What Data Looks Like for This Use Case
Data sources include ERP/MES systems, VIN and vehicle configuration systems, tightening tools, EOL offline inspection lines, and quality traceability systems. Updates generate a dedicated ledger for each vehicle. Tightening data and offline inspection results are synchronized in real time. Shift team production records are summarized per shift. Daily documents include production orders, VIN lists, assembly work instructions, tightening torque data, offline inspection reports, rework records, and consistency release documents. Fields include unique VIN code, torque value (unit: newton-meters), inspection item pass status, supplier batch number, and workstation number, among others.
Constraints for Multi-turn Dialogue and Prompt Engineering
Single-vehicle ledger matching centered on a VIN requires dialogue to first confirm the VIN code. Without this, accurate targeting of corresponding production data is not possible. Real-time updated tightening and inspection data requires multi-turn dialogue to pull real-time data sources, avoiding expired caches. Summarization needs for multiple long documents require sufficient context capacity to prevent key information from being truncated. Mixed-model production scenarios require prompt engineering to differentiate configuration rules for different vehicle models, preventing cross-model data confusion. Ledger errors can lead to vehicle offline delays or safety risks, so mandatory field verification logic must be included in the dialogue process.
Configuration Guidelines
This page applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Dialogue context window | 8000–12000 token | Carries long text data such as VIN lists and multiple inspection reports |
messages parameter configuration | Retain the most recent 15–20 dialogue turns | Filters redundant history, focusing on query needs for the current single-vehicle ledger |
variables parameter binding | Bind VIN code and torque pass threshold | Accurately match the dedicated production ledger for a single vehicle centered on VIN |
| Tool call timeout | 600 seconds | Adapts to time requirements for pulling data from multiple ERP and MES sources |
| Automatic question optimization | Enabled | Simplifies vague user queries and automatically completes query conditions related to VIN |
chatId parameter isolation | Isolate by team/shift | Avoids context interference across sessions, ensuring independence of single-vehicle queries |
Three Common Configuration Errors
- Dialogue interfaces do not display HTTP orchestration output. Symptoms: Returned results are empty after orchestration execution, and session logs show the
variablesfield does not include interface return data. Cause: The HTTP interface output was not correctly bound to dialogue variables. - Uploaded production ledger files cannot be read in local deployment scenarios. Symptoms: Knowledge base file upload functions normally, but file content cannot be parsed during dialogue calls. Cause: File read permissions for dialogue scenarios were not configured.
- Vector index progress remains stuck for an extended period. Symptoms: Index tasks stay in a running state with no progress updates. Cause: Chunking rules were not adjusted for long unique identifier fields such as VIN codes, leading to chunking processing timeouts.
How to Verify Correct Configuration
- Initiate a test dialogue with a specific VIN code, and check if returned results include tightening data and offline inspection reports for the corresponding vehicle model.
- Trigger an HTTP orchestration request, and verify that the
messagesandvariablesfields in session logs carry expected interface output data. - Upload a test rework record file, initiate a content query in the dialogue, and confirm that field information such as rework workstation and defect type from the file can be extracted.
- Start a vector index task, and observe if the task completes within a reasonable time frame with no abnormal error prompts.
The values given are common starting points and should be measured against the reader's own samples.