What data looks like for this category
Data for this category originates from customer drawings, special characteristic lists, ERP/MES systems, measuring tools, equipment process parameters, and supplier quality certificates. Commonly generated documents include control plans, process flowcharts, work instructions, first article and patrol inspection records, SPC reports, PPAP materials, and 8D reports. Production batches and shifts trigger data updates, while some equipment parameters are collected continuously. Documents include fields such as special characteristic numbers, process parameter thresholds, inspection values, and corrective measures. Some documents require matching the unit format specified by the customer.
What constraints these characteristics impose on tool calling and plugins
Data sources for this category are scattered, and update rhythms vary significantly. Tool calling must support synchronized pulling from multiple data sources, and plugins must adapt to the interface formats of different systems including ERP, MES, and measuring tools. There are diverse document types with strict field requirements, so plugins must support triggering differentiated review rules based on document types such as control plans and 8D reports. High-volume production requires tool calling to support parallel batch document processing, to avoid review delays for single production batches. Subject to strict quality constraints from vehicle customers, tool return results must retain the field associations of the original documents, and match the unit and format requirements specified by the customer.
This configuration is based on FastGPT v4.17.0.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Automatically matches review tools for multiple document types such as control plans and 8D reports for this category, no manual configuration required |
| Workflow/API function-call configuration | disabled | Prevents accidental triggering of unnecessary review logic when manually calling tools, adapts to the automatic review needs of this category |
| Maximum single tool call count | Top 5–7 entries | Matches the typical number of documents per production batch, avoids exceeding system resource capacity |
| Batch parallel processing count | 2–4 batches | Adapts to the high-volume production rhythm of this category, balances processing efficiency and resource usage |
| Context window size | 8000–12000 token | Accommodates metadata of batch documents, customer special characteristic requirements, and review rules, adapts to long-text processing needs |
| Maximum response timeout | 300 seconds | Covers the typical time required for cross-review of batch documents, avoids disrupting production rhythms |
Three common mistakes
- Issue: The text stream returned by tool calls only appears as unformatted plain text, with no way to distinguish review items from conclusions. Cause: Markdown output configuration for tool calls is not enabled, preventing structured review results from being rendered in proper format.
- Issue: When forwarding the FastGPT interface through an intermediate service, page data becomes stuck and unresponsive after stream data reception is interrupted. Cause: Stream data breakpoint resume logic is not configured, and the intermediate service does not retain session context.
- Issue: FastGPT deployed via Docker fails to load custom plugins. Cause: The packages/plugins/register directory is not created inside the container, or the corresponding plugin configuration files are not mounted.
How to confirm the configuration is correct
- The FastGPT configuration interface can be accessed to confirm the
toolChoiceparameter is set toauto, matching the recommended setting in the configuration table. - A single control plan document can be uploaded to trigger a review, and the returned results can be checked for markdown format display.
- Multiple production documents of different types can be uploaded to verify that tool calls can process them in parallel without timeouts.
- The FastGPT container deployed via Docker can be logged into to confirm the packages/plugins/register directory exists and the configuration files have been mounted.
The values provided are common starting points and should be measured against the reader's own samples.