What the data for this category looks like
The main data subjects for this category are market supervision bureau special equipment departments, inspection and testing institutions, and equipment users and maintenance units. Daily documents include special equipment use registration certificates, periodic inspection reports, maintenance records, hidden danger rectification notices, accident reports, and operator certificates. Data is sourced from special equipment safety supervision systems, inspection and testing institution systems, user unit ledgers, and operator certificate databases. The update frequency is monthly. In core business workflows, verifying equipment registration and inspection cycles, checking maintenance and operator qualifications, and tracking hidden danger rectification and closing are the most time-consuming steps. Data anomalies can lead to equipment shutdown, administrative penalties, or special equipment accidents.
What constraints these characteristics impose on the "document parsing and chunking" link
Document types are diverse and have significant structural differences. Standardized documents such as use registration certificates and periodic inspection reports exist alongside unstructured ones like maintenance records and hidden danger rectification notices. Parsing workflows must support content extraction across multiple document formats. Monthly updated data volume is relatively stable but requires batch processing. Parsing and chunking must support batch tasks and allow timeout control. Data fields include strongly correlated information such as equipment numbers, inspection cycles, and qualification validity periods. Chunking must retain contextual associations to avoid losing critical logic after splitting. The high-risk supervision attribute requires accurate parsing results. Invalid redundant content must be filtered. Critical paragraphs related to hidden danger rectification and accidents must not be omitted.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the information density of key paragraphs in special equipment documents, such as equipment numbers, inspection cycles, and rectification requirements, to avoid losing contextual associations after splitting |
chunkSplitter | Split by paragraph + title | Aligns with the structure of multiple document types, retaining the chapter logic of registration certificates and inspection reports, and the entry association of maintenance records |
customPdfParse | Enabled | Optimizes text extraction from tables and images within official PDF documents such as inspection reports and use registration certificates |
UPLOAD_FILE_MAX_SIZE | 100 MB | Adapts to batch-uploaded monthly ledgers and collections of inspection reports, preventing upload failures due to oversized files |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Covers the parsing time of large single inspection reports or accident reports, preventing parsing tasks from being interrupted due to timeout |
chunkSettingMode | Manual configuration | Allows adjustment of chunking parameters for different document types, adapting to differences between standardized registration certificates and unstructured maintenance records |
Three Common Misconfigurations
- Phenomenon: After uploading a PDF document, the interface shows no parsing completion status prompt, and search test results are empty. Cause: The
customPdfParseconfiguration is not enabled, leading to failure to correctly extract text from tables and images within the PDF, resulting in empty chunked data. - Phenomenon: After local deployment, the data processing step in the second stage of knowledge base training is empty. Cause: The
PARSE_FILE_TIMEOUT_SECONDSvalue is set too small. Large ledger files uploaded in monthly batches exceed the timeout during parsing, and no valid chunked data is generated. - Phenomenon: In the latest main branch code, question text triggers the document parsing process. Cause:
trainingTypeis not configured correctly, and conversational questions are mistakenly identified as document content to be parsed, triggering additional parsing tasks.
How to Verify Proper Configuration
- Upload a single special equipment periodic inspection report, check the backend parsing log, and confirm that the equipment number and inspection cycle fields in the document are fully extracted.
- Trigger a batch upload of monthly maintenance records, check whether the total size of uploaded files meets the configured requirements, and confirm there are no upload failure prompts.
- Initiate a simulated question, confirm that the question text does not trigger the document parsing process, and only call existing chunked data for question answering.
- Wait for the parsing task to complete, check the chunk list, and confirm that each chunk contains complete paragraphs or chapter information with no obvious truncation.
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.