What the data for this category looks like
Documents covered by this category mainly include enrollment application forms, student status information forms, transfer applications, enrollment plans, admission rosters, and student status change approval forms. Data sources include the national primary and secondary school student status management system, local enrollment platforms, household registration and property data, and school educational administration systems. The update frequency is daily. Most documents have fixed field structures, including standardized content such as student ID number, student status number, household registration address, and admission batch. Some documents contain nested multi-paragraph approval process descriptions and qualification verification requirements.
What constraints these characteristics impose on document parsing and chunking
Since data sources are scattered and updated daily, document formats generated by different local platforms have custom variants. Adaptation to multi-source heterogeneous table and text hybrid structures is required. Fixed fields in documents such as student status number and ID number must be accurately extracted to avoid splitting critical identity identifiers during chunking. Documents with nested approval processes must retain paragraph logic, and must not separate approval nodes from application content. A single document may contain multiple batches of student data. Chunking must avoid merging content across students to ensure the accuracy of qualification verification during subsequent retrieval.
How to configure the settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Documents contain fixed fields and approval processes. This range retains the complete logic of a single student application or single student status change, avoiding splitting critical identity information |
chunkSplitter | Split by line breaks and paragraph separators | Adapts to the paragraph-based structure of enrollment-related documents, retaining manually divided approval nodes and qualification descriptions |
customPdfParse | Enable Marker parsing mode | Processes multi-source heterogeneous PDF documents, retains the original typesetting of tables and nested text, and avoids field misalignment |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to large-volume documents such as batch enrollment plans and admission rosters, meeting daily batch upload requirements |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processes long documents containing multiple batches of student data, reserving sufficient parsing time to avoid timeout errors |
chunkSettingMode | Manual configuration mode | Allows adjustment of chunking boundaries for student status-related documents, avoiding automatic chunking from merging cross-student batch data |
Three common configuration mistakes
- Uploading a large PDF document triggers an error prompt indicating parsing timeout. The cause is failure to adjust the
PARSE_FILE_TIMEOUT_SECONDSparameter. The default timeout duration is insufficient for long documents containing multiple batches of student data. - After manually setting a custom separator, chunking results either merge application paragraphs from multiple students or split the complete process of a single student status change. The cause is failure to switch
chunkSettingModeto manual configuration, or failure to adapt thechunkSizevalue range to the fixed field length of the document. - Deploying Marker and then processing a PDF results in an error, returning a JSON response containing error information. The cause is failure to correctly configure the associated environment variables for
customPdfParse, or incompatibility between the deployment version and the FastGPT version.
How to verify the configuration
- Upload a single PDF document containing 100 student status information entries, and check whether the parsed chunking results correspond one-to-one with individual student data, with no cross-student content merging.
- View the system upload limit, and confirm that
UPLOAD_FILE_MAX_SIZEis set to 1000 MB or meets the demand threshold for local document volume. - Trigger a long document parsing task, wait for the duration set by
PARSE_FILE_TIMEOUT_SECONDS, and check whether the parsing task completes successfully with no timeout errors. - Manually adjust
chunkSizeto 800 characters, upload a single transfer application document, and confirm that the chunking results retain the complete application reason and approval column content.
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 2026-09-28.