What this category of data looks like
Data for this category mainly comes from the food safety sampling monitoring system, the national market supervision administration sampling result query system, the laboratory information management system (LIMS) of inspection institutions, and the account books of food producers and operators. The data is updated monthly. Commonly processed documents include sampling plans, sampling slips, inspection reports, unqualified food verification and disposal reports, and risk control announcements. Most documents contain fixed business fields such as sampling number, inspection items, unqualified judgment items, and disposal measures. Some inspection reports include original laboratory test tables. The degree of format standardization varies depending on the submitting entity.
What constraints these characteristics impose on the document parsing and chunking link
The monthly batch-updated documents require the parsing link to support batch task scheduling to avoid single-processing timeouts. The mixed document structure of fixed fields and laboratory test tables requires the parsing module to adapt to both structured field extraction and complex table splitting. There is strong business correlation between documents, for example, a sampling slip corresponds to a subsequent inspection report. Chunking must retain contextual associations to avoid splitting that breaks business logic. Format differences across submitting entities require configurations to have flexible adjustment space while ensuring stable recognition of core business fields.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Monthly batch documents include long-text inspection reports and short-field sampling slips. This range balances contextual integrity and retrieval efficiency |
chunkSplitter | Split by paragraph + title | Documents contain title hierarchies and business paragraphs. This preserves business logical associations and avoids chunking that breaks business relevance |
customPdfParse | Enabled | Sampling reports include standardized tables and fixed fields. Custom parsing improves the accuracy of structured content extraction |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Large inspection reports take longer to parse. This range avoids task interruption due to timeout |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Monthly batch sampling document collections may occupy large storage space, adapting to batch processing needs |
chunkSettingMode | Custom mode | Different document types require adjustment of chunking parameters to adapt to multi-format scenarios such as sampling slips and inspection reports |
Three common mistakes
- Symptom: Some PDF files show empty content after upload, while others can be recognized normally. Cause: The PDF contains encrypted content, scanned images without embedded recognizable text, or the document format exceeds the preset parsing range.
- Symptom: After changing the file suffix of a Java interface file to TXT and importing it, no parsed data is generated. Cause: The unstructured text format does not match the preset parsing rules, and custom parsing to adapt to code-like text structures is not enabled.
- Symptom: After using chunk mode to call the pushdata API to upload data, the indexing status remains displayed for a long time. Cause: Chunk parameters are set too large, resulting in overly long single-block content, or batch tasks are not configured with shard processing, causing parsing and indexing blocking.
How to confirm the configuration is complete
- Upload a single typical document such as a single inspection report, check that the number of parsed chunks matches the document length, and adjust parameters to the range that meets business needs.
- Batch import the monthly sampling document collection, check the completion rate of parsing tasks, and confirm that the timeout parameter setting covers the parsing time of the longest document.
- Import encrypted or scanned PDF documents, verify whether basic text content can be extracted after custom parsing is enabled, and confirm that the configuration adapts to the current document type.
- Call the pushdata API to upload test data, check the index status update rhythm, and confirm that the chunk parameters do not exceed the system processing limits.
The values given 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.