Reference only; verify against the target FastGPT release and deployment documentation.
What Data Looks Like for This Category
Core data for metal material processing comes from production equipment such as spectrometers, hardness testers, manufacturing execution systems (MES), inventory management systems, and quality inspection reports. Data update cadence falls into three categories:
- Real-time updates of process parameters during production
- Batch-level quality inspection data synchronized upon inspection completion
- Inventory data updated via daily inventory counts
The fixed data document structure includes fields such as batch number, material grade, hardness value, yield strength, inventory weight, and warehouse entry time. Most units are professional units such as MPa, HRC, and tons.
Constraints for HTTP Interfaces and External Systems
Real-time production data has strict timeliness requirements. HTTP request timeout periods cannot be excessively long. Batch number and material grade are core input parameters. variables must accurately pass corresponding identifiers. Otherwise, correct data cannot be matched. Professional terminology and Chinese fields require unified request and response encoding as UTF-8. This avoids garbled characters. The fixed field structure requires responseData to explicitly specify extracted fields. This prevents invalid data from interfering with internal office system displays.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer {internal system API key} | Most internal office and production systems for metal material processing use authentication to ensure legitimate interface access |
variables | {"batchNo": "specified batch number", "materialGrade": "input material grade"} | Core identifiers such as batch and grade must be passed to accurately match production and inventory data for the corresponding batch |
responseData | ["batchNo", "hardnessValue", "yieldStrength", "stockWeight"] | Only extract fields required for business purposes. This reduces invalid data transmission and adapts to internal office system display logic |
| Request timeout | 10-30 seconds | Real-time production data has high response timeliness requirements. Overly long timeouts block employee office workflows. Settings are based on actual measurements |
| Request encoding | UTF-8 | Metal material professional terminology includes Chinese. This prevents garbled characters in interface responses or input parameters |
Three Common Configuration Errors
- Symptom: The HTTP module calls an interface and returns a 401 or 500 error. The request works normally in Postman. Cause: The
Authorizationheader is not configured correctly, or FastGPT automatically adds redundant request headers that do not match internal system authentication rules. - Symptom: Chinese fields returned by the interface are empty or display garbled characters. Cause: Request encoding is not set to UTF-8, or the internal system’s returned character encoding does not match FastGPT’s parsing encoding. This prevents professional terminology and Chinese information from displaying correctly.
- Symptom: The interface returns empty data, and there are no obvious errors in input parameters. Cause: Parameter names passed in
variablesdo not match the input field names required by the internal interface. Matching corresponding data is impossible.
How to Verify Correct Configuration
- View the FastGPT request log to confirm the
Authorizationheader is correctly carried, and the key matches the internal system configuration. - Manually copy the full request URL and parameters generated by FastGPT. Send a request using Postman to verify returned results match expected business data.
- Check the field list configured in
responseDatato confirm all core fields required for internal office system display are included. - Pass different
variablesparameters to test requests for different batch numbers and material grades. Confirm the interface returns corresponding data.
The values provided here are common starting points and should be measured against samples from actual deployments.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.