What the data for this category looks like
The typical users of this category are product operations, customer success teams, and product managers. Documents handled daily include demand pool lists, demand evaluation forms, customer feedback records, and version planning descriptions. Data sources cover ticketing systems, customer interview records, CRM systems, and product roadmaps. Update frequency is mostly weekly or per version release cycle. Document structures include dedicated fields such as demand priority, submission time, and associated customer ID. There is no unified fixed format, and some documents embed images or table content.
What constraints these characteristics impose on the "document parsing and chunking" link
Multi-source heterogeneous document sources create significant format differences. Support for multiple formats such as PDF, Excel, and plain text is required. Some documents contain embedded images, which places requirements on the mixed text and image processing capabilities of parsing tools. Frequently updated documents need batch parsing capabilities, and duration thresholds for single-file and batch parsing must be controlled. The presence of dedicated fields requires the chunking process to retain contextual associations, preventing loss of key content such as demand priority and customer association information after splitting. Otherwise, the accuracy of subsequent duplicate demand merging and value assessment links will be affected.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the field density of demand pool documents, avoiding overly fine splitting that prevents duplicate demand association, or overly coarse splitting that reduces subsequent merging efficiency |
customPdfParse | Enable mixed text and image parsing | Adapts to customer feedback screenshots and evaluation form charts often embedded in demand documents, avoiding loss of key visual information when only extracting plain text |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Matches the parsing duration requirements of weekly updated batch documents, preventing batch tasks from being interrupted due to single-file parsing timeout |
chunkSplitter | Split by semantic blocks | Retains contextual associations of dedicated fields such as demand priority, version number, and associated customer ID, ensuring chunked content has independent assessment value |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to large documents such as version planning descriptions imported in batches, meeting the batch processing needs of full-cycle demand documents |
Three common errors
- Empty image fields after parsing: The symptom is that after uploading a PDF demand evaluation form with embedded images, the parsing result only retains plain text with no image-related content. The cause is that the mixed text and image parsing mode of
customPdfParseis not enabled, and only basic text content is extracted. - Timeout errors during batch parsing: The symptom is that when uploading multiple weekly updated demand documents, some tasks return timeout status codes. The cause is that
PARSE_FILE_TIMEOUT_SECONDSis not adjusted to a value suitable for batch parsing, and the single-file parsing duration exceeds the preset threshold. - Duplicate demands cannot be merged after chunking: The symptom is that multiple feedbacks from the same customer are split into multiple independent chunks, making merging via semantic clustering impossible. The cause is that
chunkSizeis set too small, splitting the same segment of demand text into multiple unrelated small chunks.
How to confirm the configurations are correct
- Upload a PDF demand evaluation form with embedded images, and check if the parsing result includes image annotations or corresponding text descriptions.
- Upload a single version planning description document of around 100 MB, and confirm that the parsing progress does not trigger a timeout error and the task completes normally.
- Select a segment of text that includes demand priority and customer ID, and verify that the chunking result retains the complete context of that segment.
- Import customer feedback records in Excel format, and check if the parsed text blocks completely retain the table column field information.
This page is written based on the document parsing and chunking functions of FastGPT v4.17.0. The values listed are common starting points and should be evaluated 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.