Data profile for this category
This category covers the full quality workflow from work-in-progress to finished products. Data sources include MES systems, online inspection equipment, laboratory test records, process parameter ledgers, customer quality standards, and historical defect databases. Daily produced documents include control plans, patrol inspection records, SPC statistical reports, finished product inspection reports, nonconforming product review forms, finished product release forms, and monthly quality summary reports. Data is updated by production batch and work shift. Some online inspection data is collected continuously. Each document includes fields such as inspection item number, measured value, judgment result, inspector, timestamp, release status, and more. Units follow standard measurement formats including dimensional tolerances, defect levels, and qualified judgment ranges.
Constraints on tool calling and plugins workflow
Multi-source heterogeneous data sources for this category require tool calling to support integration with multiple external data sources such as MES, inspection equipment, and laboratory systems. It also requires adaptation to parsing of original data in different formats. High-frequency update characteristics require tool trigger logic to be bound to production batch or work shift nodes, to avoid data lag or duplicate processing. Multiple document fields covering full workflow quality nodes require tool calling to explicitly specify extraction of core fields such as inspection items, judgment results, and release status, to avoid loading invalid data. At the same time, serious error consequences such as accidental release of nonconforming products require tool calling to configure failure retry and exception log retention mechanisms, to enable rapid traceability when calling exceptions occur.
Configuration settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | Specify the mode bound to the quality ledger tool | This category requires fixed calls to preset ledger extraction and report generation tools, to prevent the model from autonomously selecting tools that deviate from business processes |
| Workflow/API function-call configuration | Define as function parameters for extracting quality ledger fields | Explicitly specify the input parameters of the called function, to prevent the model from generating invalid tool call formats |
| Tool call timeout | 600 seconds | Adapt to the parsing and summarization time of single-batch inspection data, to avoid call interruptions caused by fluctuations in data volume |
| Single batch data extraction limit | First 100 inspection records | Match the conventional inspection data scale per shift or batch, to avoid loading too much data in a single call |
| Failure retry count | 2 times | Balance call success rate and system resource consumption, to handle occasional network exceptions during multi-source data integration |
| Retry interval | 30 seconds | Adapt to the recovery cycle of multi-source data integration, to avoid resource occupation caused by frequent retries |
Three common configuration errors
- Symptom: Returns the error "400 Messages with role 'tool' must be a response to a preceding message" during calling. Cause: Tool call messages are not arranged in order, and the context for tool responses is not correctly bound in advance.
- Symptom: Returns the error "getaddrinfo ENOTFOUND 406" when sending a request using an HTTP tool. Cause: The domain name resolution or interface address of the target data source is not correctly configured, so a connection cannot be established.
- Symptom: The quality report content returned by API calls and online conversations differs significantly. Cause: The
toolChoiceparameter is not uniformly configured. Online conversations automatically select tools, while API calls do not bind the preset ledger generation tool.
How to verify correct configuration
- Trigger a simulated production batch tool call, and check if the returned ledger data includes preset inspection items, judgment results, and release status fields.
- View tool call logs, and confirm that the trigger timing matches the production batch node, with no duplicate or delayed call records.
- Simulate a data source connection exception scenario, and check if the preset failure retry mechanism is triggered and exception logs are generated.
- Compare the
functionCallparameter settings of online conversations and API calls, confirm they are consistent, and ensure the returned report content has no obvious differences.
The values provided here are common starting points, and should be evaluated against the reader's own samples.