This document applies to the document parsing and chunking module of FastGPT v4.17.0.
What the data for this category looks like
The data for this category primarily comes from content libraries, event backends, traffic data, and user feedback. Commonly processed documents include event plans, resource slot schedules, event page copy, post-event review reports, and more. Document fields include unique event identifiers, resource slot positions, execution times, participation metrics, and others. Data updates follow a weekly cycle, with temporary updates around holidays. Some core documents are finalized before the event launches.
What constraints do these characteristics impose on document parsing and chunking?
Event-related documents have clear time nodes and resource binding relationships. Chunking must avoid splitting event workflows and resource-related information. Structured schedule documents and plans contain fixed fields. Parsing must retain the original format and relational structure of these fields. Frequently updated documents require parsing processes that adapt to rapid iteration. Post-event review reports include unstructured user feedback snippets, so the system must support splitting mixed-format content.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Event documents often include process descriptions and structured fields. This range preserves logical integrity for content in a single chunk |
chunkSplitter | Split by paragraph + heading | Event documents have clear chapter divisions. Splitting by headings and paragraphs matches the logical nodes of events |
customPdfParse | Enabled | Event plans are often submitted as PDF files. Enabling this setting preserves original layout and table structure |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Post-event review reports may include multi-page materials and data attachments. This upper limit covers the size requirements of standard event documents |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large event document parsing requires sufficient time for format conversion and content splitting to avoid mid-process timeouts |
chunkSettingMode | Manual configuration | Event documents have large format differences. Manual configuration allows flexible adjustment of chunking rules to adapt to different document types |
Three common errors
- When uploading a public Yuque link, parsing fails with an unsupported data source type error. The cause is failing to confirm that the link is publicly accessible and meets the platform's parsing format requirements. Only publicly shared Yuque document links are supported; private links cannot be parsed.
- When calling an HTTP tool, parameter parsing succeeds but the request does not execute. The cause is incorrect configuration of the tool's trigger conditions or permission settings. In some scenarios, confirm that the target request address is within the allowed range.
- Embedded images are not correctly extracted after parsing a PDF document. The cause is failing to enable the image extraction switch for custom PDF parsing. The default parsing mode may only extract text content and ignore embedded image resources.
How to verify correct configuration
- Upload a typical event plan PDF. Check if the parsed text chunks retain the original document's heading and paragraph structure. Verify that the character length of each text chunk falls within the
chunkSizeconfiguration range. - Submit a compliant public Yuque document link. Confirm that the parsing task status shows as completed, and that the returned content includes all text and image information from the original document.
- Test the configured HTTP tool. Check if the tool call logs show that parameters are correctly passed, and that the request execution status matches expectations.
- Adjust
PARSE_FILE_TIMEOUT_SECONDSto a shorter value. Upload a large event document. Confirm that a timeout error is triggered, which verifies the effectiveness of the timeout configuration.
The values provided 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 2026-10-19.