This document was written for FastGPT v4.17.0.
What this category of data looks like
Data for this category comes from outputs of multiple systems across ironmaking, steelmaking, continuous casting, quality control, and scheduling departments of integrated iron and steel enterprises. Daily documents include heat plans, batching lists, converter smelting records, composition reports, continuous casting process records, and slab judgment sheets. Data sources cover hot metal and scrap data, L2 process control data, laboratory test results, MES systems, energy medium systems, and equipment condition monitoring data. Update frequencies include per-heat, real-time, and shift-based updates. Data fields include heat number, composition content, process parameters, equipment operating status, and more. Units are set flexibly based on the corresponding document scenario.
What constraints do these characteristics impose on model access and configuration
The multi-source, high-frequency update nature of this category of data requires model access to adapt to a knowledge base update mechanism combining real-time and batch synchronization. This prevents expired data from being retrieved. Field differences across multiple document types and flexible unit settings require clear definition of retrieval document scope and field filtering rules during configuration. This stops irrelevant data from interfering with Q&A results. The high-temperature continuous production business attribute requires model response time to be controlled within a reasonable range. This avoids impacting the timeliness of on-site inspection and maintenance decisions. The strong coupling between heats and casting runs requires retrieval results to be associated with complete process data for the corresponding heat. Context association logic must be configured to match the business scenario.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Complete process data for a single heat in this category is large, requiring coverage of context from multiple associated documents, while avoiding exceeding the model's capacity limit |
maxTokens | 4000–6000 token | Equipment inspection and maintenance Q&A requires concise output, while retaining sufficient context to associate heat, process, and equipment data |
quoteMaxToken | 1500–2000 token | Limit the length of a single referenced document, preventing retrieval content from being too long to trigger the reranking model, and adapting to the compact requirements of on-site Q&A |
| Workflow/API tool selection | auto | Allow the model to independently choose whether to call the retrieval tool, adapting to Q&A requirements across scenarios such as inspection, maintenance, and process queries |
temperature | 0.2–0.4 | Equipment maintenance and inspection Q&A requires rigor and accuracy, reducing randomness to ensure business consistency of output |
AIPROXY_API_ENDPOINT | Fill in according to the intranet address of the deployed model service | Adapt to the security requirements of intranet deployment in iron and steel enterprises, avoiding sensitive data leakage |
Three common configuration mistakes
- Phenomenon: Model response time exceeds the preset threshold. Logs show the retrieval link accounts for an excessively high proportion of total time. Cause: The value of
maxContextis not reasonably limited, resulting in an overly large context window. This causes excessive resource usage for model loading and retrieval, failing to meet the timeliness requirements of on-site real-time decision-making. - Phenomenon: The reranking model passes deployment testing, but actual retrieval returns
falsefor reranking results. Cause: No configuration ofquoteMaxTokento limit the length of a single reference. Retrieval content exceeds the input limit of the reranking model, so the reranking logic does not trigger. - Phenomenon: The Qwen3-Reranker-8B reranking model fails to load properly. Cause: No registration information for the corresponding reranking model added in the model access configuration, or the input and output format requirements of the model are not matched.
How to verify successful configuration
- Run a Q&A for converter equipment inspection. Check whether the returned result is associated with the smelting record and equipment status data of the corresponding heat, to confirm that the context configuration takes effect.
- Upload a continuous casting process record document that exceeds the preset length. Trigger the retrieval process, check whether the reranking model normally returns sorted results, to confirm that the
quoteMaxTokenconfiguration is reasonable. - View the resource usage logs of the model service. Confirm that the response time meets the timeliness requirements of on-site operations, and adjust the value ranges of
maxContextandmaxTokensas needed. - Check the API interface call logs. Confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly, with no connection timeouts or permission errors.
The values provided are common starting points and should be measured against the reader's own samples.