What this category's data looks like
Data sources for dairy product intelligent due diligence include upstream dairy farm raw milk test reports, dairy enterprise production logs, third-party quality inspection spot check reports, and industry association compliance reference documents. Update frequency varies by scenario: raw milk batch tests are updated daily, production logs are updated in real time alongside production processes, and third-party spot check reports and industry statistics are updated monthly or quarterly. Document formats include structured CSV/Excel tables (such as bulk quality inspection data), PDF reports with embedded tables, and standardized qualification documents. Core fields include batch identifiers, collection/production timestamps, milk fat percentage, milk protein percentage, total bacterial count, somatic cell count, and more. Corresponding units use fixed formats such as percentage, CFU/mL, and 10,000/mL.
What constraints do these characteristics impose on the document parsing and chunking link
The multi-source structured features of dairy due diligence documents require the parsing link to accurately identify fields and their corresponding units, to avoid unit confusion that invalidates subsequent data. Bulk quality inspection CSV/Excel files often contain tens of thousands of entries, so large file sharded parsing must be supported to avoid timeouts or out-of-memory errors. Format differences across source documents are significant: field layout and table nesting logic differ between farm test reports and dairy enterprise production logs, so extraction of structured content across multiple formats must be supported. PDF reports with cross-page embedded tables require cross-page content stitching, otherwise chunking will split complete data entries. Frequently updated batch data requires the parsing link to support content splitting by batch dimension, to ensure logically consistent data after chunking.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500–1000 MB | Bulk quality inspection Excel/CSV files for dairy due diligence often reach hundreds of MB, so this accommodates large file upload requirements |
PARSE_TABLE_STRICT_MODE | Enabled | Dairy data fields are fixedly bound to units, strict mode prevents misidentification of non-standard table structures |
CHUNK_SIZE | 800–1200 characters | A single dairy batch data entry includes test values and explanatory text, this length preserves complete context |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Parsing large bulk files requires longer processing time to avoid mid-run interruptions |
ENABLE_TABLE_CHUNK_SPLIT | Enabled by business field group | Structured tables must be split into chunks by business dimensions such as milk fat, total bacterial count, etc., to ensure data logical integrity |
RECOGNIZE_UNIT_AUTOMATICALLY | Enabled | Dairy data includes exclusive units such as CFU/mL, 10,000/mL, automatic recognition prevents loss of unit information |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Issue: Fields are empty after parsing CSV files, or table columns cannot be identified. Cause: Table parsing configuration is not enabled, or the CSV file delimiter is not correctly specified, causing the system to fail to match the structured data format.
- Issue: A
408 Request Timeouterror is returned when parsing large Excel files. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and the default timeout duration is insufficient to complete parsing of large-volume dairy quality inspection data. - Issue: A single batch data entry is split into multiple independent chunks after chunking, leading to broken context during subsequent retrieval. Cause:
ENABLE_TABLE_CHUNK_SPLITis not configured by business field group, and direct splitting by fixed length does not preserve data logical associations.
How to confirm configurations are correct
- Upload a standard dairy quality inspection CSV file, check the parsed field list to confirm all exclusive fields and units are correctly identified.
- Upload a single file with a volume close to the
UPLOAD_FILE_MAX_SIZElimit, verify that the parsing process does not experience timeout interruptions. - Randomly select a single batch data entry, confirm that its complete content is contained within a single chunk and is not split apart.
- Adjust the
CHUNK_SIZEparameter, compare the length changes of chunking results to confirm that the configuration takes effect.
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.