What Data Looks Like for This Category
Typical entities in this category are manufacturers of air conditioners, refrigerators, washing machines, and small household appliances. Daily generated documents include configuration BOMs, assembly work instructions, refrigerant charging records, electrical safety test reports, performance spot check reports, and repair records. Data sources include ERP/MES systems, component batch ledgers, tightening and charging equipment logs, safety tester data, performance laboratory reports, and after-sales fault databases. Data updates run per individual product, shift production batch, and spot check schedules. Fields across all documents include model identifiers, component numbers, test parameters, fault types, and more. Units cover professional parameters such as voltage, flow rate, and energy efficiency rating.
Constraints Imposed on Model Access and Configuration
Multi-source heterogeneous real-time data requires models to quickly integrate field information from different systems. This prevents mismatches between quotations in bid documents and actual production parameters caused by data silos. Per-unit update frequency means models must handle high-frequency changing component batch data. Context windows must cover sufficient historical data to ensure quotation accuracy. Compliance documents such as safety and performance test reports require bid content output by models to strictly match measured data in the documents, with no parameter deviations. High-volume mixed-line production requires models to accurately distinguish BOMs and work requirements for different models, avoiding model mix-ups in documents.
How to Set Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Bid documents need to integrate multiple BOMs, safety reports, and spot check data. This range covers complete associated document content |
maxTokens | 15000–20000 token | Complete bid documents include product introductions, quotation details, compliance explanations, and other modules. This range ensures generated content is not truncated |
quoteMaxToken | 3000–4000 token | The quotation section needs precise calculation of component costs and batch prices. Limiting this length avoids redundant content that affects quotation accuracy |
temperature | 0.2–0.4 | Bid and quotation content must remain rigorous. A lower temperature parameter reduces random deviations and ensures data matching |
| Workflow/API tool selection | auto | Allow the model to automatically call tools to obtain real-time component batch data, adapting to quotation requirements updated per production batch |
AIPROXY_API_TOKEN | Enterprise-specific encrypted token | Used for secure transmission of sensitive production data such as safety and after-sales information, preventing data leaks |
Three Common Mistakes
- Insufficient number of results returned by the workflow issue classification module. This occurs because the
maxContextparameter was not adjusted, so full model-related documents cannot be loaded, and classification dimensions cannot be accurately identified. - Interface authorization failure when attempting to connect MCP functionality in the workflow. This occurs because the
AIPROXY_API_TOKENparameter was not filled correctly, orAIPROXY_API_ENDPOINTwas not configured as the enterprise’s internal compliant proxy address. - A browser not supported for speech input prompt appears after enabling speech recognition. This occurs because cross-domain speech transmission permissions were not enabled in the FastGPT v4.17.0 frontend configuration, or the current browser was not granted microphone access permissions.
How to Verify Successful Configuration
- Upload a specified model’s air conditioner BOM and safety test report, and check if the generated bid document correctly references the corresponding model’s component numbers and test parameters.
- Trigger an MCP plugin call in the workflow, and confirm that the returned results include preset fields such as component batch and production time, with no missing or incorrect entries.
- Adjust the
temperatureparameter to 0.3, then generate 3 quotation texts for the same product model consecutively. Check that the content maintains consistent rigor with no obvious random differences. - Enable speech input functionality in the FastGPT v4.17.0 test environment, use a browser that supports speech recognition to enter commands, and confirm no browser compatibility errors occur.
The values provided are common starting points and should be measured against the reader's own samples.