What the data for this category looks like
Data comes from iron melt and scrap steel ledgers, L2 process control system, laboratory composition reports, MES production management system, energy medium monitoring and equipment status monitoring modules. Daily generated documents include heat plans, batching lists, converter smelting records, molten steel composition reports, continuous casting process parameter records, slab quality judgment sheets, and others. Fields include heat number, cast number, element content, smelting temperature, equipment operating duration, and more. Update frequency: records are generated per heat, key process parameters are synchronized in real time, and scheduling documents are updated per shift.
What Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
Process documents and abnormal report data for iron and steel smelting and continuous casting have high update frequency and strong process coupling. Real-time data per heat requires retrieval responses to not lag behind production rhythms. Data fields contain a large number of professional process parameters, requiring precise matching of specific fields such as heat number and element content. General retrieval logic is prone to matching deviations. Multiple data sources require the knowledge base to integrate structured and semi-structured data in heterogeneous formats, placing high demands on the retrieval's field mapping capabilities. Abnormal reports have strong timeliness, requiring rapid recall of historical processing records for the same type of abnormality. The number of retrieved records and sorting logic must adapt to the priority of production abnormalities.
Configuration Settings
These configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Process documents and abnormal reports need to match core fields such as heat number and composition content. This interval filters low-correlation historical records |
limit | Top 8–12 entries | The number of historical process records associated with a single heat is limited. This range fits the FastGPT context window and avoids interference from redundant information during drafting |
searchMode | mixedRecall | The query examples combine domain terms with natural-language context; compare with representative data, not as a product default. |
usingReRank | Enabled | Multi-source heterogeneous process data needs to be reordered by abnormality severity and process relevance to improve the recall priority of key disposal records |
datasetSearchUsingExtensionQuery | Enabled | Expands synonymous queries for professional process terms, covering process parameters expressed in different ways, and improves retrieval coverage |
collectionIds | Bind four dedicated knowledge base collections: iron smelting, steel smelting, continuous casting, and quality | Isolates data by business domain, prevents recall of irrelevant documents across processes, and improves retrieval accuracy |
Three Common Configuration Mistakes
- Phenomenon: FastGPT instances deployed on Alibaba Cloud experience retrieval response timeouts, with a 504 status code displayed on the interface. Reason: Retrieval parameters were not adjusted for the real-time, high-concurrency process data of iron and steel smelting, and the load of a single round of retrieval exceeded the server's default resource threshold.
- Phenomenon: Irrelevant records across heats are mixed in the retrieval results, and the batching list data for the current heat cannot be accurately matched. Reason: A reasonable threshold for
similaritywas not set, or the precise matching mode ofsearchModewas not enabled, resulting in the recall of low-correlation data. - Phenomenon: Unable to read the content of slab judgment sheets in xlsx format in the knowledge base, and the fields of specified rows are empty. Reason: The knowledge base import rules were not configured for the structured format of iron and steel smelting documents, and core fields such as heat number and element content were not mapped, resulting in loss of key data during import.
How to Confirm the Configuration Is Correct
- Enter the FastGPT knowledge base management interface, check whether the bound
collectionIdsinclude the four business domain collections of iron smelting, steel smelting, continuous casting, and quality, and confirm that no cross-domain irrelevant collections are selected. - Initiate a test query containing the current heat number, check whether the matching scores of the retrieval results meet the preset interval, and confirm that the
similarityconfiguration is effective. - Import a real converter smelting record xlsx file, check whether the imported document contains core fields such as heat number and smelting temperature, and confirm that the field mapping rules are correct.
- Submit a test query for continuous casting abnormalities, check whether the retrieval results are sorted according to process relevance, and confirm that the
usingReRankfunction is normally enabled.
The values provided are common starting points and should be measured against the reader's own samples.