What the data for this category looks like
Data sources for snack food intelligent due diligence reports include production enterprise annual compliance reports, raw material supplier quality inspection certificates, batch factory inspection reports, and product parameter pages published on e-commerce platforms. Update cadences vary by document type: raw material batch documents are updated with production cycles, annual compliance reports are updated per fiscal year, and supplier qualification documents are reviewed every six months. Document structures include structured nutrition facts tables, scattered batch traceability numbers, paragraph-style production process descriptions, and tabular raw material purchase details. Fields and units include energy and fat content per 100 grams, additive usage amounts, and identification fields such as batch numbers and supplier unified social credit codes.
What constraints do these characteristics impose on the "document parsing and chunking" link
Structured nutrition facts tables and raw material purchase details require retaining the association between cells and their corresponding values during parsing. This avoids losing field correspondence after splitting. Scattered batch traceability numbers must be bound to the context of their associated production batch. They cannot be split into independent chunks. Document formats vary widely across sources. Standardized PDF reports and paper quality inspection certificates in image format are both common, so multi-source parsing capabilities must be supported. The diversity of unit fields requires unified conversion after parsing. Mixed units such as grams and milligrams would disrupt subsequent retrieval and analysis. Bulk batch documents have consistent structures but independent content. A unified chunking logic must be maintained to avoid inconsistent chunk granularity.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Balances the completeness of information per chunk and retrieval granularity, as snack food due diligence documents contain content of varying lengths such as nutrition facts tables and process descriptions |
chunkOverlap | 100–150 characters | Prevents loss of contextual association when splitting structured content such as nutrition facts tables and raw material purchase details |
PARSE_TABLE_MODE | structured | Supports standardized quality inspection tables and purchase detail tables in documents, retaining the association between cell fields and their corresponding values |
PARSE_IMAGE_ENABLE | Enabled | Supports parsing text and table content from image-format quality inspection certificates provided by some suppliers |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Reserves sufficient parsing time to avoid timeout interruptions, as bulk batch documents can contain large amounts of content |
UPLOAD_FILE_MAX_SIZE | 500 MB | Meets the size requirements for single annual compliance reports and bulk batch documents |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Parsing results are empty after uploading image-format quality inspection certificates, or the parsing API call with the
image_urlparameter returns no response. This occurs because thePARSE_IMAGE_ENABLEconfiguration is not enabled, or the providedimage_urldoes not have public network access permissions. - The parsing API call returns a
400 Bad Requeststatus code, and the chunking result is empty. This occurs because thefileorimage_urlparameter is not correctly included, or the parameter format does not meet interface requirements. - After locally deploying MinerU, FastGPT calls the parsing interface and returns a connection timeout. This occurs because the Docker port for MinerU is not correctly mapped to the host machine, or FastGPT is not configured with the correct API address for MinerU.
How to Confirm Configurations Are Correct
- Upload a single image-format snack food quality inspection certificate, and check if the parsing result completely extracts the nutrition facts table and batch number. This confirms the
PARSE_IMAGE_ENABLEconfiguration is active. - Call the parsing API with a test document, and check if the returned chunk list retains the contextual association of raw material purchase details. This verifies the effect of the
chunkOverlapconfiguration. - Upload a test document exceeding the preset size, and check if the corresponding error is triggered. This confirms the
UPLOAD_FILE_MAX_SIZEconfiguration is active. - View the FastGPT parsing logs, and confirm that the table parsing mode is set to
structured, matching the configured setting.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.