What the data for this category looks like
This category of data primarily comes from contract systems, copyright registration information, content libraries, and complaint inboxes. Commonly processed documents include authorization contracts, copyright ledgers, takedown notices, and infringement complaint materials. Document structures vary significantly: authorization contracts contain structured fields and tables, copyright ledgers are plain text lists, and infringement complaint materials often include multi-level evidence directories. Data updates follow per-order or monthly cycles, and individual documents cover core content such as rights subjects, authorization scope, and validity periods.
What constraints do these characteristics impose on the document parsing and chunking step
The diversity of document structures requires the parsing module to adapt to different formats and hierarchies, without losing multi-level directories, tables, or field associations. Differences in data format across sources such as PDF, Feishu Docs, and Word demand flexible parsing configurations to accommodate non-standard content layouts. The high value of individual documents and the severe consequences of errors require parsing and chunking to be accurate and complete, with no missing core fields or semantic breaks. The monthly or per-order update rhythm means the parsing module must support small-batch, high-stability single processing, adapting to resource usage requirements for individual tasks.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the core field length of authorization contracts and copyright ledgers, avoiding semantic breaks after splitting |
chunkSplitter | Split by paragraph + heading | Adapts to the multi-level directory structure of infringement complaint materials and authorization contracts, preserving content hierarchy associations |
customPdfParse | Enabled | Adapts to PDF-format authorization contracts and complaint materials, preserving original layout and table structures |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles infringement complaint documents with multiple evidence attachments, avoiding timeout failures due to large content volume |
UPLOAD_FILE_MAX_SIZE | 100 MB | Adapts to packaged copyright ledger files for batch uploads, meeting file size limits for single processing |
chunkSettingMode | Manual configuration | Adjust chunking rules for different document types (contracts/ledgers/notices) to accommodate field specificity |
Three common mistakes
- Issue: External pdf-maker parser calls fail, with no parsing results returned. Cause: The corresponding parsing key was not configured in the FastGPT v4.17.0 backend, preventing the parsing module from calling external tools.
- Issue: Feishu Docs with multi-level directories only parse top-level content, and doc documents and images cannot be retrieved. Cause: The default chunking configuration does not adapt to the hierarchical structure of Feishu Docs, and the non-text content parsing switch is not enabled.
- Issue: Split-related error logs appear during marker parsing, and the FastGPT 4.9.0 docker-deployed parsing module does not pass text. Cause: The
chunkSizeparameter is set incorrectly, leading to failure to match content boundaries during text splitting, or the parsing module’s file transfer directory was not mounted during deployment.
How to confirm the configuration is complete
- Upload a standard authorization contract PDF, check that the parsed text fully retains core fields such as authorization scope and validity period, with no missing content or layout chaos.
- Upload a Feishu Doc with multi-level directories, confirm that the parsing result includes all levels of directories and corresponding body content, with no hierarchy lost.
- Upload a packaged copyright ledger file under 100 MB, check that parsing time does not exceed 600 seconds, with no timeout errors.
- Call the batch parsing interface, verify that the returned chunked text has no semantic breaks or field confusion across different document types.
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.