What the Data for This Category Looks Like
Core entities in dairy processing include liquid milk, yogurt, milk powder, and dairy ingredient enterprises. Daily production ledgers cover raw milk acceptance records, standardized ingredient lists, pasteurization or UHT heat treatment records, CIP cleaning records, filling records, sample retention and microbiological test reports. Data sources include dairy source testing systems, MES production management systems, homogenization and heat treatment equipment, online temperature and flow monitoring devices, CIP systems, and LIMS laboratory information management systems. Update frequency follows per milk tank or per batch; some device parameters update in real time. Documents include quantitative test values, equipment operating parameters, batch numbers, timestamps and other metadata, with most fields attached with corresponding units.
What Constraints Do These Characteristics Impose on the Document Parsing and Chunking Link?
The ledger characteristics of dairy processing impose multiple constraints on the document parsing and chunking link. First, the volume of real-time updated single-batch documents is small but batches are dense. Unlike solid baking where batches can be isolated individually, liquid dairy raw materials are perishable, and abnormalities in a single batch have a wide impact. Chunking must strictly distinguish batches to ensure each chunk corresponds to the complete logic of a single production batch. Second, documents contain a large number of quantitative fields with units. Chunking must retain the association between fields and units, and must not destroy parameter integrity due to splitting. Third, ledgers come from diverse sources: there are structured CSVs exported by equipment, as well as fixed-format PDFs generated by LIMS. The parsing link must adapt to multiple document formats. Finally, real-time updated production data requires that the parsing process not be overly long, and overall time consumption must be controlled to match production rhythms.
How to Configure the Settings
These configurations are written based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the total content of single-batch production ledgers, avoids splitting that breaks single-batch business logic, and matches the context window requirements of large language models |
chunkSplitter | Chunk by paragraph + semantics | Matches the structured paragraphs of dairy product ledgers, such as the sub-item detection paragraphs of raw milk acceptance, and retains the complete association between fields and units |
customPdfParse | Enabled | Adapts to formats such as micro-test reports exported by LIMS and PDF records generated by equipment, retains original typesetting and field positions, and avoids data loss after parsing |
PARSE_FILE_TIMEOUT_SECONDS | 60–90 seconds | Adapts to the parsing duration of single-batch documents, avoids failure to import real-time updated batch data due to timeout |
chunkSettingMode | Manual configuration | Adjust chunking rules for documents from different sources (such as equipment CSV, system PDF) to adapt to the diverse structural characteristics of dairy product ledgers |
Three Common Misconfigurations
- Phenomenon: The interface becomes unresponsive or returns a parsing failure prompt when attempting to enable PDF enhanced parsing. Cause: The
customPdfParseparameter is not configured correctly, or the custom parsing module adapted to the dairy product ledger format is not deployed. - Phenomenon: Chunking results contain ledger content across multiple production batches, and some fields and units are lost during splitting. Cause: The
chunkSizesetting is too small, or semantic chunking is not enabled, which destroys the business logic association of a single batch. - Phenomenon: An error indicating that the
/v2/parse/filerelated code is not found is returned when calling the file parsing interface. Cause: The custom PDF parsing module of the corresponding version is not deployed, or the configuration parameters of the parsing plugin are not correctly associated.
How to Confirm the Configuration Is Correct
- Upload a standard raw milk acceptance PDF record, check whether the parsed text completely retains fields such as antibiotic test values, sampling time and their corresponding units.
- Upload a single-batch CIP cleaning record, verify that the parsing duration is lower than the configured value of
PARSE_FILE_TIMEOUT_SECONDS, and no timeout error occurs. - View the chunking result list, confirm that each chunk only contains ledger content of a single batch, with no cross-batch splicing.
- Call the FastGPT file parsing API interface, upload a standardized ingredient list, and confirm that the returned chunked data length falls within the configured range of
chunkSize.
The values provided here are common starting points and should be evaluated against the reader's own samples.