What data looks like for this category
Production data for this category includes documents such as casting process cards, batching lists, heat records, mold history records, first article inspection records, material test reports, and non-conforming product review forms. Data sources cover ERP material master data, melting furnaces and thermometers, spectrometers, MES heat records, mold management systems, customer drawings, and technical specifications. Updates occur per heat, per batch, or daily. Fields include alloy batching ratio, melting temperature, pouring parameters, mold cycle times, defect types, and more. Units involve degrees Celsius, kilograms, ppm, and others.
What constraints do these characteristics impose on HTTP interfaces and external systems
Dispersed, multi-source data sources require HTTP interfaces to adapt to heterogeneous output formats and field naming conventions of different devices and systems. This increases the complexity of parameter validation. The high-frequency update rhythm (per heat, per batch, or daily) requires keeping interface response latency within a reasonable range. This avoids backlogs of production ledger data. Interfaces must support structured parameter and file stream transmission to handle multiple field types and attached documents such as non-conforming product review forms. Cross-departmental custom field mapping requirements require interfaces to reserve flexible field adaptation configuration options.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Authorization | Bearer {企业自定义令牌} | Complies with OAuth2 authentication formats used by most external production systems to secure interface access |
variables | {"炉次ID": "${input.炉次号}", "熔炼温度": "${data.测温仪数据}"} | Maps real-time fields from production systems to interface request variables to meet multi-source data splicing requirements |
responseData | {"台账ID": "id", "生成状态": "status"} | Extracts return results using core fields of production ledgers to facilitate subsequent system parsing and invocation |
| Interface timeout period | 30–60 seconds | Adapts to data collection latency of equipment such as melting furnaces, avoids timeouts triggered by high-frequency requests, and aligns with FastGPT v4.17.0 workflow node configuration logic |
| Request retry count | First 2 attempts | Balances interface stability and resource consumption to address temporary network fluctuations |
| Attachment transmission switch | Calibrated based on actual testing | Adjust configuration based on whether attachments such as scanned heat records and spectrometer test reports need to be transmitted |
Three common mistakes
- Phenomenon: Returns 401 status code after calling the interface, authentication failed. Cause: Failed to correctly configure the token format of the
Authorizationparameter, or the token permissions do not cover the target production system interface. - Phenomenon: After passing knowledge base retrieval results in a workflow to an HTTP interface, AI conversations cannot correctly associate production ledgers. Cause: Failed to configure return field mapping rules according to the
responseDatarequirements, preventing the AI from obtaining key identification information of the ledgers. - Phenomenon: After restarting the FastGPT service, the configured
Authorizationtoken is lost, causing interface authentication failure. Cause: Authentication information was only saved in a temporary session instead of global configuration, so the configuration becomes invalid after the service restarts.
How to confirm correct configuration
- Manually trigger an interface request to check whether the fields in the return result match the mapping rules configured in
responseData. - Check whether the format of the
Authorizationparameter meets the authentication requirements of the target system, and verify the authentication pass status using a mock request tool. - Simulate high-frequency requests initiated according to production rhythm to confirm that interface response latency meets business requirements and there are no continuous timeouts.
- After restarting the FastGPT service, check whether configuration items such as
Authorizationare still retained and no loss occurs.
The values provided here are common starting points and should be measured against the reader's own samples.