What data for this category includes
The daily documents for this category include formula sheets, weighing records, mixing curves, rubber compound quick tests, vulcanization process cards, mold records, and finished product performance reports. Data sources include formula systems, raw material batch ledgers, internal mixers, Mooney viscometers, rheometers, vulcanizers, and laboratory testing equipment. Data is updated after each raw material batch delivery, each production batch, and each molded product is finished. Document fields include specialized unit content such as rubber compound ratio parts, mixing temperature ranges, vulcanization time parameters, mold specifications, finished product tensile strength, and each single document is linked to full-process production data for the corresponding production batch.
What constraints these characteristics impose on model access and configuration
The heterogeneous formats of multi-source production documents require configuring unified document parsing rules to adapt to data sources with different structures such as formula sheets and mixing curves. The high-frequency updates by batch, production run, and mold count require configuring trigger logic that supports incremental synchronization to avoid performance loss from full data pulls. Process fields with specialized units require configuring semantic verification rules for parameter extraction to ensure that parameters output by the model match the units of production data. Bid documents must be linked to performance data for specific batches, so the context invocation scope must be limited to avoid interference from irrelevant data and ensure output accuracy. This configuration is based on the parameter logic of FastGPT v4.17.0.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers multiple mixing and vulcanization process documents linked to a single bid, avoiding information loss from context overflow |
temperature | 0.2–0.4 | Ensures accuracy of output for process parameters and formula data, reducing the proportion of irrelevant divergent content |
AIPROXY_API_ENDPOINT | Domestic compliant exclusive endpoint | Adapts to domestic large model call links, meeting requirements for localized processing of production data |
| Workflow/API tool selection | auto | Automatically selects appropriate tools to complete formula parameter extraction and bid document association matching |
quoteMaxToken | 1500–2000 token | Retains core content of performance reports and mixing curves for key production batches, ensuring integrity of referenced data |
Three Common Configuration Mistakes
- Symptom: Identity verification or connection timeout errors are returned when calling the embedding model. Cause: The index model adapted to this category’s production data is not configured separately, and the default cross-domain link is still used, resulting in restricted access to domestic data sources.
- Symptom: Request errors are returned when uploading PDFs of mixing curves or vulcanization process cards. Cause: Document parsing rules are not adjusted to adapt to the chart and parameter structure of this category’s documents, causing the document parsing service to fail to extract fields correctly.
- Symptom: Abnormal logs containing the
nodeIdfield are returned during chat interaction, with no valid bid document output. Cause: The range ofmaxContextis not configured correctly, resulting in too many production batch data linked to the context, triggering abnormal node calls.
How to Verify Successful Configuration
- Enter the mixing record for a specific batch and bid quotation requirements, check whether the document returned by the model links to the core production data of the corresponding batch.
- Upload a typical document of this category, verify that the parsed fields fully match the content of formula sheets and vulcanization process cards.
- View the model call log to confirm that the
AIPROXY_API_ENDPOINTlink is normal, with no abnormal records of identity verification failure. - Test the parsing and call process for a single document to verify that the parameters output by the model comply with the process specifications of this category.
The values given are common starting points and should be measured against the reader's own samples.