What this category of data looks like
Data sources include food safety sampling monitoring systems, the State Administration for Market Regulation’s sampling results query system, laboratory LIMS systems, and food producer and operator ledgers. Update frequency is monthly. Daily documents include sampling plans, sampling slips, inspection reports, unqualified food verification and disposal reports, and risk control announcements. Most documents combine structured tables and unstructured text. Sampling slips include fields such as sample identification and inspection items. Inspection reports include test values and judgment bases, with fields using professional units such as weight and concentration.
What constraints these characteristics impose on document parsing and chunking
Monthly batch updated documents come from diverse sources, with mixed structured table and unstructured text formats. This requires consistent chunk continuity. Sampling-related documents contain professional test values and regulatory judgment fields. Chunking must retain field associations to avoid splitting that breaks data integrity. Document formats exported from different systems vary. Parsing must support multiple formats including PDF, Excel, and CSV. It must also adapt to structural features of different document types such as sampling slips and inspection reports, to prevent missing key information during parsing due to format mismatches.
Configuration Settings
Configuration details for FastGPT v4.17.0 are listed below:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the mixed content length of sampling documents, retaining complete associations between inspection items and judgment bases |
chunkSplitter | Split by paragraph + table block | Matches the mixed structured table and unstructured text characteristics of sampling documents, avoiding field breaks caused by splitting table rows |
customPdfParse | Enable structured parsing mode | Adapts to fixed-format tables and fields in inspection reports and sampling slips, improving parsing accuracy for professional documents |
PARSE_FILE_TIMEOUT_SECONDS | 120–180 seconds | Covers parsing time for large inspection reports and monthly batch sampling plan documents, preventing parsing interruptions due to timeouts |
chunkSettingMode | Auto-adapt by document type | Compatible with document formats from different sources, meeting the processing needs of multi-type documents updated in monthly batches |
Three Common Configuration Mistakes
- Request errors occur when calling marker-pdf, and connection failure displays when accessing via 127.0.0.1. The cause is that the Docker-deployed marker service did not correctly map ports, or network access permissions for the service were not configured, preventing FastGPT from connecting to the parsing service.
- The knowledge base cannot import Excel format files, and CSV files only support two columns of data. The cause is that custom file parsing configuration was not enabled, or the CSV parsing column count threshold was not adjusted, leading to failure to properly recognize multi-column data.
- After submitting test question-answer pairs, returned content deviates from the original answer text of the specified question-answer pairs. The cause is that complete context of the question-answer pairs was not retained during chunking, or recall parameters were not set to limit usage to only question-answer pair data within the knowledge base, leading the AI to introduce additional generated content.
How to Verify Correct Configuration
- Upload a single inspection report PDF, and check if the parsed text retains complete paragraphs of inspection items, values, and judgment bases, with no chaotic table splitting.
- Upload monthly batch sampling plan documents, and verify that parsing time falls within the set
PARSE_FILE_TIMEOUT_SECONDSrange, with no timeout errors. - Import test CSV and Excel files, confirm that multi-column data can be properly recognized and imported into the knowledge base, with no format errors.
- Submit the original question of the test question-answer pair, and verify that returned content exactly matches the original answer text within the knowledge base, with no additional generated content.
The values provided here are common starting points and should be evaluated against the user’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.