What Data for This Category Looks Like
The main entities in this category are suppliers of braking, steering, chassis, interior and exterior trim, and powertrain systems. Daily business documents include control plans, process flowcharts, work instructions, first-piece and patrol inspection records, SPC reports, PPAP materials, and 8D reports. Data sources include customer drawings and special characteristic lists, ERP/MES systems, measuring and inspection tools, equipment process parameters, and supplier quality certificates. Update frequency is set per batch, per shift, or via continuous collection. Documents include structured tables and long text, with standardized fields such as special characteristic numbers, tolerance ranges, and process parameters.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Multi-source, frequently updated data requires dialogue context to filter redundant history and focus on valid information for the current production batch. Multiple document types and structured fields require prompts to clearly specify extraction rules to avoid confusing parameters across different processes. Characteristics constrained by vehicle customers require dialogue to retain complete batch-related identifiers. This ensures Q&A results can be traced back to the corresponding production link and helps reduce the risk of batch defects. The need to process long documents and large data volumes requires dialogue configuration to balance context length and response efficiency.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
messages context management | Retain the last 20 rounds of dialogue plus process documents from the last 3 batches | Matches the batch update rhythm of mass production to avoid interference from redundant data |
variables binding rules | Inject the special characteristic list and equipment parameters of the current batch | Connects to real-time data from ERP/MES systems to accurately match process requirements |
| Maximum context window | 10000–15000 characters | Adapts to the standard length of a single PPAP material or 8D report |
chatId binding rules | Associate with production batch numbers | Enables context tracing for full-process dialogue of a single batch |
| Prompt template configuration | Clearly require rectification suggestions with process numbers | Aligns with mass quality control processes for automotive parts |
| Dialogue history truncation threshold | Calibrated based on actual testing | Balances response speed and context completeness |
Three Common Misconfigurations
- Front-end calls to
http://localhost:3000/api/v1/chat/completionstrigger CORS errors. The cause is that the production environment front-end domain name was not added to FastGPT's cross-origin configuration, only the local debugging address was configured. - Dialogue token consumption statistics do not match actual values. The cause is that the token counting rule for
messageswas not configured correctly, and the token count of context documents was not included. - Word documents containing 100,000 Chinese characters or Excel files with 15,000 rows cannot be loaded and queried normally. The cause is that chunk loading parameters were not set, and field mapping rules were not configured for table-type documents.
How to Verify Proper Configuration
- Launch a multi-turn dialogue for a single batch of process documents, check whether
chatIdcorrectly associates with the production number of the corresponding batch. - Call the dialogue interface to verify whether the
messagescontext retains the specified rounds of historical dialogue and the latest documents. - Upload a single PPAP material to launch a Q&A, check whether the special characteristics bound by
variablesare correctly injected into the prompt. - Simulate mass requests in the production environment to confirm that the cross-origin configuration takes effect and no CORS errors occur.
The values provided here are common starting points and should be evaluated against the reader's own samples.