What this category of data looks like
Data sources for this category include hot metal and scrap charging records, L2 process control system data, laboratory composition test reports, MES production management system, energy medium operation data, and equipment condition monitoring system. Data update frequencies are generated per heat, real-time, and per shift. Daily documents include heat plans, batching lists, converter smelting records, composition analysis reports, continuous casting process parameter records, and slab quality judgment sheets. Data fields include heat number, hot metal composition proportion, smelting end temperature, continuous casting pulling speed, slab thickness, defect type code, etc. Formats are adjusted according to production line configurations with no unified standard.
Constraints imposed by these characteristics on the multi-turn dialogue and prompt engineering link
Data for this category is associated by heat and casting lot, with large differences in update frequencies. Multi-turn dialogue must bind the context of the current production batch via chatId to avoid confusing data across heat retrievals. Data sources are scattered and formats are inconsistent, so prompts must explicitly specify retrieval fields, prioritizing matching heat numbers, composition reports, and continuous casting process records. Real-time updated L2 process data requires the dialogue chain to support dynamic pulling of the latest parameters, without relying on static knowledge base caches. At the same time, quality problem traceability requires retaining historical interaction records of multi-turn dialogue to facilitate retrospective analysis of the process, and the length of single-turn dialogue context must be limited to avoid response timeouts caused by excessive data volume.
How to set up configurations
The following configurations are based on FastGPT v4.17.0, adapted to the quality traceability needs of iron and steel smelting and continuous casting production lines:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context length | 8000–12000 token | Matches the total character count of documents related to a single heat, to avoid exceeding model context limits |
chatId binding rule | Generate a unique chatId based on the current heat number | Ensures multi-turn dialogue only associates production data for the current heat, avoiding confusion across batches |
variables parameter configuration | Pass heat number and casting lot number as global variables | Assists the retrieval link to accurately match documents for the corresponding heat, narrowing the retrieval scope |
| Maximum number of retrieved documents per query | Top 6 | Balances retrieval coverage and dialogue response speed, adapting to the volume of single-heat documents on production lines |
| Question optimization switch | Enabled | Optimizes user questions for steelmaking technical terms, improving retrieval matching accuracy |
| Streaming output switch | Enabled | Adapts to the needs of production line personnel to view analysis results in real time, avoiding long waits |
Three common mistakes
- Phenomenon: Dialogue return results do not reference documents corresponding to the target heat, or reference data from other heats. Cause: The current heat context is not bound via
chatId, or thevariablesparameter does not pass the heat number as a retrieval filter condition. - Phenomenon: The output of the first dialogue cannot be used as input for the second dialogue, and dialogue context is lost. Cause: The historical interaction content of the
messagesarray is not saved correctly, and the completemessagesparameter is not carried in subsequent requests. - Phenomenon: No data is returned or the return is interrupted during streaming output. Cause: The streaming output configuration is not enabled, or transmission parameters are not adjusted according to the real-time update rhythm of production line data.
How to confirm the configuration is correct
- Initiate a test question containing a known heat number, and verify that all documents referenced in the dialogue return results belong to that heat.
- Initiate two consecutive questions, and verify that the second dialogue uses the output of the first dialogue as the analysis basis.
- After enabling streaming output, initiate a question, and verify that the interface returns analysis content segment by segment.
- View the operation logs of the
chatIdandvariablesparameters, and confirm that the current heat number has been correctly passed to the dialogue chain.
The values provided are common starting points and should be measured against the reader's own samples.