What data looks like for this category
The service inquiry data for iron and steel smelting mainly comes from production ledgers, process specifications, quality inspection reports, equipment operation and maintenance records, and safety operation specifications. Data update rhythm is divided into three categories: equipment operation data is synchronized in real time, production daily reports are updated daily, and monthly process summaries are archived monthly. Documents include structured fields such as furnace number, smelting batch, steel grade, as well as unstructured process steps and troubleshooting procedures. Common units include degrees Celsius, hours, tons, and others.
What constraints these characteristics impose on the knowledge base retrieval and recall link
There are many structured fields with close associations, requiring retrieval to combine metadata for precise filtering to avoid mixing irrelevant results. Real-time updated equipment data requires the recall link to have relatively low latency to meet the real-time needs of service inquiries. Documents have a clear hierarchical logic, such as the chapter subordinate relationship of process specifications. Direct segmentation will lose hierarchical associations, affecting the accuracy of semantic matching for retrieval. Some documents contain professional terms and units, requiring that the unit format of the corresponding fields be matched during retrieval to avoid ambiguity.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.72–0.85 | Semantic similarity interval for matching iron and steel smelting professional terms, balancing recall coverage and accuracy, calibrated based on actual measurements |
limit | Top 10 results | Matching the upper limit of single query result display for service inquiries, avoiding excessive results exceeding the context window |
searchMode | Full-text retrieval + metadata filtering | Adapting to the mixed document structure of structured fields and unstructured text, prioritizing matching keywords and metadata associations |
usingReRank | Enabled | Re-rank recall results by semantic relevance to optimize the sorting logic for iron and steel smelting professional content |
datasetSearchUsingExtensionQuery | Enabled | Automatically expand query terms for vague inquiries in service scenarios, covering synonyms and alternative names of process terms |
collectionIds | Bind three datasets: smelting process, equipment operation and maintenance, quality inspection reports | Limit retrieval scope to avoid interference from irrelevant data across categories |
Three common mistakes
- Phenomenon: Too many retrieval results, and the AI response is truncated mid-way. Cause: No reasonable
limitparameter set, and the recalled results exceed the context window upper limit. - Phenomenon: Retrieval results do not carry metadata, making it impossible to associate corresponding furnace number and steel grade information. Cause: Metadata binding configuration not enabled, or associated fields not specified during retrieval.
- Phenomenon: Logical structure of markdown-formatted process documents is lost after retrieval, and matching results are scattered. Cause: Document hierarchical structure not preserved, direct segmentation causes semantic association break between titles and subordinate content.
How to confirm the configuration is correct
Reference only; verify against the target FastGPT release and deployment documentation.
- Initiate a test query that includes steel grade and equipment name, verify that the number of returned results matches the
limitsetting. - Check the metadata fields of retrieval results, confirm that information such as furnace number and smelting batch has been returned synchronously.
- Upload a process specification document with hierarchical titles, retrieve questions corresponding to specific chapters, confirm that the results retain hierarchical associations.
- Trigger a vague inquiry test, confirm that the system automatically expands queries using synonyms of professional terms.
All listed configuration values are common starting points, and should be evaluated against your own specific 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.