What data for this category looks like
This category of data comes from cement enterprise mining, raw material, calcination, grinding, quality, and energy departments. It covers documents including raw material batching plans, kiln operation records, laboratory reports, quality control notices, equipment patrol records, energy reports, and more. Data sources include online composition analysis, DCS systems, laboratories, kiln and grinding equipment, raw and fuel ledgers, and energy metering devices. Update frequencies cover real-time, per-shift, and daily. Document fields include raw material ratios, calcination temperature, free calcium content, cement strength indicators, and more, with corresponding testing units and working condition parameters attached.
What constraints do these characteristics impose during model access and configuration
The multi-source nature, varied update frequencies, and specialized field characteristics of this category of data impose multiple constraints on model access and configuration. Dispersed real-time, per-shift, and daily updated data requires configuring index synchronization mechanisms adapted to different update cycles. Recognition of specialized industrial parameters such as free calcium content and calcination heat consumption requires models and vector models to adapt to semantic encoding of industrial terminology. Long documents related to rotary kiln thermal system adjustments require limiting context and token lengths to avoid inference overflow. High-confidence recall requirements related to clinker quality require configuring appropriate retrieval and reranking parameters to ensure returned results match process specification requirements.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Covers the full context of a single kiln shift operation record and complete batching plan, avoiding truncation of core process parameters |
maxTokens | 2000–3000 tokens | Balances long document recall and inference speed, adapts to the single request length for process Q&A, and avoids inference timeouts |
quoteMaxToken | 1500 tokens | Limits the length of a single referenced document, preventing a single reference from occupying too much context space and ensuring rationality of multi-document recall |
temperature | 0.2–0.4 | Maintains rigor in process Q&A, avoids generating content that deviates from technical standards, and adapts to the deterministic requirements of industrial scenarios |
AIPROXY_API_ENDPOINT | Intranet-deployed FastGPT proxy address | Adapts to the intranet deployment environment of FastGPT v4.17.0, ensures data transmission security, and avoids public network exposure |
AIPROXY_API_TOKEN | Secret key consistent with intranet proxy configuration | Verifies request legitimacy, prevents unauthorized access, and ensures security of internal process data |
Three common misconfigurations
- Symptom: Connection timeout error with 504 status code when integrating a vector model via Docker deployment. Cause:
AIPROXY_API_ENDPOINTwas not correctly configured to point to the intranet-deployed vector model address, resulting in failed request routing. - Symptom: Key batching parameters are missing from retrieval results when importing long documents such as raw material batching plans. Cause: A reasonable
maxContextparameter was not set, resulting in truncation of core process content during document segmentation. - Symptom: After configuring a model in Config.json, only streaming fragments are returned, and a complete single answer cannot be obtained. Cause: Non-streaming call parameters were not adjusted according to documentation requirements, resulting in the model continuously returning segmented output.
How to confirm successful configuration
- Check the vector model access logs to confirm there are request records from the FastGPT deployment node.
- Import a single raw material batching plan document, then check if the returned reference fragments cover the core content of the document after retrieval.
- Submit a process standard Q&A request to confirm that no streaming segmented output appears in the returned results.
- Verify the
AIPROXY_API_TOKENconfiguration to confirm it matches the secret key of the intranet proxy, and that no permission error logs exist.
The values provided here are common starting points and should be evaluated against the reader's own samples.