What the data for this category looks like
Data for this category originates from ERP material master data, melting furnace thermometers, spectrometers, MES heat records, mold management systems, and customer technical drawings. Daily documents include casting process cards, batching lists, heat records, mold histories, first article inspection records, material reports, and nonconforming product review forms. Updates run per heat, per batch, or daily. Fields include heat number, alloy batching ratio, melting temperature, pouring parameters, defect type, mold cavity number, and similar items. No preset statistical percentage values are included.
What constraints these characteristics impose on knowledge base retrieval and recall
Updates follow heat or batch cycles. Precise matching of unique business identifiers such as heat numbers and mold cavity numbers is required. Retrieval processes must support filtering by these fields. Document types include structured heat records and unstructured process cards. Retrieval must support content across different formats. Frequent updates create high demand for incremental knowledge base synchronization. Configure sync rules tailored for heat-level updates. Numeric fields such as alloy batching ratio and melting temperature must support exact matching and range retrieval to support parameter comparison during defect traceability. Cross-document relational queries must support matching fields like heat, mold, and batching to avoid fragmented retrieval results.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.85 | Matches foundry parameter precision requirements, avoids retrieving irrelevant heat records or off-target defect documents |
limit | Top 10 results | Covers multiple document types related to a single heat, such as mold, batching, and inspection documents, avoids result overload that reduces analysis efficiency |
searchMode | embedding | Prioritizes semantic matching of process parameters and defect descriptions, fits semantic retrieval needs for defect analysis scenarios |
usingReRank | Enabled | Reorders retrieved results by defect severity and heat priority, improves display placement of critical documents |
datasetSearchUsingExtensionQuery | Disabled | Business terms in foundry scenarios are fixed, no need to rewrite query terms, avoids retrieval bias caused by semantic drift |
collectionIds | Bind by business module | Binds dedicated collections for heat records, process cards, and mold histories, avoids cross-category document interference with retrieval results |
Three Common Mistakes
- Phenomenon: After configuring the re-rank model,
usingReRankreturns false in the question-and-answer workflow. Cause: The re-rank switch was not enabled in the knowledge base node of the workflow, or the re-rank model was not bound to the current knowledge base collection. - Phenomenon: The reference variable dropdown menu of the knowledge base search node is empty, making it impossible to dynamically specify the knowledge base. Cause: No variable of the knowledge base ID type was created in global variables, or the variable was not configured with permissions to be referenced by workflow nodes.
- Phenomenon: Retrieval results do not match expected business documents, and rewritten query terms are used in the recall logic. Cause:
datasetSearchUsingExtensionQuerywas enabled. Business terms in foundry scenarios are fixed, rewritten query terms deviate from original business semantics, leading to retrieval bias.
How to Verify Proper Configuration
- Access the FastGPT v4.17.0 knowledge base management page, verify bound collections include target business documents such as casting process cards, heat records, and mold histories.
- Enter a simulated defect traceability query in the workflow test panel, review the
similarityscore distribution of retrieval results, confirm matching meets business expectations. - After enabling the re-rank function, check if reordered results prioritize core documents matching heat numbers or mold cavity numbers.
- After configuring dynamic variables, pass a preset knowledge base ID variable in the test panel, confirm the search node can normally load document content from the corresponding collection.
The values provided are common starting points and should be measured against the reader's own samples.