What data for this category includes
This category covers business documents from multiple entities: market supervision bureau food sampling departments, inspection institutions, and grassroots supervision offices. The documents include sampling plans, sampling forms, inspection reports, unqualified food verification and disposal reports, and risk control notices. Data sources include the food safety sampling monitoring system, the State Administration for Market Regulation sampling result query system, inspection institution LIMS, and enterprise ledgers. Data updates occur monthly. Core fields include sampling locations, inspection items, non-conforming items, disposal measures, and more. Field structures vary across different business documents. Call matching data based on specific processing links.
Constraints on HTTP APIs and external systems
Monthly updated data scale and multi-source system access requirements require APIs to support batch pulling of structured data from different sources. APIs must also adapt to field differences across different documents. Field association of core business documents directly impacts the accuracy of process guidance. APIs must verify the completeness of required fields. This prevents errors in subsequent disposal links caused by missing data. Multi-source authentication needs require APIs to configure unified authentication rules. APIs must also control data transmission volume to avoid request timeouts from redundant data. The monthly update cycle requires API pull intervals to match the data update schedule. This ensures retrieved information is current and valid.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Carry the API key issued by the external system, formatted as Bearer {token} | Match authentication requirements of multi-source business systems, avoid unAuthApiKey errors |
variables | Pass 1 to 5 core business fields such as sampling form number, inspection report number | Precisely bind context for business processing scenarios, assist process guidance in matching corresponding data |
responseData | Only return structured fields for the three core document types: sampling plans, sampling forms, and inspection reports | Reduce invalid data transmission, improve API call efficiency |
| Single request timeout period | 600 seconds | Cover the time required to pull monthly updated data from multi-source systems, avoid request interruptions |
| Context window configuration | 8000-12000 token | Accommodate monthly updated sampling data and business document content, support complete process guidance |
| FastGPT version | v4.17.0 | Adapt to specified API parameters and authentication logic, ensure API call compatibility |
Three common mistakes
- The
chatIdparameter does not take effect when calling the API, and the corresponding business scenario identifier does not appear in the FastGPT backend conversation log. Cause: The sampling batch ID is not bound as a session identifier in thevariablesof the API request, so the session context cannot be associated with corresponding business data. - The API returns the error
{"code":514,"statusText":"unAuthApiKey","message":"common:code_error.e"}. Cause: TheAuthorizationparameter format is incorrect, or a valid authentication key is not carried. This fails to match the authentication rules of the external system. - Calling a large model that only supports stream mode returns non-streaming results. Cause: The stream mode adaptation switch is not enabled in the FastGPT model configuration, so the API request does not adapt to the output format requirements of the large model.
How to confirm correct configuration
- Send a test request, check if the returned
responseDataonly includes preset core business document fields with no redundant data. - View the FastGPT backend conversation log, confirm that
Authorizationauthentication passes, and nounAuthApiKeyerrors appear. - Pass a test sampling form number, check if the
variablesparameter is correctly bound to the session context, and the process guidance can associate corresponding business data. - Call the large model API in stream mode, verify that the returned result is streaming segmented output that meets the calling requirements of the large model.
The values provided are common starting points and should be evaluated against the reader's own samples.
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.