What Data for This Category Includes
Data for this category originates from formula and process libraries, raw material batch ledgers, reactor DCS systems, weighing systems, LIMS laboratory information management systems, and warehouse systems. Data is updated for each production batch and every key process step. Daily documents include batch production instructions, feeding review slips, reaction process records, cleaning validation records, inspection reports, deviation notices, and change orders. Data fields include batch number, raw material specification and batch, feeding amount (kilograms/liters), reaction temperature (degrees Celsius), cleaning verification results, and additional relevant fields. Formula and cleaning-related records must be updated synchronously when switching between different products.
Constraints Imposed on HTTP Interfaces and External Systems
The batch-level data update logic for this category requires HTTP requests to carry a unique batch identifier as a request parameter, to accurately pull full data for the corresponding production batch. Multi-source heterogeneous data sources require interfaces to support compatibility with differences in field formats across systems, and enable custom field mapping. The mandatory cleaning and cleaning validation verification step requires interfaces to verify the cleaning record status of the corresponding batch before pulling feeding and reaction data. Bid quotations require aggregation of multi-dimensional data, so interfaces must support filtering associated raw material, process, and inspection data by bid project, and control return permissions for sensitive formula fields.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Authorization | Bearer ${oneapi_token} | Used for identity authentication when connecting to oneapi and domestic large models, conforms to universal interface authentication formats |
variables | {"batch_id": "", "product_type": "", "project_name": ""} | Passes category-specific parameters such as batch number, product type, and bid project name to accurately pull corresponding production data |
responseData | Filter sensitive formula fields, only return raw material costs, process duration, and qualified inspection items | Adapts to compliance requirements for bid documents, reduces invalid data transmission, and prevents sensitive information leaks |
| Request timeout | 300-600 seconds | This category has a large amount of associated data per batch, so sufficient time must be reserved to pull full data from multiple systems |
| Single pull data limit | Top 50-100 entries | Limits the number of data entries returned in a single interface call to avoid interface overload or transmission timeout |
| FastGPT Version | FastGPT v4.17.0 | This version supports custom variables and multi-source data interface connections, and adapts to the complex configuration requirements of this scenario |
Three Common Configuration Errors
- Phenomenon: Interface calls return
401 Unauthorized, or the interface prompts that the token is invalid. Cause: The Bearer prefix of theAuthorizationheader is not correctly spliced, or the access permission for the corresponding model is not checked when configuring the oneapi token. - Phenomenon: Configured domestic large models do not appear in the application or knowledge base creation interface. Cause: The call address and key of the corresponding model are not correctly bound in the oneapi channel, or the FastGPT model list is not refreshed.
- Phenomenon: Bid data fields returned by the interface are empty. Cause: Key parameters such as batch number are not correctly passed in
variables, so associated data for the corresponding production batch cannot be accurately matched.
How to Verify Correct Configuration
- Call the test interface, check that the format of the
Authorizationheader is correct, and confirm that the returned HTTP status code meets expectations. - Refresh the FastGPT model list, and confirm that the configured domestic large models appear in the drop-down menu of optional models.
- Pass preset batch number and project name as
variablesparameters, and check that the returnedresponseDatacontains valid business data for the corresponding production batch. - View the interface call log, confirm that the data pull process does not trigger timeouts or abnormal errors, and conforms to the preset verification rules.
The values provided here are common starting points and should be evaluated against the reader’s own samples.