What the data for this category looks like
This category of data mainly comes from customer requirements, public data sources, collection tools, and in-house sample libraries. The update frequency is once per single project cycle or once per week. Daily generated documents include four types: collection plans, sample specifications, authorization instructions, and task schedules. Core fields include sample scope definition, field specification requirements, and collection legality verification identifiers. Most document structures are a mixed format of structured tables and paragraphs, and some documents include multi-level headings and attachment descriptions.
What constraints do these characteristics impose on the "document parsing and chunking" link
Since most documents in this category are mixed structured content and explanatory text, the document parsing link must simultaneously adapt to table field extraction and paragraph text splitting to avoid losing field association information. The update feature per single project or weekly requires that the chunking logic can flexibly adapt to task documents of different lengths, avoiding chunks that are too short or too long that affect subsequent retrieval. At the same time, authorization instructions and legality verification content must retain complete context, preventing sensitive information from being split and causing verification failure. In project scenarios with multiple file uploads, it is necessary to ensure that parsing results of different files can be accurately distinguished, avoiding data confusion that affects subsequent task configuration.
How to Configure Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the average length of paragraphs and table fields in this category of documents, avoiding overly fragmented chunks that break context or overly long chunks that reduce retrieval accuracy |
chunkSplitter | Split by paragraph + heading | This category of documents includes heading hierarchies and paragraph descriptions. Splitting by heading preserves logical content associations and prevents unrelated content across headings from being grouped into the same chunk |
customPdfParse | Enabled | This category of documents often includes structured tables and compliance instructions. Enabling custom PDF parsing accurately extracts table fields and associated text, avoiding loss of structured content from default parsing |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Single projects may include multiple batch-uploaded documents. Reserving sufficient timeout time prevents interruptions during parsing of large files or multiple files |
chunkSettingMode | Adapt by document type | Switch chunking rules for different document types such as collection plans and authorization instructions to match content structures, improving the accuracy of parsing and chunking |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to large-sized collection documents uploaded in batches for single projects, preventing file uploads from being blocked and causing task interruptions |
Three Common Configuration Mistakes
- Symptom: PDF parsing suddenly returns an error with no clear log prompt. Cause: The
customPdfParseparameter is not enabled, or default parsing rules changed after a recent FastGPT version update, making it unable to adapt to structured table content in this category of documents. - Symptom: Node output variables cannot be saved to Feishu multi-dimensional tables via HTTP, but direct calls to Feishu workspace succeed. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not configured correctly, causing parsing timeout and interrupting variable transmission, or the request header format of the Feishu API is not matched correctly. - Symptom: Knowledge base search tests fail to hit expected chunked content. Cause: The
chunkSizesetting does not match the current document content length, causing chunked content to fail to match the context range of retrieval keywords, or chunking rules are not adapted by document type.
How to Confirm the Configuration Is Correct
- Upload a single collection plan document, check whether the parsing result completely extracts core fields such as sample scope and field specifications, with no key content lost.
- Batch upload 3 to 5 task documents of different types, confirm that parsing results of each document can be distinguished by file name or exclusive identifier fields, with no content cross-confusion.
- Adjust the
chunkSizeparameter, compare chunked content under different values, confirm that chunk length matches current document content structure, with no excessive splitting or overly long chunks. - Trigger a complete batch parsing process, check whether timeout errors occur, confirm that the value of
PARSE_FILE_TIMEOUT_SECONDSadapts to the total size of currently uploaded files.
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.