What this category of data looks like
This category of data comes from multi-system outputs from cross-departmental change management teams of manufacturing enterprises, including PLM, ERP, MES, supplier materials, verification test records, and customer special requirement documents. Daily documents cover ECN/ECR, change assessment forms, drawings and BOM version records, process verification reports, production switching plans, inventory disposal documents, and customer approval documents. Data updates are triggered irregularly with each change. Documents contain core fields such as version identifiers, effective dates, material codes, and change impact scope.
What constraints do these characteristics impose on document parsing and chunking
Heterogeneous data sources from multiple systems lead to differences in document formats, requiring adaptation of parsing rules for different file types. Irregular change updates require chunking configurations to support incremental synchronization, avoiding repeated parsing of full historical data. The size of individual documents varies widely, such as long process documents or multi-row and multi-column BOM tables, requiring precise control of chunk granularity to prevent context breaks or information redundancy. Core fields associated with changes must be accurately anchored, and field association relationships must be retained during chunking to avoid separating change instructions from corresponding verification records.
How to set the configurations
The following configurations are based on the functional logic of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the field density and context association length of manufacturing change documents, avoids single chunks being too large to exceed the vector model's support range, and reduces inter-chunk redundancy |
chunkSplitter | Semantic + fixed-length hybrid | Balances the splitting needs of structured fields such as BOM rows and unstructured paragraphs such as change descriptions in change documents, avoiding semantic breaks caused by forced fixed-length splitting |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Reserves sufficient time for parsing and chunking when processing 100,000-character Chinese documents or 15,000-row Excel data, avoiding task interruptions due to timeouts |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Meets the upload requirements for large-volume files such as large process verification reports and complete BOM lists, adapting to full-document synchronization scenarios for change management |
chunkSettingMode | Custom mode | Allows configuration of differentiated chunking rules for different types of change documents such as ECN/ECR, BOM tables, and process reports, improving parsing accuracy |
customPdfParse | Enable structured parsing | Accurately extracts key information such as drawing annotations and version identifiers in PDF format, avoiding loss of structured fields during plain text parsing |
Three common configuration mistakes
- Symptom: Empty chunks or missing fields are returned when parsing 15,000-row Excel data. Cause: Custom chunking mode is not enabled, and the default chunking logic cannot adapt to the row-level association of structured tables, leading to separation of cross-chunk material codes and change instructions.
- Symptom: Timeout errors are triggered when parsing large process documents. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted to a reasonable range, and the default short timeout setting is used, which cannot complete complete parsing of large-volume documents. - Symptom: Too many irrelevant historical change contents appear in retrieval results, and the granularity does not meet business requirements. Cause: The default
chunkSizesetting is used, the chunk length is too large, and a single chunk contains multiple independent change nodes, leading to redundant information being hit during retrieval.
How to verify the configuration is correct
- Upload a single 100,000-character Chinese document or 15,000-row Excel data, check the parsing progress and chunking results, and confirm there are no timeout or parsing failure logs.
- Randomly select chunks after splitting, verify that core fields such as version identifiers and effective dates are fully retained, with no breaks or losses.
- Adjust the
chunkSizeparameter, compare the chunking effects under different values, and select the configuration suitable for the business scenario. - Upload multiple types of change documents, confirm that the adaptation effect of structured parsing and chunking rules meets expectations.
The values provided are common starting points and should be measured against the reader's own samples.