What the data for this category looks like
The data for this category comes from daily production documents of liquid milk, yogurt, milk powder, and dairy ingredient enterprises. These documents include raw milk acceptance records, standardized ingredient lists, pasteurization or UHT heat treatment logs, CIP cleaning records, filling records, and sample retention and microbial inspection reports. Data sources cover milk source testing, MES systems, homogenization and heat treatment equipment, online temperature and flow collection modules, CIP systems, and LIMS laboratory management systems. Update frequency triggers per milk tank batch or in real time. Each individual document includes batch identifiers, timestamps, and corresponding testing or operating parameter fields.
What constraints these characteristics impose on the "document parsing and chunking" link
The real-time or batch-based update nature of this category requires document parsing and chunking to match the integrity of single-batch data, and avoid mixing content across batches. Multiple types of production documents contain structured tables and time-series operating parameter text. This requires parsing modules to accurately extract table fields and time-series data. Field structures vary significantly across different documents, so support for custom parsing rules is needed to adapt to different document formats. At the same time, production data is associated with compliance and safety risks, liquid biological raw materials are perishable, and production processes run continuously without batch-by-batch isolation. Parsing results must retain the contextual association of key parameters, and avoid losing the correspondence between batches and inspection items after chunking.
This document is written based on the configuration instructions for FastGPT v4.17.0.
How to set the configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the mixed content of this category’s documents, which includes both long time-series operating records and structured inspection data, and avoids single chunks that are too long or too fragmented |
chunkSplitter | Split by paragraph + heading | Matches the structure of production documents divided by process and batch, and retains the parameter association of the same process |
customPdfParse | Enable custom parsing rules | Adapts to non-standard format production documents such as raw milk acceptance records and CIP cleaning records, and accurately extracts key fields such as batch numbers and inspection values |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses large-volume real-time-collected device log files, and avoids losing batch data due to parsing timeout |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports batch uploads of complete single-batch device operating logs and multiple associated documents |
chunkSettingMode | Manual configuration | Adjust chunking parameters for different document types such as microbial inspection reports and filling records, to ensure contextual integrity of key data |
Three common mistakes
- The batch field is empty after parsing, or content is mixed across batches. Cause: The
customPdfParsecustom parsing rule is not enabled, and the extraction logic for batch identifiers of production documents is not configured. - A timeout error (status code 504) is returned after the parsing task is triggered. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and the parsing time of large-volume device operating logs exceeds the default threshold. - A single chunk after chunking contains parameters from multiple different processes. Cause:
chunkSplitteris configured to split by fixed characters, and is not split by process headings or paragraphs, leading to mixing of cross-process data.
How to confirm the configuration is correct
- Upload a standard raw milk acceptance record PDF, check if the parsed text completely extracts key fields such as raw milk source and antibiotic inspection values, to verify that the
customPdfParseconfiguration takes effect. - Upload a single-batch device operating log file, check if the parsing time matches the setting of
PARSE_FILE_TIMEOUT_SECONDS, with no timeout errors. - Review the chunked results, confirm that the content in each chunk belongs to the same process or the same batch, to verify the rationality of the
chunkSplitterandchunkSizeconfigurations. - Check that files pushed by external systems are correctly stored in the upload directory specified by FastGPT, and are in supported text formats such as PDF and DOCX. Audio and video file parsing is not supported at this time.
The values provided here are common starting points, and should be evaluated against the reader's own samples.