What Data for This Category Looks Like
Data for this category comes primarily from online composition analysis systems, DCS distributed control systems, laboratory records, kiln and grinding equipment ledgers, raw material inventory ledgers, and energy metering devices. Daily stored documents include raw material batching plans, rotary kiln operation logs, clinker test reports, quality control notices, equipment inspection forms, and energy consumption reports. Data update frequencies include real-time (such as kiln temperature and material composition data), shift-based (such as inspection records), and daily (such as quality statistical reports). Document fields for raw material and firing workshops cover material ratios, thermal parameters, strength indicators, equipment operating hours, and more. Units include tons, degrees Celsius, megapascals, kilowatt-hours, and others.
Constraints Imposed by These Characteristics on Model Access and Configuration
The multi-source, multi-update-frequency business data for this category requires model access to support loading logic matched to data types. This prevents real-time kiln temperature data from being overwritten by batch reports. Professional documents include segmented fields with units such as raw material ratios and clinker strength. Parameter filtering must be configured to remove irrelevant content and reduce token consumption. Equipment inspection and maintenance Q&A must link to inspection records and thermal system data from the past seven days. The context window must adapt to the length of batch historical data. Business errors directly lead to production shutdowns and quality returns. Retry and downgrade logic for failed calls must be configured to avoid Q&A interruptions affecting on-site processing.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers inspection records, kiln operation logs, and quality reports from the past seven days, meeting historical data association requirements for equipment inspection |
maxTokens | 2000 token | Limits the output length of single-round maintenance guidance, adapting to quick reading scenarios during on-site inspection |
quoteMaxToken | 3000 token | Sets a token limit for referencing business documents, only loading test reports and equipment ledger content related to the current inspection issue |
| Workflow/API tool selection | auto | Automatically triggers tool calls to retrieve real-time online data such as kiln temperature and material ratios, adapting to dynamic inspection scenarios |
temperature | 0.2–0.4 | Reduces output randomness, ensuring maintenance plans comply with professional cement production specifications |
AIPROXY_API_TOKEN | Enter the exclusive key applied for by the enterprise | Verifies call permissions, preventing unauthorized access to production-related data |
AIPROXY_API_ENDPOINT | Fill in according to the enterprise's deployment environment | Adapts to internal network access restrictions, ensuring transmission security of production data |
This page is written based on the configuration logic of FastGPT v4.17.0.
Three Common Configuration Mistakes
- Symptom: A 429 error is returned when calling the model, with the message "Current group upstream load is saturated". Cause: No reasonable request throttling strategy is configured, and too many equipment inspection and maintenance Q&A calls are initiated simultaneously, exceeding the load limit of the model service.
- Symptom: After reconfiguring the reranking model, the FastGPT interface displays an Invalid URL (POST /v1/rerank) error. Cause: The API endpoint and key for the reranking model are not filled in correctly, or the port of the deployed reranking model does not match the FastGPT proxy configuration.
- Symptom: After uploading documents such as raw material batching plans and inspection records, the Q&A results do not reference the uploaded document content. Cause: The document association context configuration item is not enabled, or the fields of the uploaded document are not correctly parsed into input recognizable by the model.
How to Confirm Correct Configuration
- Submit a test Q&A about rotary kiln scaling inspection, verify whether the output content links to preset historical inspection records and thermal data.
- Simulate multiple concurrent Q&A requests, observe whether a reasonable current limiting strategy is triggered, and no load exceeding errors occur.
- Upload a raw material test report, submit a Q&A about strength adjustment related to this report, verify whether the output references specific parameters from the uploaded document.
- Check the API endpoint and key configuration items, confirm that they fully match the parameters of the deployed model service, with no format errors.
The values provided here are common starting points, and should be measured against the reader's own samples.