Data Profile for Pharmaceutical Preparations Production
Quality traceability data for pharmaceutical preparations production comes from approved formulation processes, material batches, MES/electronic batch records, equipment and environmental monitoring data, LIMS, and quality management systems. Data is updated for each production batch, each key process step, and per fixed monitoring plans. Covered documents include master production files, batch production records, batch packaging records, cleaning records, inspection records, deviation/CAPA/change documents, and batch release review forms. Core fields include batch number, material batch number, process parameter values, inspection results, deviation descriptions, and equipment numbers. Corresponding formats are string, degrees Celsius, hour-minute-second time format, pass/fail flag, text description, and equipment number string.
Constraints for HTTP Interfaces and External Systems
Since data is updated per production batch and linked to GMP-compliant documents, HTTP interfaces must support triggering calls by batch number to avoid pulling redundant full datasets. Since heterogeneous production systems such as LIMS and MES are involved, interfaces must comply with different authentication and response format requirements. Since data is directly linked to batch release decisions, fields returned by interfaces must strictly match internal document structures, and data transmission integrity and traceability must be ensured. Since production operations must be synchronized with documents, interface calls must align with the time nodes of production steps to avoid disrupting normal production workflows.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
Authorization | Bearer {Dedicated API Token Assigned by LIMS System} | External system interactions for pharmaceutical preparations production must comply with GMP authentication specifications to ensure traceable data access |
variables | ["batchNumber", "materialBatch", "deviationDesc"] | Matches core fields of batch production records to ensure data passed via interfaces aligns with internal document structures |
responseData | Only include the three core fields: batch number, inspection results, and deviation records | Reduces invalid data transmission and complies with GMP data minimization requirements |
| HTTP Request Timeout | 300–600 seconds | Batch record data summarization may take extended time, preventing data pull interruptions from timeouts |
| Request Retry Count | 2 times | Occasional network fluctuations in production systems can lower the probability of data pull failures with retries |
Three Common Misconfigurations
- Phenomenon: Boolean data parsed by the HTTP component results in empty fields after processing by the conditional judgment component. Cause: The type of the boolean field is not declared in the
variablesconfiguration, causing the field to be lost during FastGPT's internal data conversion. - Phenomenon: The
getaddrinfo ENOTFOUNDerror is returned when sending an HTTP request. Cause: The domain name or IP address of the target production system is not configured correctly, or FastGPT's outbound requests are not added to the production system's whitelist. - Phenomenon: The relative path of a file uploaded via FastGPT carries a token parameter, and the target system fails to read the file normally when submitted for Flask processing. Cause: The token parameter in the path is not stripped from the HTTP request, preventing the target system from identifying the valid file path.
How to Validate Configuration Settings
- Manually trigger an HTTP interface call, and check whether the returned
responseDataincludes the preset core fields and that the field formats meet internal document requirements. - Simulate an authentication failure request scenario, confirm that the returned error prompt matches expectations, and verify that the
Authorizationconfiguration is effective. - Upload a test file and generate a relative path, strip the token parameter manually, then send an HTTP request, and confirm that the target system can read the file normally.
- View FastGPT's running logs, confirm that the HTTP request timeout time matches the configured value, and there are no abnormal timeout records.
The values provided are common starting points and should be measured against the reader's own samples.