What data for this category looks like
Data for this category typically comes from object storage, data warehouses, annotation platforms, and customer acceptance feedback. The update rhythm follows each batch delivery or version iteration cycle. Commonly processed documents include dataset descriptions, version records, delivery lists, and acceptance reports. Core fields include version number, delivery batch identifier, sample difference description, and delivery integrity check items. There are no uniform mandatory units, and adjustments must be made to adapt to the format of specific delivery packages.
What constraints do these characteristics impose on the document parsing and chunking workflow
The data sources for this category are scattered, covering documents in different formats from multiple types of platforms, which creates requirements for parsing adaptability. Updates follow the rhythm of batch delivery or version iteration, so chunking must be associated with version identifiers to enable traceability binding and avoid mixing data across versions. Core fields such as version number and sample difference description contain key information, so metadata association must be retained during chunking to prevent omission of key information. Documents such as delivery lists have fixed structures, so content must be extracted by accurately matching field positions to ensure the integrity of delivered data.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapt to the field length of fixed-structure documents such as dataset descriptions and delivery lists, avoiding splitting of key information |
chunkSplitter | By paragraph + heading hierarchy | Match the hierarchical structure of version records and acceptance reports, retaining context association |
customPdfParse | Enabled | Adapt to PDF format documents delivered by customers, retaining metadata of original typesetting |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapt to the upload requirements of large-volume delivery packages, avoiding interruptions during large file uploads |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Cover the time requirements of batch parsing of multiple documents in FastGPT v4.17.0, avoiding timeout interruptions |
chunkSettingMode | Manual configuration | Adapt to format differences of different delivery packages, flexibly adjusting chunking rules |
Three common errors
- Phenomenon: No valid content is identified when chunking original delivery documents directly, and only question-answer pair extraction can return results. Cause:
customPdfParseis not enabled to adapt to the fixed typesetting structure of delivery documents, and the metadata of the original documents is not parsed correctly. - Phenomenon: When uploading a large-volume PDF delivery package, the upload progress reaches 90% and then prompts "Offset out of range". Cause:
UPLOAD_FILE_MAX_SIZEis not adjusted to the threshold adapted to the delivery package, orPARSE_FILE_TIMEOUT_SECONDSis not configured to cover the time required for large file parsing. - Phenomenon: Documents uploaded via the file collection API have inconsistent chunking results with documents uploaded directly via the platform. Cause:
chunkSettingModeconfiguration is not unified. The API upload uses the general chunking rule by default, while platform upload allows manual adjustment of chunking parameters, leading to differences in chunking granularity.
How to confirm configurations are properly set
- Upload a single typical delivery document (such as a version record), and check whether the chunking result retains the complete context of core fields including version number and delivery batch.
- Upload a large-volume PDF delivery package, and check whether the upload progress completes without errors such as offset out of range.
- Upload the same document via the API and the platform respectively, compare whether the chunking results are consistent, and confirm that the
chunkSettingModeconfiguration is unified. - View the parsing log, confirm that
customPdfParsehas taken effect, and the metadata of the original document has been correctly extracted.
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.