What the Data for This Category Looks Like
Typical entities in this category are liquid milk, yogurt, milk powder, and dairy ingredient processing enterprises. Daily generated documents include raw milk acceptance records, standardized ingredient lists, pasteurization or UHT heat treatment records, CIP cleaning records, filling records, and microbial test reports. Data sources cover raw milk testing systems, MES production systems, homogenization and heat treatment equipment, online temperature and flow collection systems, CIP cleaning systems, and LIMS laboratory management systems. Data updates occur in real time per milk tank and each production batch. Documents include clear fields and units, such as raw milk antibiotic test values, heat treatment duration and temperature, and CIP cleaning parameters.
Constraints for Document Parsing and Chunking
Multi-source, heterogeneous document formats require the parsing workflow to support multiple types: paper scans, structured files exported from equipment, and PDF reports generated by laboratory systems. This avoids field loss caused by format incompatibility. Real-time, production-batch-based data updates require chunking to retain logical relevance for single-batch documents. Cross-process related content must not be split, for example the binding relationship between raw milk acceptance records and subsequent heat treatment records. Fields with specific industry units require chunking to preserve the correspondence between fields and values. Splitting indicators and their units must be avoided. Sequential document structures from continuous production require chunking to retain the production process timeline. This allows quick retrieval of full process traces during audits.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the field density of single-batch dairy documents, avoids splitting content across related fields |
chunkSplitter | Chunk by paragraph and semantic meaning | Retains logical relevance between raw milk acceptance and heat treatment records, adapts to sequential document structures |
customPdfParse | Enable structured parsing | Adapts to signed PDF test reports and CIP records, preserves original field layout |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Handles batch uploads of multiple milk tank documents, avoids parsing interruptions due to timeout |
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates complete batch-level filling and microbial test archive files |
chunkSettingMode | Manual configuration | Adjust chunking rules for different document types (equipment exported, paper scans) |
Three Common Mistakes
- Phenomenon: After uploading a raw milk acceptance PDF with a digital signature, the parsing result lacks antibiotic and physicochemical indicator fields. Cause: Structured parsing mode for
customPdfParseis not enabled. The default parsing cannot recognize form content wrapped in encrypted signatures. - Phenomenon: An upload failure error returns when calling the Doc2x tool to upload CIP cleaning records. Cause:
UPLOAD_FILE_MAX_SIZEis not adjusted to fit the size of batch-level archive files, or the file format exceeds supported ranges. - Phenomenon: The parsed knowledge base returns a reading link, but clicking it causes a page error. Or the large language model output does not include image content from microbial test reports. Cause:
customPdfParseis not configured to support PDF parsing with embedded images, orPARSE_FILE_TIMEOUT_SECONDSis set too short, causing image resources to fail to load completely.
How to Verify Correct Configuration
- Upload a single milk tank raw milk acceptance record PDF, check if the parsing result retains complete fields and corresponding values for antibiotics and physicochemical indicators.
- Batch upload filling and CIP record files from the same production batch, verify that parsing tasks do not experience timeout interruptions.
- Test parsing a signed microbial test report, confirm that no form fields are missing.
- Trigger knowledge base recall, check that chunked associated documents retain the sequential logic of the production process.
This page applies to FastGPT v4.17.0. The values provided are common starting points and should be measured against the reader's own samples.