What the Data for This Category Looks Like
Production ledger data for batch fine chemical synthesis comes from formula and process libraries, raw material batches, reactor DCS systems, weighing systems, LIMS, and warehouse systems. Update frequency is per batch or each critical step. Daily documents include batch production instructions, feeding review sheets, reaction process records, cleaning records, inspection reports, and deviation and change orders. Fields cover batch number, feeding amount (kg/L), reaction temperature (℃), reaction duration, raw material batch number, and more. Some documents are structured forms with fixed headers, while others are handwritten or scanned process records.
What Constraints Do These Characteristics Impose on the Document Parsing and Chunking Link
The multi-variety, multi-batch switching business characteristic requires chunking to anchor on batch number as the context boundary, to avoid mixing content across batches. Different document types have large field differences, so mixed parsing of structured forms and unstructured records must be supported. General fixed chunking rules cannot be used. The high-frequency single-batch update requirement means the parsing and chunking process must support fast loading of single-batch associated documents, while retaining associations between fields. This ensures correct data aggregation during subsequent ledger summary. The business requirement of formula confidentiality requires that the parsing process prevents unauthorized access to sensitive formula fields during chunking.
How to Set the Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Fine chemical ledger pages have high content density. Each chunk must cover a complete feeding or reaction step to avoid splitting key fields |
chunkSplitter | Separate by batch number + document type | Different batches and document types have large structural differences. Anchor chunk boundaries with identifiers to retain context association |
customPdfParse | Enabled | Most ledgers are scanned documents or formatted production records. Custom parsing supports fixed header field extraction |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | A single batch ledger may include multiple associated documents, requiring sufficient time for full parsing |
UPLOAD_FILE_MAX_SIZE | 50 MB | Limit the size of a single ledger file to avoid parsing overload during batch uploads |
chunkSettingMode | Manual configuration | Chunk rules must be adjusted for different document types; automatic mode cannot adapt to multi-format ledgers |
Three Common Misconfigurations
- Phenomenon: Empty content or incomplete fields are returned when calling the API to retrieve chunk indexes. Cause: The batch number is not used as the chunk anchor point, so the chunk boundary does not match the batch logic of the ledger, and corresponding data cannot be associated.
- Phenomenon: Timeout errors occur when parsing multiple associated ledgers. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and the default timeout duration is insufficient for batch parsing of multiple documents. - Phenomenon: Chunk results mix production records from different batches. Cause: The
chunkSplitteris not configured to separate by batch identifiers. The default chunking rule splits by character length and does not retain batch context.
How to Confirm the Configuration Is Set Correctly
- Upload a single batch's feeding review sheet and reaction process records, then check if chunk results are aggregated by batch number, with each chunk containing complete fields for a single record.
- Adjust the
chunkSizeparameter, re-upload the test document, and compare whether the chunk length matches the preset character range. - Simulate a batch upload of multiple documents, then verify that the parsing progress completes within the duration set by
PARSE_FILE_TIMEOUT_SECONDS. - Call the FastGPT v4.17.0 API to retrieve the chunk index, then confirm that the returned index information includes key identifier fields such as batch number and document type.
The values given are common starting points and should be measured against the reader's own samples.