This document was written based on FastGPT v4.17.0.
What data for this category looks like
Business data for this category comes from registered process documents, raw material batch ledgers, production control systems (DCS), weighing systems, laboratory information management systems (LIMS), chromatography data systems, and quality management systems (QMS). Data is updated after each production batch or process step is completed. Daily documents include batch production records, material balance ledgers, reaction and purification records, cleaning validation records, impurity profile reports, deviation and change documents. Data formats vary across systems. Fields include batch identifiers, process step parameters, material input and output quantities, impurity test values, and supplier qualification information. Field mapping must be completed according to business scenarios.
Constraints for HTTP interfaces and external systems
Multi-source heterogeneous data sources require interfaces to support cross-system data pulling and field mapping, to avoid data anomalies caused by differences in system formats. The update rhythm based on batches and steps requires interfaces to be bound with precise business identifiers to trigger calls. Fixed polling cycles should not be used, to reduce invalid requests. The specificity of professional fields requires interface returned data to filter content not needed for tenders, while retaining core parameters such as impurity profiles and yields. Data association compliance requirements mandate guaranteeing interface transmission security and traceability, to avoid data leakage or tampering.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
Authorization | Carry a Bearer token with a 7-day validity period | Complies with security access specifications for pharmaceutical production systems, reduces token leakage risk |
| Request timeout | 120–180 seconds | Adapts to time required for cross-system data pulling, avoids request failure due to individual system response delays |
variables | Bind batch number, process step ID, supplier code | Accurately locates business data for the corresponding production batch, avoids returning irrelevant information |
responseData | Only return impurity profile, yield, and material balance data | Filters redundant content not needed for tenders, reduces interface transmission resource usage |
| Request retries | 2 times | Addresses temporary network fluctuations or brief system freezes, ensures data pulling stability |
| Field mapping rules | Map native fields from LIMS and DCS to standard tender document fields | Unifies data formats, adapts to quotation preparation template requirements |
Three common configuration errors
- The interface returns empty fields. Application logs show the
variablesparameter was not bound. Business variables such as batch number and process step ID were not injected into the interface request, so the system cannot locate the corresponding production batch business data. - The interface returns a 500 error. The log shows
Post "https://xxx tls: failed to verify. A valid SSL certificate was not configured, or the certificate has expired, so the external system cannot establish a secure connection. - The interface returns a 429 status code. Call frequency is too high, triggering system current limiting. A fixed polling cycle was used instead of following the production batch update rhythm, leading to a large number of invalid requests in a short period.
How to confirm configuration is correct
- Initiate an interface request triggered by a specified batch number, and check whether returned data includes core business fields such as impurity profiles, yields, and material balances.
- Check interface call logs to confirm the
Authorizationtoken was carried correctly, and request parameters include boundvariablesvariables. - Simulate a temporary network outage, and check whether the interface triggers the retry mechanism and returns valid data after retries.
- Compare interface returned data with raw data from LIMS and DCS systems, to confirm field mapping follows preset rules.
The values provided are common starting points and should be evaluated against the reader's own samples.