What the data for this category looks like
Data in complete vehicle final assembly scenarios uses the Vehicle Identification Number (VIN) as the sole core thread. Main data sources include ERP/MES systems, VIN and configuration systems, tightening tools, EOL inspection lines, and quality traceability systems. Data updates happen in real time per vehicle, plus shift-level summary release documents. Daily documents cover production orders, VIN lists, assembly work instructions, tightening torque data, offline inspection reports, rework records, and consistency release documents. Fields include VIN, part configuration items, torque values, inspection results, supplier batches, and more. Data from different sources must be linked via VIN.
What constraints these characteristics impose on citation sources and traceability
Mixed-model production centered on VIN requires citation sources to accurately link to individual vehicles. This avoids interference from historical data of unrelated vehicles during audits. Real-time updated tightening and inspection data requires the traceability system to support fast indexing and real-time matching. This ensures that the latest process parameters can be obtained during audits. Heterogeneous data from multiple sources must be unifiedly linked via VIN. Otherwise, the traceability chain will break. A single vehicle usually includes dozens of process and inspection documents. The context length must adapt to the total vehicle data volume to avoid truncation of critical information.
How to configure settings
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
quoteMaxToken | 9000–13000 token | The total token count of full-process documents for a single vehicle’s final assembly usually falls within this range, to avoid truncation of critical traceability data |
responseData | Complete source data including VIN and tightening torque | Ensure that the vehicle’s unique identifier and key process parameters can be directly linked during audits |
detail | Enabled | Display full fields of the original inspection report, to facilitate verification of the compliance of process records |
| Maximum Context | First 2500–3000 documents linked to VIN | Adapt to mixed-model production scenarios, avoid introducing historical data from unrelated vehicles |
| Single Search Limit | Calibrated based on on-site production line testing | Match document update frequency and data scale of different production lines |
| Encoding Format | UTF-8 | Adapt to parsing requirements for documents from multiple sources, avoid garbled characters |
This configuration is based on FastGPT v4.17.0.
Three common errors
- Garbled characters appear when uploading tightening data or VIN lists in CSV format, and traceability cannot be linked after calling the knowledge base interface. Cause: The encoding format of the CSV file is not specified as UTF-8, resulting in field misalignment during parsing.
- When
quoteMaxTokenis set to a value above 3000, the large language model cannot receive context data. Cause: The value exceeds the system’s default single-round context token limit, and global configuration parameters are not adjusted synchronously. - When HTTP response detection data is directly uploaded as a citation source, corresponding records cannot be matched during audits. Cause: The response data is not converted to a structured format, and VIN linking fields are missing.
How to confirm the configuration is correct
- Upload test documents for a single vehicle, trigger the inspection and audit process, and check whether the returned results include linked VIN identifiers and original data.
- Adjust the value of
quoteMaxToken, verify that the context length returned by the large language model meets expectations, with no truncation or data loss. - Upload test data in HTTP response format, confirm that the system can correctly parse and link to the corresponding vehicle’s traceability chain.
- Check the citation statistics of the knowledge base, confirm that the number of documents linked to VIN matches the configured single search limit.
The values provided are common starting points and should be measured against the reader's own samples.