What the data for this category looks like
Data for packaging and printing intelligent due diligence reports comes primarily from independently submitted enterprise production ledgers, industrial and commercial public information, printing business qualification documents, raw material purchase records, monthly production capacity reports, and compliance test reports. There are two update cycles: production and operation data is updated monthly, while qualification and compliance data is updated annually. Document structures include both structured fields and unstructured attachments. Structured fields cover printing format (unit: millimeters), single-batch production capacity (unit: ten thousand color impressions), raw material inventory (unit: tons), and similar items. Attachments are mostly PDF-format test reports and order contracts.
What constraints these characteristics impose on model access and configuration
The multi-source, staggered update cycles, and mixed structure of packaging and printing due diligence data create three key constraints for model access and configuration. First, coexisting structured fields and unstructured attachments require parsing rules adapted to mixed formats, to avoid field parsing deviations. Second, differences in data update cycles require configured switching logic between incremental and full synchronization, to accommodate monthly and annual update cycles. Third, fields with dedicated units such as printing format and production capacity require unit recognition and calibration rules, to ensure the model correctly understands field meanings and numerical units. Additionally, parsing long document attachments requires adapted segment processing logic for PDF formats, to avoid content truncation.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_PDF_MODE | Multimodal Parsing + Structured Extraction | Packaging and printing due diligence reports include PDF-format qualification reports and order contracts. This mode can extract both text content and structured table fields |
chunkSize | 800–1200 characters | Production capacity reports and order details for packaging and printing are mostly long text segments. This range avoids model understanding errors caused by overly long single segments, while retaining context relevance |
similarityTopK | Top 6–8 entries | Due diligence reports require association of multi-dimensional data (such as production capacity, compliance, orders). Too many recalled entries increase context pressure, while too few will miss key associated information |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Packaging and printing compliance test reports are mostly multi-page PDF files with large individual file sizes. This setting supports large-file upload parsing |
SYNC_INTERVAL | 86400 seconds | Production and operation data is updated monthly. Daily synchronization ensures data timeliness while reducing server load |
maxContext | 12000–16000 characters | Due diligence reports require integration of multi-module data. This range can accommodate sufficient context information to support cross-dimensional correlation analysis for the model |
The parameter values provided on this page are all conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Phenomenon: Inconsistent model versions are used between the conversation interface and workspace applications. Cause: No binding rules for global models and application-specific models are configured, leading to different scenarios calling different model versions.
- Phenomenon: The service can be accessed normally via the intranet address, but the third-party proxy address returns a 403 status code. Cause: No cross-domain request header rules are configured, or the proxy address is not correctly bound to the FastGPT API entry.
- Phenomenon: The console reports
failed to get gpt-3.5-turbo token encoderafter startup. Cause: The model's API key and interface address are not configured correctly, or dependency package version incompatibility causes token parsing failure.
How to confirm the configuration is complete
- Upload a single packaging and printing compliance test report, check if the parsed structured fields include dedicated information such as printing format and production capacity, and verify that field units match the preset rules.
- Initiate a due diligence analysis request, check if the returned result associates multi-dimensional data, and confirm that the number of recalled context entries matches the expected configuration.
- Simulate an incremental synchronization task, check if only the current month's production and operation data is updated, and previously synchronized annual qualification files are not reprocessed.
- Call the API interface for testing, check if the returned model response matches the model parameters configured in the interface, and confirm model consistency across scenarios.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.