What this category of data looks like
Engineering machinery service inquiry data mainly comes from original equipment manufacturer (OEM) full machine manuals, after-sales maintenance specifications, parts parameter ledgers, and construction working condition guides. The data update rhythm varies by machine type. Parameter ledgers for mass-produced models are updated quarterly. Documents for custom models are updated synchronously with order delivery. Most individual documents include fields such as model number, part number, technical parameters, maintenance cycles, fault codes and corresponding disposal steps. Most parameter fields come with standard units, such as rated power (kW), working radius (m), and replacement cycle (hours).
Constraints Imposed on Knowledge Base Retrieval and Recall
The professional unit attributes of engineering machinery data require matching parameter units during retrieval to avoid invalid results with mismatched units. Documents contain both structured parameter blocks and long text descriptions. When retrieving in chunks, the association between parameters and their corresponding descriptions must be retained to prevent information fragmentation after splitting. Differences in update rhythms across machine types require setting synchronization cycles classified by data source to avoid old data mixing into current retrieval results. Part numbers as unique identifier fields need combined recall logic supporting both exact and fuzzy matching to adapt to query needs across different inquiry scenarios.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Engineering machinery parameters require high matching accuracy to avoid irrelevant model results from low similarity, and this aligns with FastGPT v4.17.0's retrieval logic |
limit | Top 8 entries | Engineering machinery documents have many parameters; 8 entries cover core inquiry needs for common models and avoid result overload |
searchMode | hybrid | Supports combining keyword matching and semantic retrieval to adapt to mixed query scenarios for model numbers and parameter descriptions |
usingReRank | Enabled | Semantic drift easily occurs after chunking long documents; re-ranking corrects the relevance ranking of recall results to improve accuracy |
datasetSearchUsingExtensionQuery | Enabled | Supplements extended content related to working conditions, such as construction scenario adaptation plans for a specific excavator model |
collectionIds | Configured by data source groups | Distinguishes datasets of different types such as OEM manuals and after-sales documents to avoid cross-category data interfering with retrieval results |
Three Common Misconfigurations
- The symptom is that imported CSV-format parts documents cannot be displayed in retrieval cards. The cause is that the CSV dataset is not associated with the corresponding
collectionIdsgroup, so the retrieval scope does not cover this data source. - The symptom is that knowledge base variables cannot be called for filtering in the retrieval card. The cause is that the association setting for variable references is not enabled in the retrieval configuration, so the target dataset cannot be bound.
- The symptom is that retrieval results contain content with mismatched parameters and units. The cause is that a reasonable
similaritythreshold is not set; a low threshold recalls documents that are similar but have mismatched units.
How to Verify Proper Configuration
- Upload an engineering machinery parameter document with standard units, enter a query term that includes units, and verify that the units of the returned results match the query requirements.
- Enter a known part number, verify that the retrieval results prioritize returning documents for the corresponding model to confirm the exact matching effect.
- View the retrieval configuration page, confirm that
collectionIdsis bound to the dataset group of the corresponding data source to avoid cross-dataset recall. - After enabling
usingReRank, verify that the relevance ranking of the re-ranked results meets expectations to confirm that the re-ranking function is active.
The values provided 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.