What the data for this category looks like
Data for complete vehicle final assembly scenarios primarily comes from ERP/MES systems, VIN and configuration systems, tightening tools, EOL inspection lines, quality traceability systems, and similar sources. Daily documents include production orders, VIN lists, assembly work instructions, tightening data, offline inspection reports, rework records, consistency release documents, and more. Data update frequencies fall into three categories: per vehicle, real-time, and shift-based. Core fields include unique VIN codes, torque parameters, inspection results, station numbers, and others. Document structures vary significantly, with both structured numerical data and unstructured operational guidance text.
What constraints do these characteristics impose on vector models and indexes?
Mixed-model production tied to VIN as the single vehicle identifier requires vector indexes to support precise association of documents for the corresponding vehicle batch, to avoid recall interference across different vehicles. High-frequency real-time updates require indexes to support incremental embedding and quick synchronization, to prevent inspection results from becoming invalid due to data delays. Mixed multi-type document formats require chunking rules to adapt to different document structures, to avoid splitting continuous operational steps in work instructions or breaking the numerical correlation of tightening data. Additionally, the maxToken setting must adapt to the total length of spliced multiple documents, to avoid exceeding the large model’s processing limit. Business attributes with severe potential error consequences also require recall result confidence to strictly match preset thresholds, to reduce recall risks related to mismatching or missing installations.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the length of single-section operational descriptions in assembly work instructions, avoids splitting steps across stations |
maxToken | Set to the selected embedding model/service maximum supported input tokens | maxToken is the input-token maximum supported by the selected embedding model/service; verify it in that provider configuration. See the v4.17.2 model-field reference. It is not retrieval/LLM context or a FastGPT-wide fixed cap. |
| Similarity Threshold | 0.75–0.85 | Calibrated via actual testing, balances recall accuracy for structured tightening data and unstructured guidance text |
| Single Search Limit | Top 10 results | The number of documents associated with a single VIN is limited in final assembly scenarios, avoids redundant recalls interfering with core results |
| Reranking | Enabled | Sorts recall results by station priority, adapts to the operational sequence logic of equipment checks |
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. |
Three Common Configuration Errors
- After configuring the Doubao embedding API, test calls return 404 Not Found. This occurs when the API endpoint address is not correctly configured, the key does not have permission to access the corresponding vector service, or the interface format does not match FastGPT requirements.
- After batch replacing the embedding model, the multilingual recall rate of the existing knowledge base does not meet standards. This happens when full re-embedding is not performed on existing knowledge base documents, and only vector data for newly uploaded documents is updated.
- Default indexes generated for manually inserted final assembly documents disappear after several hours. This is caused by not enabling index persistence configuration: the system triggers the automatic cleanup threshold for temporary indexes, which conflicts with the high-frequency update index cleanup rules used in final assembly scenarios.
How to Confirm the Configuration is Properly Set
- Navigate to the vector model configuration page in FastGPT v4.17.0, confirm the target embedding model is selected and correct API information is filled in, initiate a test interface call, and verify that normal vector data is returned.
- Upload a typical final assembly work instruction, review the chunking preview results, confirm that content is not split across station steps, and that it complies with the preset chunk length rules.
- Submit an equipment check query targeting tightening data anomalies, verify that the similarity scores of recall results fall within the preset threshold range, and that correct VIN and station information is associated.
- Wait 12 hours, check the knowledge base index list, confirm that manually inserted document indexes are not automatically cleaned up, and verify that the persistence configuration is active.
The values provided here are common starting points, and should be evaluated against the reader's own samples.