What this type of data looks like
Ledger data for steel smelting comes primarily from production execution systems (MES), equipment inspection systems, and quality inspection and testing systems. Data is generated per smelting process batch, with updates synced per shift or uploaded in real time at key nodes. Individual ledger documents include fields such as process number, furnace number, steel grade, smelting duration, core composition test values, equipment operating temperature and pressure parameters, quality inspection conclusions, and abnormal disposal records. Field units include ℃, MPa, %, hours, and others.
What constraints these characteristics impose on knowledge base retrieval and recall
The multi-structured fields and batch-based nature of steel smelting ledgers require retrieval to prioritize exact field matching, to avoid interference from generic text. The real-time or shift-level update rhythm requires incremental knowledge base synchronization to align with production shift cycles, preventing invalid retrieval results caused by data lag. Each ledger document contains multi-dimensional parameters, so retrieval and recall must retain logical relationships between fields, avoiding loss of critical corresponding connections after splitting. The large volume of similar batch data also requires retrieval to filter invalid duplicate entries, improving recall accuracy.
How to configure the settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches the structured field precision requirements for steel smelting ledgers, balances recall rate and accuracy |
limit | Top 10 entries | Adapts to the volume of ledger batches per shift, avoids excessive redundant data occupying the context window |
searchMode | Exact matching | Prioritizes accuracy of field matching for steel smelting ledger data with multiple structured fields |
usingReRank | Enabled | Performs secondary sorting on initial recall results by the relevance between documents and queries, corrects deviations from single vector matching |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands query terms for professional smelting terminology, covers industry-specific expressions |
collectionIds | Configured by smelting process grouping | Divides datasets by steel smelting processes such as blast furnace, converter, and steel rolling, narrows retrieval scope to improve response speed |
Three common misconfigurations
- Retrieval response time exceeds 10 seconds, and the interface displays a loading timeout prompt. The cause is failure to configure
collectionIdsby smelting process grouping, with the retrieval scope covering all ledger data, and the large data volume slowing down the recall process. - Retrieval returns no results, and the interface prompts that no matching documents were found. The cause is failure to enable
datasetSearchUsingExtensionQuery, which cannot expand query terms for professional smelting terminology, causing input professional expressions to fail to match document vectors. - Retrieval results include ledger data for non-target steel grades. The cause is failure to set a reasonable
similaritythreshold, with a threshold that is too low recalling low-relevance documents that do not meet similarity standards.
How to verify the configuration is correct
- Submit a retrieval request for a specified furnace number and steel grade, verify that the returned result fields match the query content.
- Check the retrieval logs in the FastGPT backend, confirm that the switch status of
usingReRankanddatasetSearchUsingExtensionQuerymatches the configuration. - Test queries with different professional smelting terms, confirm that the returned results include ledger data for the corresponding process.
- Count the response time of a single retrieval, confirm that it meets the time requirements of the business scenario.
The values provided here are common starting points, and should be evaluated 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.