What this category of data looks like
This category of data primarily comes from contract systems, copyright registration databases, internal content repositories, and complaint inboxes. Daily processed documents include authorization contracts, copyright ledgers, removal notices, and infringement complaint materials. A single authorization contract contains fields such as authorized subject, scope, and valid period. Copyright ledgers associate authorization status with individual content entries. Updates follow individual business orders or monthly cycles. The structure of each document varies by business scenario, with no unified fixed format. Some documents include scanned copies or original credentials in multiple attachment formats.
What constraints do these characteristics impose on the "document parsing and chunking" link
The multi-source nature of this category of data requires the parsing module to support structured tables, unstructured text, and scanned copies simultaneously. This prevents missing ledger fields exported from contract systems. Document structures are inconsistent, and individual text lengths are long. Chunking must adapt to long text splitting. This avoids splitting related fields such as authorization scope and valid period into different chunks. Updates follow individual orders or monthly cycles, so high-frequency real-time parsing is not required. However, full parsing of single documents must be ensured. This prevents errors in core links such as authorization verification and expiration reminders due to parsing failures. Some documents are authorization materials or complaint attachments in PDF and PPT formats. Parsing logic for different formats must be supported to avoid unreadable content.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | This category of documents includes long-text contracts and structured ledgers. This range balances the integrity of information per chunk and retrieval accuracy. It avoids splitting core fields such as authorization scope. |
chunkSplitter | Split by paragraph + semantic chunking | This category of documents has inconsistent structures. Paragraphs contain authorization clauses, ledger entries, and other content. Semantic chunking preserves business logic relevance. It avoids splitting cross-paragraph authorization-related information. |
customPdfParse | Enabled | This category frequently processes PDF-format authorization contracts and complaint materials. Enabling custom parsing optimizes recognition of formulas and tables. It adapts to complex document formats. |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Some authorization contracts may include multi-page scanned copies or large PDF attachments. This upper limit covers the size of conventional business documents. It prevents upload failures due to oversized files. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large multi-page documents take longer to parse. This duration prevents parsing failures due to timeout. It adapts to the scale of documents processed in monthly batches for this category. |
chunkSettingMode | Manual configuration | This category has large differences in document formats. Manual configuration allows adjusting chunking rules for different document types such as contracts and ledgers. It adapts to business requirements. |
Three Common Configuration Mistakes
- Symptom: After configuring the Feishu knowledge base, PPT or PDF format documents cannot be synced. Cause: The
customPdfParseconfiguration is not enabled, or parsing adaptation rules for the corresponding formats are not configured. This prevents non-standard document formats from being recognized and synced. - Symptom: When deploying FastGPT v4.8.12 locally, complex formulas cannot be parsed normally, and abnormal results are returned. Cause: The marker_images version does not match the FastGPT version, or GPU acceleration support is not enabled. This prevents the formula parsing module from being called normally.
- Symptom: When clicking chunk preview for a PDF document in the FastGPT knowledge base, a prompt that the file content cannot be read is returned. Cause: The
UPLOAD_FILE_MAX_SIZEconfiguration value is smaller than the actually uploaded PDF file size, or thePARSE_FILE_TIMEOUT_SECONDSconfiguration duration is insufficient. This causes the parsing process to interrupt.
How to Confirm Successful Configuration
- Upload a single authorization contract in PDF format. View the parsed chunk content. Confirm that core business fields are not split into different chunks.
- Upload a PPT-format copyright ledger file. View the parsed text content. Confirm that there is no garbled text or missing key information.
- In the knowledge base management backend of FastGPT v4.17.0, verify that the values of configuration items such as
chunkSizeandcustomPdfParsematch the preset rules. - Submit a batch document parsing task. View the system logs. Confirm that no error prompts for timeout or file size exceeding the limit appear.
The values provided here are common starting points. They 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.