What Data for This Category Looks Like
It typically serves manufacturers of pumps, valves, compressors, reducers, machine tools, and non-standard equipment. Daily data includes assembly process cards, BOMs, torque records, clearance adjustment records, commissioning reports, factory inspection sheets, and packing lists. Data comes primarily from PLM/ERP systems, part inspection data, assembly tool collection terminals, test benches, MES systems, and customer technical agreements. It is updated per device or per order, with fields including specific assembly parameters, inspection values, and debugging records. Common units cover professional parameters such as torque values, clearance tolerances, and test pressures.
Constraints on Vector Models and Indexes
The multi-source heterogeneous data structure of this category requires vector models to adapt to both textual process documents and numerical assembly parameters. This prevents loss of key professional information such as tolerances and torque in single-modal vectors. Based on update frequency per device or order, indexes must support lightweight incremental updates. This avoids excessive resource usage from full index reconstruction. The multi-variant order-based assembly business model requires indexes to support quick filtering and recall by order number or device model. This narrows the retrieval scope. Assembly parameters have strong correlations, so complete parameter context must be retained during chunking. This prevents loss of corresponding relationships between clearance adjustment and torque records after splitting.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the paragraph length of documents such as assembly process cards and BOMs, retains complete assembly steps and parameter correlations |
| Overlap Length | 100–150 characters | Prevents key parameters such as clearance adjustment and torque records from being truncated across chunks |
maxToken | 4096 token | Adapts to the input length limit of mainstream vector models, covers the full text content of assembly debugging records |
| Vector Model Selection | Alibaba Cloud General Multimodal Embedding multimodal-embedding-v1 | This category contains mixed data of textual process documents and numerical assembly parameters, and multimodal models can adapt to both types of content |
| Similarity Threshold | 0.75–0.85 | Filters low-match assembly error records, focuses on inspection data strongly related to fault phenomena |
defaultToken | Calibrated via actual measurement | Adapts to the parameter text length of different device models, avoids key information overflow caused by fixed values |
Three Common Misconfigurations
- Symptom: Indexing takes too long, and the interface shows "Index task timed out" status. Cause: The chunking strategy was not adjusted for incremental data per single device, and full scanning of all assembly data caused excessive retrieval load.
- Symptom: Dataset fields after vectorization are empty, and retrieval results have no matching content. Cause: Complete context of key parameters such as clearance adjustment and torque records was not retained, and core numerical fields were truncated during chunking.
- Symptom: Unable to add indexes in a docker-compose deployment environment, and the interface prompts "Index node not ready". Cause: Port mapping for the vector index service was not opened in the docker-compose configuration file, and the corresponding data volume for storing index metadata was not mounted.
How to Verify Proper Configuration
- Run a single device assembly data indexing task, check the indexing progress logs, and confirm that the number of chunks matches the expected number of document paragraphs.
- Input a fault phenomenon, retrieve the results, and verify that the returned content includes the assembly process card of the corresponding device and relevant records of the faulty part.
- Check the vector model call logs, confirm that the input text length does not exceed the limit set by
maxToken, and there are no truncation errors. - Check whether the
defaultTokenconfiguration adapts to the current device's parameter text length, to avoid content overflow. - Confirm that the index service container in the docker-compose deployment is running, and the port mapping and data volume configurations are correct.
The values provided are common starting points and should be measured against the reader's own samples.