FastGPT v4.17.0
What Data for This Category Looks Like
Data sources include online composition analysis systems, DCS control systems, laboratory test data, kiln and mill equipment operation data, raw fuel ledgers, and energy metering reports. Update frequencies fall into three categories: real-time (such as kiln temperature, material flow), shift-based (such as inspection records), and daily (such as quality control notices, energy reports). Daily documents include raw meal proportioning plans, kiln operation logs, clinker test reports, quality control notices, equipment inspection records, and energy consumption reports. Document fields include material proportioning parameters, thermal parameters, composition test values, equipment operating hours, and no additional custom units are used.
Constraints for Model Integration and Configuration
The multi-source heterogeneous data and differentiated update rhythms of this category require model integration to adapt to the recall frequency and context carrying capacity of different data types. Real-time kiln and mill operation data requires short-cycle context support to prevent information expiration. Batch daily quality reports and ledger documents require sufficiently high reference token limits to cover complete business data. At the same time, strict accuracy requirements for core fields such as clinker composition and strength indicators require limiting randomness in model generation, to avoid quotation and document content that does not match actual production parameters.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
quoteMaxToken | 8000–12000 token | Covers complete content of multiple business documents including raw meal proportioning plans, kiln operation data, test reports, etc., to avoid truncated recalled data |
maxContext | 16000–24000 token | Carries total content volume of real-time operation data and batch reports, adapting to context requirements for multi-source documents |
temperature | 0.1–0.3 | Reduces randomness in generated content, ensuring tender documents and quotations match actual production parameters |
AIPROXY_API_ENDPOINT | Exclusive API address of the deployment node | Ensures data requests are directed to the specified model service, avoiding cross-node interference |
AIPROXY_API_TOKEN | Exclusive key bound to the API address | Ensures interface call security and permission control |
vision | Off | Current business documents are primarily in text format, with no immediate need for image decoding processing |
Three Common Configuration Mistakes
- Adjusting the
quoteMaxTokenparameter above 2000 results in the model not referencing recalled knowledge base documents during generation. This occurs because the parameter configuration is not linked to context window linkage limits, causing the total context length to exceed the carrying capacity and recalled content to be automatically truncated. - The model displayed in the chat interface does not match the configured model. This occurs because the key bound to the API proxy configuration and the node is incorrect, causing requests to be redirected to the default model.
- The generated quotation document contains proportioning ratios or energy consumption data that do not match actual production parameters. This occurs because the
temperatureparameter is set too high, leading to model hallucinations that deviate from actual business data.
How to Confirm Successful Configuration
- Initiate a test request, check the reference token-related fields returned by the interface to confirm they match the configured values.
- Upload a single raw meal proportioning plan document, trigger generation of a test document, and verify that referenced document fragments match the original document content.
- Check API call logs to confirm the request address matches the configured
AIPROXY_API_ENDPOINT. - Adjust the
temperatureparameter to different ranges, observe changes in the rigor of generated content, and confirm the parameter takes effect.
The values provided are common starting points and should be measured against the reader's own samples.