What the data for this category looks like
Data for internal office and employee services in non-ferrous metal smelting comes from four main channels: PLC collection logs from production sites, equipment operation and maintenance ledgers, safety operation procedures and employee training materials, and historical question-and-answer records from internal office groups. Production sites generate logs per shift, updating them every four hours. Operation and maintenance ledgers update in real time with equipment overhauls. Teams update procedures and training materials quarterly. The system automatically archives historical Q&A records by question time. The data structure includes two categories: structured fields and unstructured text. Structured fields include smelting temperature (unit ℃), current intensity (unit A), material component proportion, and equipment number. Unstructured text includes operation steps, exception handling instructions, and colloquial Q&A content.
What constraints do these characteristics impose on knowledge base retrieval and recall
The real-time update feature of production logs requires retrieval systems to support incremental recall. This avoids repeated processing of full historical data and reduces retrieval latency. The mixed structured and unstructured data format requires retrieval systems to support both keyword matching and semantic matching. This prevents irrelevant text content from being recalled only through keywords. Professional terms and fixed-unit fields require retrieval systems to accurately recognize unit semantics. This stops temperature unit confusion and parameter matching deviations. Colloquial Q&A records from internal groups require retrieval systems to automatically convert employees’ daily questions into professional terms. This improves recall accuracy. Longer procedure documents require reasonable chunking rules. This ensures semantic integrity for individual chunks.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.85 | Semantic similarity threshold for matching smelting-specific professional terms, filters low-relevance recall results and prevents irrelevant documents from being returned |
limit | Top 8 entries | Limits the maximum number of results returned per retrieval, adapts to the context carrying capacity of a single employee query and avoids excessive results interfering with judgment |
searchMode | Hybrid retrieval | Matches both structured fields and unstructured text, adapts to the mixed format characteristics of smelting data |
usingReRank | Enabled | Re-ranks recall results by semantic relevance to employee questions, corrects relevance deviations caused by keyword matching |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands employees' colloquial questions into professional terms, for example, expanding "insufficient furnace temperature" to "smelting furnace temperature lower than the set threshold", improving recall accuracy |
collectionIds | Bound to smelting production database, operation and maintenance document database, internal Q&A database | Limits the scope of retrieval datasets, avoids recalling non-smelting administrative office documents |
Three Common Misconfigurations
- Phenomenon: The same production exception consultation question appears repeatedly in internal groups without reused answers. Cause: Internal historical Q&A records are not included in the retrieval dataset, and
datasetSearchUsingExtensionQueryis not enabled to reuse existing answers. - Phenomenon: A large number of administrative office documents appear in retrieval results, and it is impossible to locate content related to smelting production. Cause: The retrieval dataset scope is not limited via
collectionIds, and non-smelting documents are included in the recall pool. - Phenomenon: Professional parameter units in retrieval results are matched incorrectly, for example, "1200℃" is recalled as "1200K". Cause: Semantic matching rules are not configured for professional fields of smelting data, leading to unit semantic confusion.
How to Confirm Proper Configuration
- In the FastGPT v4.17.0 console, upload a test document of smelting production logs, enter the query "smelting furnace temperature abnormality", and check whether the recall results include relevant content from this document.
- View the retrieval configuration panel and confirm that both
usingReRankanddatasetSearchUsingExtensionQueryare enabled. - Enter the colloquial query "the furnace is not heating up", and check whether the recall results automatically expand to professional terms and match the correct operation and maintenance documents.
- Check that
collectionIdsis only bound to the smelting production database, operation and maintenance document database, and internal Q&A database, and does not include irrelevant administrative datasets.
The values provided here are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.