What this category of data looks like
Daily documents for this category include winding and core assembly records, vacuum drying records, insulating oil reports, partial discharge and withstand voltage test reports, and nameplate parameter tables. Data sources are design BOMs, silicon steel sheet and insulating material data, vacuum and drying equipment, oil testing, and test stations. Data updates occur per product unit or per material batch. Document structures include assembly steps, test parameters, and material batch information. Fields cover equipment operating values and process requirement descriptions, using measurement markings specific to power equipment manufacturing.
Constraints for vector models and indexes
Full-process documents for a single product require precise association. Chunking must avoid mixing content across batches or products. Data updates occur frequently and with fine granularity. Indexes must support incremental triggers to accommodate small-batch updates for single products or material batches. Semantic recognition of professional test parameters and insulation-related terms has high requirements. Domain-specific embedding models must be used. Errors can lead to serious issues such as insulation breakdown. Index recall accuracy directly impacts subsequent process judgments. False recall rates must be strictly controlled.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxToken | 4000 | Accommodates complete process specification and test report fragments, matching the document length of a single product |
defaultToken | 200 | Baseline token value for basic chunking, matching the core content of a single test report |
| Chunk Length | 800–1200 characters | Matches the content length of a single document for one product, preventing cross-batch content from mixing in a single chunk |
| Overlap Length | 100–150 characters | Preserves contextual association for assembly steps and test processes, preventing semantic fragment breaks |
| Similarity Threshold | 0.75–0.85 | Matches professional semantics of insulation parameters and test standards, reducing false recalls of irrelevant documents |
| Embedding Model | alibaba-emb3 | Adapts to semantic expression of professional terms in power equipment manufacturing |
Three common mistakes
- Interface prompts indicate indexing takes an extended period. The cause is missing incremental index trigger configuration. Full indexing of all documents for each product results in long runtimes.
- The interface shows the embedding model failed to load correctly. The cause is no domain-adapted embedding model is specified. The model cannot recognize specialized terms from the transformer industry such as insulation and partial discharge.
- Retrieval results deviate significantly from query semantics. The cause is incorrect chunk overlap length setting. Semantic fragments of key process steps are split and lost.
How to verify proper configuration
- View incremental index trigger logs in the FastGPT v4.17.0 index management interface. Confirm only updated documents for single products or material batches are synced. No full duplicate indexing records appear.
- Enter professional queries such as "partial discharge anomaly analysis" and "insulating oil breakdown voltage requirements". Verify retrieval results include corresponding test report fields and process descriptions.
- Test the conversion relationship between chunk length and
defaultToken. Confirm the ratio of chunk character count to token count aligns with general standards for industry documents. - Adjust the similarity threshold and compare retrieval results. Confirm recall rate changes follow expected linear trends as the threshold shifts.
The values provided are common starting points and should be measured against the reader's own samples.