing requirement iteration cycles. Nested acceptance criteria and priority fields need contextual association retained during chunking, to prevent loss of business logic after splitting. For security and compliance review scenarios, sensitive information must not be truncated. Chunk boundaries must cover complete business boundary conditions.
How to configure the settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of paragraphs and acceptance criteria in PRD documents, avoiding loss of business logic after splitting |
chunkSplitter | Segment by headings and line breaks | Matches the structured hierarchy of requirement documents, preserving the integrity of requirement groups |
customPdfParse | Enabled | Compatibility with tables and nested content in exported PRD PDF documents, improving parsing accuracy |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing duration of batch requirement change forms, avoiding parsing failures in frequently updated scenarios |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to batch import scenarios for requirement documents, avoiding large file upload failures |
chunkSettingMode | Manual configuration | Flexibly adjust chunking parameters for different types of requirement documents, adapting to multi-source data |
Three common errors
- Symptom: Chunked documents cannot be reused normally across environments using different vector models. Cause: The
chunkSettingModeconfiguration is not unified, leading to a mismatch between chunking format and the target environment’s parsing logic. - Symptom: Markdown formatted content fails to render in context references. Cause: The
chunkSplitterconfiguration does not retain document format markers, resulting in lost typesetting information after chunking. - Symptom: Valid content cannot be parsed when passing in a document link. Cause: The
PARSE_FILE_TIMEOUT_SECONDSconfiguration value is too low, orcustomPdfParseis not enabled to adapt to external link parsing rules.
How to verify the configuration is correct
- Upload a typical PRD document, check if the parsed chunked content retains acceptance criteria and priority fields.
- Adjust the
chunkSizeparameter, compare document lengths before and after chunking to confirm chunk boundaries meet expectations. - Test passing an external document link, verify the parsing result contains complete text content with no empty fields or truncation.
- Switch between different vector model environments, import the same chunked document, confirm the model can normally read the chunked data.
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 2026-10-19.