What data for this category looks like
This category's data comes from formula systems, raw material batch records, internal mixers, Mooney and rheometers, vulcanizers, and laboratory testing equipment. Daily documents include formula sheets, weighing records, mixing curves, rubber material quick test reports, vulcanization process cards, mold records, and finished product performance reports. Data updates take place per truckload of rubber, per batch of raw materials, or per production cycle. Fields include mixing temperature (℃), vulcanization time (minutes), rubber batch number, mold number, and more. Some documents include structured tables and visualized curve data.
What constraints these characteristics impose on model access and configuration
The multi-source heterogeneous data format and high-frequency update rhythm require model access to support structured data parsing and low-latency calls, to avoid missing real-time ledger summary requirements due to delays. The feature where multiple documents are associated with a single production batch requires configuration items to adapt to multi-context merging processing, to prevent key process parameters from being truncated. Fields with specific units require the model to accurately identify and associate with corresponding business logic, to reduce output error rates. Severe error consequences require higher output stability from the configured model, and output randomness must be limited to avoid scrap risks caused by process parameter mismatches.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
temperature | 0.1–0.3 | This category's ledger summary requires accurate matching of process parameters. Low temperature values reduce output randomness and avoid process parameter mismatches |
maxContext | 8000–12000 token | Covers the total content of multiple documents for a single production batch (formula sheet, mixing curve, vulcanization card) to avoid context truncation and loss of critical data |
| Workflow/API tool selection | auto | Supports automatic calling of SQL generation tools to process structured ledger data, adapting to multi-source data summary requirements |
AIPROXY_API_ENDPOINT | Fill in the address provided by the selected domestic model service provider | Adapts to domestic model access needs that do not rely on OpenAI, aligning with actual usage scenarios |
maxTokens | 2000–3000 token | Limits the output length of single-batch report generation to avoid long text processing timeouts or format confusion |
vision | Enabled | Used to parse chart data in mixing curves and vulcanization process cards, extracting key process parameters |
Three common configuration mistakes
- Phenomenon: The generated SQL statement only removes line breaks without adding spaces, resulting in a syntax error "near consecutive characters" during execution. Cause: No text formatting rules were configured for the model output, only line break cleanup was performed.
- Phenomenon: When accessing a domestic model via the HTTP API, the returned result is empty or the status code is 403. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENwere not configured correctly, or the proxy address was not switched to a non-OpenAI one. - Phenomenon: When summarizing long-text ledgers, the model output is truncated and a complete report cannot be generated. Cause: The
maxTokensparameter was not adjusted to fit the output length of a single-batch report, or themaxContextsetting was too small, causing context overflow.
How to verify a successful configuration
- Test calls for single-batch mixing and vulcanization data, check whether the generated SQL statement includes necessary spaces and has no syntax errors.
- View the FastGPT v4.17.0 model call log, confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare correctly configured, with no connection errors. - Test merged input of multiple documents, check that the generated report is not truncated and the output length meets the summary requirements for single-batch production.
- Verify the tool call function, confirm that the model can automatically trigger SQL generation or content extraction adaptation logic, with no empty fields.
The values provided are common starting points and should be evaluated against local samples.