What the Data for This Category Looks Like
The equipment inspection and maintenance Q&A data for electric motor and generator manufacturing comes from multiple sources. These include electromagnetic design data, copper wire and insulating material batch records, winding equipment logs, balancing machine data, electrical test bench reports, and production data from ERP/MES systems. Daily documents cover winding process cards, insulation treatment records, dynamic balance reports, voltage withstand insulation test records, and type and factory inspection reports. Data is updated once per device or batch of products. Synchronized updates are made for each type modification. Documents include core fields such as winding turns, wiring methods, impregnation and drying parameters, vibration and temperature rise values, and withstand voltage values. Corresponding unit information is attached to each field.
Constraints on Vector Models and Indexing From These Data Characteristics
Multi-source heterogeneous data sources require index configurations to support unified chunking rules for structured device logs, semi-structured test reports, and ERP text. High-frequency updates tied to batches or type modifications require incremental index trigger logic. This prevents excessive resource usage from full reindexing. Core numeric fields with units, such as winding turns and withstand voltage values, are included in documents. Field and unit information must be retained during vector generation to ensure matching accuracy. Fixed document structures require chunking to align with paragraph logic. This avoids cutting continuous content with core parameters. It also controls single chunk length to fit the context window limit of defaultToken.
How to Set Up the Configuration
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the single-paragraph length of winding process cards and test reports, avoids splitting across core parameters |
| Overlap Length | 100–200 characters | Preserves critical cross-paragraph information such as winding turns and wiring steps |
| Similarity Threshold | 0.72–0.85 | Adapts to the numeric precision of motor test data, filters low-relevance historical reports |
| Single Search Limit | Top 6 results | Controls context length to avoid exceeding the maxToken limit |
defaultToken | Set from representative sample documents | defaultToken is the default token count for direct text chunking; tune it on representative source documents. See the v4.17.2 model-field reference. It is not a vector dimension, embedding-model input cap, or retrieval/LLM context. |
| Maximum Context | 12000 characters | Adapts to multi-document consolidation needs for batch inspection Q&A |
Three Common Misconfigurations
- Phenomenon: Multiple duplicate results for the same document are returned during retrieval. Cause: No deduplication is configured for multiple index entries of the same document chunk, leading to multiple index branches recalling the same paragraph at the same time.
- Phenomenon: Index tasks remain in a queued state for extended periods. Cause: No incremental index trigger rules are set. Full reindexing covers high-frequency updated data per device or batch each time, consuming significant computing resources.
- Phenomenon: Results matching winding parameters cannot be recalled during retrieval. Cause: The corresponding embedding model is not specified in the index configuration. This creates a mismatch between the vector generation model and the Q&A query model.
How to Confirm the Configuration Is Correct
- Upload a winding process card, run a chunk preview, and confirm that core parameters such as winding turns and impregnation temperature are not split across paragraphs.
- Initiate an inspection Q&A query, check that the number of returned results matches the single search limit configuration, and that no duplicate paragraphs from the same document are recalled.
- View index task logs, confirm that incremental indexing only triggers for type modifications or batch updates per device, with no high-frequency full reindexing runs.
- Verify the embedding model configuration, confirm that the model used during indexing matches the model used for Q&A queries, to avoid vector space mismatches.
The values provided are common starting points and should be measured against the reader's own samples.