Data Characteristics of This Category
Inspection and process records for dairy processing cover end-to-end documents from liquid milk, yogurt, milk powder, and dairy ingredient enterprises. Core documents include raw milk acceptance records, standardized ingredient lists, pasteurization/UHT heat treatment records, CIP cleaning records, filling records, reserved sample and microbial test reports. Data sources include milk source testing systems, production MES systems, homogenization and heat treatment equipment, online temperature and flow collection systems, CIP systems, and LIMS laboratory management systems. Records are generated in real time for each milk tank and each production batch. Document structures vary significantly by process step, with fields including batch identifiers, test values, timestamps, equipment operating parameters, and more. Some paper-based scanned documents have table alignment deviations.
Constraints Imposed on Document Parsing and Chunking
The multi-source nature, high-frequency updates, and varied document structures of this category create multiple constraints for the parsing and chunking workflow. Mixed data sources from multiple systems require parsing tools to support both spreadsheets and scanned documents, to avoid missing cross-system batch-related information. The high real-time update frequency requires chunking rules to adapt to small-batch, high-frequency imports, to reduce parsing time per individual document. Varied document structures require chunking logic to retain contextual connections between fields, to prevent splitting raw milk antibiotic test values and their corresponding batch numbers into different chunks. Format deviations in paper scanned documents require enhanced parsing capabilities to fix table alignment errors, to ensure the accuracy of test data.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the field density and associated length of dairy processing documents, avoids splitting critical test values and batch information |
chunkSplitter | Chunk by paragraph + semantic meaning | Adapts to structural differences across document types such as raw milk acceptance records and heat treatment records, retains cross-field business associations |
customPdfParse | Enabled | Adapts to paper-scanned CIP and filling records, uses enhanced parsing to extract tables and handwritten fields |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing duration of bulk raw milk acceptance records, avoids timeout failures for large batch documents |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports bulk uploads of full production records, adapts to enterprises' high-frequency batch data imports |
chunkSettingMode | Manual configuration | Adjusts chunking rules for different document types such as microbial test reports and ingredient lists, matches business data association logic |
Three Common Configuration Mistakes
- Scenario: Enabling enhanced PDF parsing fails to properly extract table fields from CIP records. Cause: No adjustment was made to the adaptation parameters for
customPdfParsefor dairy processing scanned documents, leading to failed parsing of low-resolution tables. - Scenario: Knowledge base chunking splits raw milk antibiotic test values and their corresponding batch numbers into different chunks. Cause:
chunkSizeis set too small, interrupting cross-field associated information and failing to retain complete inspection logic. - Scenario: A timeout error triggers when bulk uploading raw milk acceptance records. Cause:
PARSE_FILE_TIMEOUT_SECONDSis set lower than the actual parsing duration of bulk documents, leading to early task termination.
How to Verify Proper Configuration
- Upload a typical raw milk acceptance record PDF, check that the parsed chunks contain complete batch numbers, antibiotic test values, and acceptance times.
- Navigate to the knowledge base settings page in FastGPT v4.17.0, confirm that the
customPdfParsetoggle is enabled, and check if the enhanced parsing logs show successful table extraction. - Upload a single bulk production record under 100 MB, wait for parsing to complete, check for timeout errors, and confirm that the
PARSE_FILE_TIMEOUT_SECONDSsetting is appropriate. - Test chunking effects for different document types such as microbial test reports and CIP records separately, confirm that the manual
chunkSettingModeconfiguration takes effect, and that the chunking logic matches the structural characteristics of the corresponding documents.
The values listed above are common starting points, and should be evaluated against your own specific samples.