What this type of document data looks like
Backend service development-related documents mainly come from code repositories, API gateway logs, databases, and configuration centers. Update frequency is mostly daily or per version release cycle. Common documents include interface design documents, database change tickets, code review records, and release notes. Document structures typically include interface paths, request parameters, return fields, transaction rules, exception handling logic, and more. There is no unified mandatory format. Some documents embed code snippets, SQL statements, or configuration items.
What constraints these characteristics impose on the document parsing and chunking link
Since document sources are scattered and formats vary, parsing must support different content types such as plain text, code blocks, and SQL fragments. Interface design documents often contain long paragraphs of business rule descriptions. Chunking must avoid splitting continuous logic related to transaction boundaries. Database change tickets involve precise table structures and permission configurations, so chunking must retain contextual associations. Documents with high update frequencies require the parsing process to have low-latency processing capabilities, while also avoiding index conflicts caused by frequent updates.
How to configure the settings
The following configurations apply to FastGPT v4.17.0
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the average length of interface descriptions and business rules in backend service documents, avoiding splitting continuous logic related to transaction boundaries |
chunkSplitter | Split by paragraphs + code blocks | Compatible with code snippets and SQL statements in interface documents, preserving semantic integrity |
PARSE_FILE_TIMEOUT_SECONDS | 60–120 seconds | Addresses documents containing long log fragments or multiple code blocks, preventing upload failures caused by parsing timeouts |
UPLOAD_FILE_MAX_SIZE | 50–100 MB | Meets the upload requirements for batch documents such as code review records and version release notes |
chunkSettingMode | Manual configuration | Allows adjustment of chunking rules for different document types including interface design documents and database change tickets, adapting to business scenarios |
Three common mistakes
- Symptom: After using the
chunkmode to call the pushdata API to upload documents, it stays in indexing with no progress for a long time. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter is not configured correctly, or the document exceeds theUPLOAD_FILE_MAX_SIZElimit, causing the parsing process to be forcibly terminated without triggering a retry. - Symptom: After uploading a PDF document, the charts and code screenshots in it are not recognized and included. Cause: The image extraction switch for the
customPdfParseparameter is not enabled. The default parsing mode only extracts text content. - Symptom: After uploading HTML interface documents generated by Javadoc, no valid parsed content is obtained. Cause: The structure splitting rules for HTML documents are not adapted. The default chunker cannot recognize interface definition tags in HTML, resulting in lost semantic chunks.
How to confirm the configuration is correct
- Check the parsing progress field in the upload log, confirm that a parsing completion marker appears within the duration configured by
PARSE_FILE_TIMEOUT_SECONDS. - Upload a PDF document containing images, check whether the knowledge base search results include text descriptions associated with the images or image placeholder identifiers.
- Upload HTML documents generated by Javadoc, verify whether the search results include core content such as interface paths and request parameters.
- Test calling the
chunkmode of the pushdata API, confirm that the indexing completion status is returned within 1 minute after upload.
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.