What Data for This Category Looks Like
This category of data typically originates from concentrate and auxiliary material analyses, DCS systems, cell voltage and current monitoring systems, laboratories, and energy and environmental monitoring equipment. Update frequencies align with per-batch raw materials, per-furnace or per-cell operating conditions. Some indicators update in real time. Daily documentation includes batching lists, smelting logs, electrolytic cell condition records, laboratory reports, anode and cathode quality logs, and environmental protection ledgers. Fields cover raw material composition parameters, furnace temperature parameters, cell voltage and current values, laboratory test results, and environmental monitoring readings.
What Constraints Do These Characteristics Impose on the "Multi-Turn Dialogue and Prompt Engineering" Link
This category’s data has varied update rhythms and dense professional fields. Multi-turn dialogue must retain near-real-time cell condition, furnace temperature and other parameters to avoid context disconnection. Documentation fields cover the entire production process. Prompt engineering must explicitly limit retrieval scope to smelting-related batching, furnace condition, laboratory, and environmental protection records to prevent irrelevant content from interfering with retrieval results. Documents for a single batch of raw materials or single cell have strong relevance. Multi-turn dialogue must associate context by batch or cell number to avoid mixing parameters across batches. In addition, environmental protection ledgers and production records require linked retrieval. Prompt engineering must specify rules that match both production and environmental fields to ensure comprehensive cross-review.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
messages | Retain the most recent 20 turns of dialogue context | Smelting data is mostly batch-associated. Excessively old context will interfere with parameter matching for current cell conditions or furnace numbers |
variables | Bind current batch number and cell number parameters | Limit retrieval scope to production and laboratory documents for the corresponding batch to avoid mixing data across batches |
| Single search result limit | Top 8 retrieval results | This category has many documentation fields. Excess results will exceed the model's processing limit |
| Maximum context size | 8000–12000 token | There are many professional terms. Sufficient context is needed to carry parameter associations for multi-turn dialogue, and this must comply with FastGPT v4.17.0 context processing rules |
chatId | Generate a unique ID per production batch | Isolate dialogue history across different batches to avoid data cross-interference |
| Question optimization toggle | Enable professional term optimization | Adapt to exclusive terms in the smelting field to improve retrieval and response accuracy |
Three Common Misconfigurations
- Symptom: After local deployment and uploading technical documentation, the data processing steps show as empty. Cause: Retrieval fields are not correctly configured to match the documentation structure of this category, so the system cannot identify target documents such as batching lists and cell condition records.
- Symptom: Calling the dialogue interface returns model question-and-answer results, but the AI dialogue stage does not reference knowledge base content. Cause: Retrieval limit parameters for the corresponding batch or cell number are not bound in
variables, so the retrieval scope covers irrelevant documents. - Symptom: No dialogue history records can be queried in the MongoDB database. Cause: The configuration toggle for dialogue history storage is not enabled, or the
chatIdgeneration rule does not meet system storage requirements.
How to Confirm Successful Configuration
- Launch a multi-turn dialogue, enter the current batch's cell number parameter, and check that retrieval results only include smelting and laboratory documents for the corresponding batch.
- Call interfaces related to
messages, and verify that the context only retains the specified number of dialogue turns with no redundant old data. - Check that
variablesparameters are bound to exclusive fields for the current production scenario to ensure precise retrieval scope. - View the MongoDB storage directory, and confirm that dialogue history record entries identified by
chatIdexist.
The values provided here are common starting points, and should be measured against the reader's own samples.