What data looks like for this use case
Data sources for complete vehicle final assembly scenarios include ERP/MES systems, VIN and vehicle configuration systems, supplier batch management systems, tightening tool terminals, EOL offline inspection lines, and quality traceability systems. Daily documents cover production orders, VIN lists, assembly work instructions, tightening torque data, offline inspection reports, rework records, compliance release documents, and more. Data update frequency follows per-vehicle execution: key tightening data and offline test results are synced in real time, while batch configurations and release documents are updated per shift. Document fields include unique VIN code, torque value (unit: newton meters), inspection item number, part batch number, station number, and others. Different documents are linked using the VIN code as the unique identifier.
What constraints do these characteristics impose on vector models and indexing
Using the VIN code as the unique association identifier requires indexes to store vectors grouped by VIN, to avoid mixing configuration data across vehicles. Real-time updated tightening data and test results require indexes to support incremental synchronization, to prevent performance loss from full index rebuilding. Documents include both text-based work instructions and numerical tightening data, so vector models must support vectorization conversion for both text and structured numerical data. Error consequences include vehicle line stoppage and recall risks, so index recall accuracy must meet business verification thresholds to avoid deviations in process document and exception report drafting caused by incorrect matching.
The following configurations are based on FastGPT v4.17.0.
How to set configurations
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the paragraph length of assembly work instructions, prevents single chunks from containing process content across multiple stations |
| Overlap Length | 100–150 characters | Retains contextual association between adjacent chunks, ensures VIN-linked configuration information is not truncated by chunking |
| Similarity Threshold | 0.75–0.85 | Meets high-precision retrieval requirements for final assembly scenarios, filters low-correlation process documents and test data |
| Single Search Limit | Top 20 entries | Covers retrieval requirements for hundreds of configuration items per vehicle, avoids processing delays from excessive returned results |
defaultToken | 2000 | Adapts to the vectorization length of a single process document, meets full text requirements for work instructions |
maxToken | 8000 | Limits total token consumption for a single batch of indexes, avoids exceeding model call quota limits |
Three common configuration errors
- Switching the knowledge base vector model results in stuck progress and inability to revert to the original model. Phenomenon: The index progress bar stalls, no synchronization logs appear in the background, and the model selection dropdown cannot select the previously configured original model. Cause: Failed to automatically clean up associated index metadata from the old vector library, and failed to trigger a full reindex task.
- 404 error when configuring Doubao embedding. Phenomenon: Test retrieval returns "404 page not found". Cause: Failed to correctly configure the API gateway address for Doubao embedding, causing requests to fail to reach the target service.
- After uploading an assembly schematic, search results still show the old image. Check the image's data row and training-task status. The FastGPT v4.17.2 release notes describe image imports being persisted after parsing and tracked through
parsed,indexing, andindexed; data awaiting indexing is not returned by retrieval. Recheck search results once the row reachesindexed.
How to verify successful configuration
- Upload a sample assembly work instruction document containing a single VIN, check that index generation progress completes normally with no error prompts.
- Enter the search term "tightening torque requirements for VIN XXX", confirm returned results include tightening data for the corresponding station, with matching degree within the preset threshold.
- Attempt to switch the vector model, confirm that configurations can be saved normally and reindexing is triggered, with the progress bar advancing normally.
- Check index storage usage, confirm that preset storage space limits are not exceeded, with no overflow errors.
The values given are common starting points and should be measured against the reader's own samples.