What Data Looks Like for This Category
This category covers smelting, electrolysis, laboratory, and scheduling departments of copper, aluminum, lead-zinc, and other smelting enterprises. Daily documents include batching lists, smelting records, electrolytic cell condition records, laboratory reports, anode and cathode quality records, and environmental protection ledgers. Data sources include concentrate and auxiliary material analysis results, DCS systems, cell voltage and current monitoring data, laboratory reports, energy and environmental protection monitoring systems. Data update frequency adjusts per batch of raw materials, each furnace, or each electrolytic cell. Some key parameters such as cell voltage and current are updated in real time. Document fields include batching ratio, furnace temperature, cell condition parameters, metal grade, impurity content, and other metrics. Fields and units must be matched according to the corresponding document type.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
Real-time updated operating condition data requires multi-turn dialogue to quickly retrieve the latest furnace and cell condition parameters, to avoid using expired data for inspection. The subdivided fields of multiple types of professional documents require prompts to clearly specify retrieval scopes, for example, only recalling relevant content from electrolytic cell condition records. Complex metal balance and impurity control processes require retaining multi-turn dialogue context to avoid inspection personnel from repeatedly entering parameters such as equipment numbers and raw material batches. At the same time, the consequences of errors in this category are directly linked to metal recovery rates and environmental protection indicators. Prompts must include operating condition verification logic to ensure that questions match actual equipment status.
Configuration Settings
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
chatId | Bind to the unique equipment number of the inspection work order | Distinguish dialogue contexts for different smelting furnaces and electrolytic cells, avoid cross-device information confusion |
messages | Retain the first 20 rounds of dialogue content | Cover the complete inspection process for a single furnace or cell, avoid loss of key operating condition parameters |
| Maximum Context Length | 8000–12000 token | Adapt to long-content retrieval and dialogue context storage for non-ferrous metal smelting professional documents, complies with FastGPT v4.17.0 specifications |
| Question Optimization | Enabled | Convert colloquial questions from inspection personnel into standardized queries matching professional documents |
| Single Search Limit | Top 8 recall results | Balance retrieval accuracy of professional documents and dialogue response speed |
Three Common Misconfigurations
- Symptom: The workflow hangs without response when reaching the AI dialogue stage, triggered when reproducing specific issues. Cause: The
variablesparameter is not bound to pass the cell number or furnace number of the current inspection equipment, causing the knowledge base to fail to locate exclusive documents for this category, and the workflow interrupts. - Symptom: No streaming output when calling dialogue via API, and the front end cannot display reply content step by step. Cause: Streaming return configuration for dialogue is not enabled, or response data is not packaged in the standard format.
- Symptom: The annotation function for dialogue logs does not work, and annotated content is not synchronized to the knowledge base. Cause: Automatic synchronization configuration for dialogue logs is not enabled, or the annotation scope does not cover dialogue fragments related to equipment inspection.
How to Verify Successful Configuration
- Initiate an inspection query for a specific electrolytic cell, check whether the dialogue context retains previously entered cell numbers and current cell voltage and other parameters.
- Call the API to initiate a dialogue, verify that the response is returned in streaming segments, and the front end can gradually receive content fragments.
- Enter the dialogue log page, attempt to annotate a section of dialogue content related to equipment inspection, confirm that the annotation can be saved normally without errors.
- Trigger the complete workflow, execute from question optimization, knowledge base search to AI dialogue stage in sequence, with no mid-process interruptions.
The values provided are common starting points and should be measured against the reader's own samples.