Dairy Processing Equipment Inspection and Maintenance Q&A: Document Parsing and Chunking

What Data for This Category Looks Like

Data sources for this category include milk source testing, production MES systems, homogenization and heat treatment equipment, online temperature and flow monitoring, CIP cleaning systems, and LIMS laboratory management systems. Daily documents cover raw milk acceptance records, standardized batching sheets, pasteurization or UHT heat treatment records, CIP cleaning records, filling production records, sample retention records, and microbial test reports. Data is generated per milk tank, per batch, or in real time. Most fields include numerical parameters and batch identifiers, with units for professional production indicators such as temperature, flow rate, time, and colony count.

Constraints Imposed by These Characteristics on Document Parsing and Chunking

Real-time batch data for dairy processing requires document parsing to respond quickly, to avoid delaying equipment inspection and maintenance Q&A for production links. Multi-source heterogeneous device logs and quality inspection reports require chunking logic to adapt to different document structures, to avoid mixing fields across documents. Frequently updated batch data must avoid chunk overlap or truncation that causes loss of critical information. It must also adapt to bulk Excel-format records to ensure single batch data is fully chunked for accurate retrieval. Production data for perishable raw materials has strong timeliness; excessive parsing timeout will reduce the availability of real-time Q&A.

Configuration Settings

This document is based on configuration instructions for FastGPT v4.17.0.

Three Common Misconfigurations

• After uploading Excel-format production records, the chunking result merges multiple rows, and single batch records are truncated. The symptom is that the knowledge base cannot accurately match single batch data during retrieval. The cause is that chunkSplitter is not configured to split by line; the default chunking logic does not adapt to the row-level data structure of Excel.
• When uploading Word-format heat treatment records larger than 10 MB, the parsing process takes too long and exceeds business response requirements. The symptom is that the interface shows parsing timeout or task failure. The cause is that the PARSE_FILE_TIMEOUT_SECONDS parameter is not adjusted to a reasonable range, and bulk parsing optimization is not enabled.
• After importing structured PDF-format CIP cleaning records, some fields are empty or identified incorrectly. The symptom is that retrieval results cannot match specific parameters in the document. The cause is that the customPdfParse function is not enabled, so table fields in structured documents cannot be extracted correctly.

How to Verify Correct Configuration

• Upload a single batch of Excel test file, check if the chunking result shows one independent record per row, with no cross-row merging.
• Upload a Word-format heat treatment record larger than 10 MB, verify if the parsing time falls within the range preset by PARSE_FILE_TIMEOUT_SECONDS.
• Import structured PDF-format CIP cleaning records, confirm that the field hierarchy matches the original document, with no critical information lost.
• Check the knowledge base upload file limit settings, try uploading a test file matching the UPLOAD_FILE_MAX_SIZE value, and verify that the upload and parsing processes operate normally.

The values provided here are common starting points, and should be evaluated against the reader's own samples.