What Data for This Category Looks Like
The administrative inquiries and responses data for construction machinery comes primarily from equipment operation and maintenance manuals, troubleshooting case libraries, spare parts parameter lists, and official administrative process documents. The data update rhythm falls into three categories: full manual updates synchronized with new model releases, quarterly updates to spare parts parameters and compliance processes, and daily additions of fault feedback cases for individual equipment. Each individual document includes fixed fields such as equipment model, fault symptom, troubleshooting steps, solution method, spare parts number, response time limit, and others. Units involve professional engineering units such as pressure value (MPa), operating duration (hours), dimensions (millimeters), and similar engineering units.
Constraints Imposed on Vector Models and Indexing
The binding relationship between professional engineering parameters and operating steps requires retaining adjacent parameter descriptions and logic during chunking. This avoids semantic fragmentation after splitting. Document types from multiple sources need to be grouped into separate index partitions based on manuals, cases, and processes. This prevents cross-category retrieval interference. Frequently updated fault cases need support for incremental index triggers. This avoids resource consumption from full index rebuilding. Fields such as equipment model and fault level must be used as retrieval filter conditions. This ensures recalled results match the target inquiry scenario.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Construction machinery documents contain continuous parameter descriptions and troubleshooting steps. This avoids splitting that destroys semantic integrity |
| Overlap Length | 100–150 characters | Covers associative logic between cross-chunk fault symptoms and subsequent steps. This improves retrieval coherence |
maxToken | 4096 | Adapts to long text embedding needs of professional documents. This avoids truncation of key parameters and steps |
| Similarity Threshold | 0.75–0.85 | Matches semantic similarity of professional terminology. This filters low-correlation retrieval results |
| Single Search Result Limit | Top 10 entries | Balances retrieval efficiency and coverage of fault cases |
defaultToken | 512 | Supplies a default token quota for basic context. This adapts to embedding of short inquiry questions |
Three Common Configuration Mistakes
- Symptom: The vector model cannot be called normally after configuration. Timeouts or parameter errors are returned. Cause: Long parameter blocks of construction machinery documents are not adapted. The
maxTokenparameter is not set correctly, leading to truncation of embedding requests. - Symptom: Old fault cases cannot be retrieved after index updates. Cause: Incremental index triggers based on document update timestamps are not configured. Full index rebuilding is accidentally enabled, leading to overwrite conflicts.
- Symptom: Retrieval results include documents from non-target equipment. Cause: Field filter configuration is not enabled. Equipment model is not specified as a retrieval matching condition.
How to Verify Proper Configuration
- Upload a complete construction machinery fault document with full parameters. Check that the chunk preview retains continuous parameters and steps.
- Trigger an incremental index. Check that the index log only synchronizes updated documents. No full index rebuilding is triggered.
- Input an inquiry question that includes a specific equipment model. Verify that retrieval results only return documents for that model.
- Call the vector embedding interface. Check that returned vector dimensions match the configured model dimensions. No truncation errors appear.
The values provided are common starting points, and should be evaluated against local sample data.
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.