What This Category's Data Looks Like
Typical data for this category comes from concentrate and auxiliary material analysis, DCS systems, cell voltage and current monitoring, laboratories, and energy and environmental monitoring equipment. Daily documents include batching lists, smelting records, electrolytic cell condition records, test reports, anode and cathode quality records, and environmental protection ledgers. Data updates are conducted per batch of raw materials, per furnace, or per cell. Some cell condition and energy consumption data is updated in real time. Document fields include batch number, furnace/cell number, process parameter values, test result values, and timestamps. Units include degrees Celsius, volts, amps, tons, and other standard industrial units.
Constraints on Source Citation and Traceability
Data for this category is scattered across multiple systems and documents. Field formats vary across different sources. When citing and tracing data, associated fields such as batch numbers and furnace/cell numbers must be accurately matched to avoid mixing data across batches. Data update frequencies differ. Some cell condition and energy consumption data is pushed in real time, while remaining data is archived per batch of raw materials or furnace run. During retrieval, datasets within the corresponding time window must be limited. Most document fields contain process and test values. Original unit identifiers must be retained when citing data to preserve the process reference value of traced records. Additionally, impurity control and metal balance rely on precise traced data, which must be strictly bound to the original data’s source node and generation time.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Maximum Context Length | First 800–1200 characters | Matches the typical length of a single process document for this category, to avoid truncating core process parameters |
quoteMaxToken | 1500–2000 token | Covers the total content of multiple associated documents per batch, to ensure complete citation of data |
| Maximum Number of Citations | First 3–5 entries | Limits the number of retrieval results to avoid mixing irrelevant cross-batch data |
responseData | Only return archived batch data | Adapts to the data source filtering logic of FastGPT v4.17.0, filtering real-time temporary data |
detail | Enable full field display | Retains identifiers such as original document units and timestamps to meet the detail requirements of process traceability |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Filters low-relevance historical data to accurately match batch parameters for the current audit |
Three Common Misconfigurations
- Phenomenon: The interface returns a citation list, but some entries do not exist in the local knowledge base. Cause: The retrieval scope is not limited to associated data for the current audit batch via configuration, resulting in retrieval of redundant cross-batch data.
- Phenomenon: A configuration error is triggered when applying knowledge base variables. Cause: Variables are not bound to the data source identifier corresponding to the furnace/cell or batch, or batch association verification is not enabled in the system configuration.
- Phenomenon: The retrieved citation data lacks original units or timestamps. Cause: The
detailconfiguration item is not enabled, resulting in only simplified numerical content being returned, which cannot meet the detail requirements for process traceability.
How to Verify Proper Configuration
- Import documents such as batching lists and test reports for a single batch, submit a quality audit request, and check whether the returned citation list only includes associated documents for that batch.
- View the displayed content of cited data to confirm that units, timestamps, and source identifiers of original parameters are included.
- Adjust the configuration for maximum number of citations, and verify whether the number of returned citations matches the set range.
- Test calling the configuration using batch variables, and confirm that variables can correctly bind to the corresponding data source without triggering errors.
The values provided are common starting points and should be measured against the reader's own samples.