Data Overview for This Category
Data sources for this category include ERP/MES systems, component batch ledgers, tightening and charging equipment, safety compliance testers, performance laboratory data, and after-sales fault records. Daily documents include configuration BOMs, assembly work instructions, refrigerant charging records, electrical safety test reports, performance spot check reports, and rework records.
Data is updated per single product, per shift production batch, or per spot check plan. Fields include component batch numbers, tightening torque values, refrigerant charging amounts, test voltages, fault codes, and corresponding physical units.
Constraints Imposed on HTTP Interfaces and External Systems
High-frequency, small-batch data updates per single product or shift require HTTP interfaces to support high-concurrency, low-latency request processing, and avoid blocking production workflows.
Differentiated field structures across multiple document types require interfaces to support dynamic reception of different business fields, and adapt to parameter formats for documents such as BOMs and test reports.
Multiple device and system data sources require interfaces to support standardized authentication and data format conversion, and ensure consistency for cross-system data synchronization.
Mixed-line assembly scenarios require interfaces to transmit precise product model and batch identifiers, and avoid cross-mixing of documents for different models.
Configuration Guidelines
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
Authorization | Bearer {API key assigned by FastGPT} | Follow FastGPT official API authentication standards to ensure the legality of interface calls |
variables | {"product_model": "", "batch_no": "", "collection_id": ""} | Transmit the complete home appliance model, batch number, and target collection ID to accurately match corresponding technical documents |
responseData | Return matching document list and compliance check mark | Simplify returned content to adapt to low-latency processing requirements of production systems, and avoid redundant data occupying bandwidth |
| Maximum data volume per single request | 50 items per request | Adapt to small-batch data upload scenarios for single products or shifts, and avoid request overload |
| Callback address timeout period | 30 seconds | Match the response waiting cycle of production equipment, and prevent production interruptions caused by callback failures |
Three Common Configuration Mistakes
- After calling the API to upload technical documents to a specified collection, the corresponding document does not appear in the collection list. Cause: The target collection identifier was not correctly passed in the request parameters, resulting in the document being assigned to the default collection or failing to complete binding.
- The system log for receiving callbacks shows that the returned data lacks the AI review thought process content. Cause: The initial API request did not configure the corresponding parameters to enable thought process output. FastGPT only returns the final review result.
- Calling the API to delete document data within a collection returns a 403 status code. Cause: The
Authorizationparameter does not carry a valid key correctly, or the key permission is not configured to allow delete operations.
How to Verify Successful Configuration
- Call the test interface to upload a standard home appliance assembly work instruction, and check whether the document is successfully included in the corresponding collection of the knowledge base.
- Initiate a simulated review request, and check whether the returned
responseDatacontains the expected document matching results and check marks. Matching rules can be set according to actual business needs. - After configuring the callback address, trigger a simulated production data upload, and check whether the callback interface receives complete request response data. Response fields can be adjusted according to business scenarios.
- Perform call tests with different API keys to confirm that the authentication logic of the
Authorizationparameter works normally. Permission scope can be configured as needed.
The values provided here are common starting points, and should be measured against the reader's own samples.