What data for this category looks like
The data for this category mainly comes from customer surface treatment specifications, bath chemistry test records, rectifier operation data, online water quality monitoring results, and hazardous waste management ledgers. Daily documents include bath analysis records, process parameter cards, fixture usage logs, film thickness and adhesion reports, hazardous waste ledgers, and wastewater monitoring records. Data update frequencies cover per bath run, per shift, and fixed monitoring cycles. Most documents contain fields such as process parameters, test values, and compliance records. Some documents include test charts and parameter range requirements.
Constraints imposed on document parsing and chunking by these characteristics
Data sources for this category are diverse, and document formats include structured parameter cards, semi-structured monitoring reports, and reports with charts. This requires the parsing module to adapt to multiple types of document structures. Frequently updated documents require fast parsing capabilities, and timeout settings must match the processing rhythm of daily documents. Documents related to hazardous waste and wastewater include compliance fields. When chunking, it is necessary to retain field associations, and avoid splitting that breaks the binding between parameters and compliance records. Some documents bind process steps to test results, so chunking must balance context integrity to ensure that corresponding processes and test data can be linked during Q&A.
Configuration settings
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Most documents in this category contain continuous process parameters and test values. This range preserves the association between parameters and their context |
chunkSplitter | Split by paragraph + title | Process documents are divided into sections by process steps and test items. Retaining title associations improves the accuracy of information during Q&A |
customPdfParse | Enabled | PDF documents in this category often include embedded test tables and charts. Enabling custom parsing preserves table structure and chart annotations |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing a single hazardous waste ledger or batch bath record documents takes a long time. This duration covers most business scenarios |
chunkSettingMode | Manual configuration | Document structures vary widely across this category. Manual adjustment allows adaptation to different document types such as process parameter cards and monitoring reports |
Three common misconfigurations
- After calling the knowledge base creation API, no parsing in progress, ready, or failed status returns are received. The cause is that the API callback address is not configured or the status push function is not enabled, so real-time parsing progress cannot be obtained.
- After uploading a PDF process document with embedded test charts, the parsed results lose the association between the charts and their corresponding parameter values. The cause is that the
customPdfParseconfiguration is not enabled. The default parsing module cannot adapt to the table and chart structures of documents in this category. - When uploading multiple hazardous waste ledger documents in a single batch, a parsing timeout error is triggered. The cause is that
PARSE_FILE_TIMEOUT_SECONDSis set too low, and does not cover the actual parsing time of batch documents.
How to confirm the configuration is properly set
- Upload a typical process parameter card PDF document, check the parsed text structure, and confirm that titles and their corresponding paragraphs are not incorrectly split.
- Call the knowledge base parsing status query interface, and verify that standardized parsing status fields can be returned.
- Upload a hazardous waste ledger document that includes embedded tables, and check that the parsed results retain the table's row and column structure and numerical content.
- Adjust the
chunkSizeconfiguration, upload the same document, and compare chunking results across different configurations to confirm that the expected context retention effect is achieved.
The values provided are common starting points and should be measured against the reader's own samples.