What this category of data looks like
Data for this category comes from hot metal and scrap collection systems, L2 process control nodes, laboratory testing systems, manufacturing execution systems (MES), energy medium monitoring systems, and equipment status monitoring modules. Data update frequencies include furnace plans and batching lists generated per furnace, real-time synchronized smelting and continuous casting process parameters, and shift-wise summaries of energy consumption and equipment operation records. Daily documents include standardized furnace ledgers, batching details, smelting process records, composition test reports, continuous casting process parameter logs, and slab quality judgment sheets. Fields include furnace number, smelting duration, process temperature, equipment operation parameters, and quality test values, with corresponding units such as minutes, degrees Celsius, millimeters, and others.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering
The high-frequency real-time update and multi-source heterogeneous data characteristics of this category require multi-turn dialogue to gradually guide users to clarify the queried furnace number or time interval, to avoid calling expired or irrelevant production data. The fixed-format ledger document structure requires prompt engineering to explicitly specify matching document fields and categories, to prevent the generation of confusing summary content. The independent ledger generated per furnace requires the dialogue context to be bound to the furnace identifier corresponding to the current session, to avoid cross-furnace data misuse. The real-time synchronized process parameters require dialogue responses to prioritize returning the latest collected node data, to ensure the timeliness of summary reports.
How to configure the settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind the currently queried furnace number as the unique identifier | Each furnace’s production data is independent, to avoid cross-furnace data misuse |
messages | Limit the single-turn dialogue context to retain the last 3 interactions and 2 furnace ledgers | Reduce redundant data interference and improve response accuracy |
variables | Preset two required variables: furnace number and detection time range | Clarify the query dimensions of multi-source production data and narrow the retrieval scope |
| Maximum context token count | Calibrated based on actual testing, no more than 12000 tokens | The total amount of single-furnace ledger and process data is large, balancing context completeness and response speed |
| Number of recalled documents per single request | Top 6 entries | Documents associated with a single furnace do not exceed 5, retaining reasonable redundancy space |
| Question optimization switch | Enabled | There are many production-specific terms, optimizing the matching accuracy between user questions and ledger fields |
Three common mistakes
- Phenomenon: An error occurs in the debug preview dialogue after enabling input guidance and configuring a custom thesaurus address. Cause: The custom thesaurus is not associated with the furnace number variable bound to
variables, causing the thesaurus to fail to match professional terms in the current production scenario. - Phenomenon: The maximum response token is set to above 3000, but the response length for a specific production question is only 200. Cause: The dialogue context triggers the built-in context truncation rule, or the complete ledger data of the current furnace is not bound, resulting in a limited retrieval scope.
- Phenomenon: Real-time collected production data cannot be added to the knowledge base during the dialogue, or the token statistics value of the session cannot be viewed. Cause: The real-time data synchronization trigger logic based on
messagesis not configured, and the viewing entry for global token statistics cannot be found.
How to confirm the configuration is complete
- Enter the session configuration page of FastGPT v4.17.0, check whether
chatIdis bound to the furnace number field, and confirm that the preset furnace number and time range parameters are included in the variable list. - Initiate a test dialogue containing a specific furnace number, check whether the response only returns relevant ledgers and process data of that furnace, with no cross-furnace data mixed in.
- Test query questions of different lengths, confirm that the response token count conforms to the preset upper limit, with no abnormal truncation.
- Try to mention real-time production data in the dialogue, confirm that the system can synchronously update the corresponding content in the knowledge base, and that the token statistics value can be viewed on the global configuration page.
The values provided here are common starting points and should be measured against the reader's own samples.