What Data Looks Like for This Category
Core data for this category comes from formula and process libraries, raw material batch ledgers, reactor DCS systems, weighing record systems, LIMS laboratory management systems, and warehouse management systems. Data updates follow each production batch cycle, with immediate synchronization after completion of key reaction steps. Daily documents include batch production instructions, feeding verification sheets, reaction process records, cleaning confirmation sheets, finished product inspection reports, deviation and change notices. Most document fields include unit-bearing process parameters such as batch number, feeding amount, reaction temperature, and residence time. Single documents typically use a mixed format of structured forms and unstructured records.
Constraints Imposed on Document Parsing and Chunking
The multi-batch switching production mode creates differences in document formats for individual production batches. Content must be split accurately by batch and step dimensions to avoid mixing data across batches. Unit-bearing process parameters are scattered across structured forms and unstructured records. During chunking, retain the association between parameters and their corresponding processes to prevent field breakage. Frequent batch-level data updates require reasonable timeout settings for the parsing process, to avoid task interruptions caused by overly long single documents. Some sensitive documents also require custom parsing rules to ensure sensitive information processing aligns with internal management requirements.
Configuration Settings
The following configurations are based on FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the mixed length of structured forms and unstructured records in documents of this category, preventing single chunks from containing content across processes |
chunkSplitter | Split by paragraph + semicolon | Matches the document format where process steps act as paragraphs and parameter items are separated by semicolons |
customPdfParse | Enable custom parsing rules | Adapts to structured forms with fixed headers in documents of this category, extracting core fields including batch number and feeding amount |
UPLOAD_FILE_MAX_SIZE | 500 MB | Meets the file size requirement for complete production records of a single batch, preventing upload failure due to oversized files |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to parsing time for multi-step documents, preventing parsing failure due to timeout for complex forms |
chunkSettingMode | Manual configuration | Flexibly adjust chunking parameters to address the differentiated formats of single-batch documents |
Three Common Configuration Errors
- Uploaded documents show parsing failure, with connection timeout errors in logs. The cause is failure to adjust the
PARSE_FILE_TIMEOUT_SECONDSparameter; parsing time for complete production records of a single batch exceeds the default threshold. - Parsing tool calls fail after local Docker deployment. The cause is failure to map the parsing service port to the local machine, or failure to specify the local parsing service address in the configuration.
- The correspondence between process parameters and their processes is broken in parsing results. The cause is an overly large
chunkSizesetting, merging content across processes into the same chunk and causing lost association relationships.
How to Verify Proper Configuration
- Upload a single typical batch production record, check the completion status of the parsing task, and confirm no timeout or connection errors occur.
- Extract core process parameters from the document, and verify that the binding relationship between parameters and their corresponding processes in the parsing results is complete.
- View the chunked text generated after parsing, confirm that each chunk’s length falls within the preset
chunkSizerange, and no mixed content across batches or processes is present. - Test the enabled status of custom parsing rules, and confirm that extraction or shielding functions for sensitive fields work as expected.
The values provided here are common starting points, and should be evaluated against the reader's own samples.