What the Data for This Category Looks Like
The data for this category comes from animal quarantine information, supplier batches, production traceability, temperature collection, metal detection, and laboratory testing. Daily documents include quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature records, and microbial test reports. Data updates follow this rhythm: quarantine information corresponds to each batch of raw materials, processing records are generated per shift, and cold chain temperature data is pushed continuously. Fields include batch association identifiers, temperature thresholds, and test result values, with units such as degrees Celsius and colony counting units. All fields must meet category-specific traceability and compliance requirements.
Constraints on HTTP Interfaces and External Systems
The category’s traceability and compliance requirements require HTTP interfaces to carry associated parameters such as batch numbers and quarantine numbers. Without these parameters, qualified tender documents and quotations cannot be generated. Continuously pushed cold chain temperature data requires interfaces to support high-frequency real-time data reception. Multi-batch processing records require interfaces to support batch submission. For intranet deployments, public network authentication configuration must be avoided. Interfaces must also adapt to large models that only support stream mode, otherwise authentication failures or call errors will occur. Strict data verification rules require returned structured data to match category-specific fields. Otherwise, compliant document content cannot be automatically filled.
How to Configure Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer ${Intranet-deployed API Key} | Matches authentication requirements for intranet APIs such as DeepSeek, to avoid the unAuthApiKey error |
variables | {"batchId":"{{input.batch}}","quarantineNo":"{{input.quarantine}}"} | Binds batch and quarantine numbers to meet category-specific traceability association requirements |
responseData | Return only structured quotation and compliance document fields | Adapts to batch generation needs for tender documents and reduces subsequent data processing overhead |
| Timeout Setting | 600 seconds | Covers total time for multi-batch data pulling and large model generation |
| Chunk Length | 800–1200 characters | Adapts to segment processing of long texts such as slaughter records and test reports |
| Maximum Context | First 10 historical records | Prevents context overflow caused by excessive temperature control data |
Three Common Misconfigurations
- A
code:514status code appears alongside theunAuthApiKeyerror message. This occurs when the authentication format for theAuthorizationheader is configured incorrectly, and a valid key for the intranet API is not used. - A large model returns an error prompt stating "only stream mode is supported". This happens when stream mode is not enabled in the FastGPT model configuration, making adaptation to large models that only support this mode impossible.
- Generated tender documents lack fields related to quarantine certificates. This is caused by failure to bind batch and quarantine number parameters via
variables, which prevents association with category-specific traceability data.
How to Verify Correct Configuration
- Call the configured HTTP interface in an intranet environment. Check that the
Authorizationheader carries valid authentication information, and confirm that theunAuthApiKeyerror does not appear. - Trigger a model call. Confirm that the returned result uses stream format, and verify that large models that only support stream mode can connect normally.
- Pass test batch and quarantine numbers. Check that
responseDatareturns structured data containing category-specific fields. - View interface logs. Confirm that the data pulling frequency matches the update rhythm per batch and per shift, with no timeout exceptions.
The values provided are common starting points and should be measured against the reader's own samples.