Q&A for Process Procedures and Technical Standards for Active Pharmaceutical Ingredient and Pharmaceutical Intermediate Production: Deployment and Upgrade

This category of data comes from registered process documents, raw material batch ledgers, DCS production control systems, weighing systems, LIMS

What data for this category looks like

This category of data comes from registered process documents, raw material batch ledgers, DCS production control systems, weighing systems, LIMS laboratory information management systems, chromatography data systems, and QMS quality management systems. Daily documents include batch production records, material balance sheets, reaction and purification process records, cleaning validation records, impurity profile reports, deviation and change control documents. Data updates trigger upon completion of each batch production and each process step. Fields include process step parameters, impurity content, solvent residue levels, yield data, and compliance review status.

What constraints these characteristics impose on deployment and upgrade

Because data sources are scattered and updated in real time with production batches, multi-source data synchronization links must be configured during deployment to ensure unified access to documents and structured data from different systems. Daily documents include multiple types of structured and unstructured content, so parsing rules for different formats must be adapted. The update frequency of process parameters and compliance data is high, so data refresh intervals and index update strategies must be adjusted during upgrades to avoid delayed Q&A results. The accuracy of core fields such as impurity profiles and yields directly affects the credibility of Q&A results. During the deployment phase, the integrity of data field mapping must be verified to prevent missing key information.

How to set configurations

Configuration ItemRecommended ValueRationale
REDIS_URLredis://r-xxxx.redis.rds.aliyuncs.com:6379Adapt to the caching requirements of multi-node deployment in active pharmaceutical ingredient production scenarios, ensuring stable session and vector index caching
MONGODB_URImongodb://user:pass@host:port/dbname?authSource=adminStore unstructured documents such as batch production records and impurity profile reports, supporting high-concurrency read and write operations
STORAGE_S3_ENDPOINToss-cn-hangzhou.aliyuncs.comStore large-volume raw data files exported from chromatography data systems, adapting to large-capacity storage requirements
DB_MAX_LINK200–300Adapt to concurrent requests for process Q&A initiated by multiple departments simultaneously, avoiding database connection exhaustion
FILE_TOKEN_KEY32-bit random encrypted stringProtect download permissions for production documents and prevent unauthorized access

Three common mistakes

  • Phenomenon: Pulling the Redis image fails during docker-compose deployment, with an error indicating the image does not exist or pull timed out. Cause: The official Alibaba Cloud Redis image address is not used, or the configured image address format is incorrect.
  • Phenomenon: Q&A results cannot return the latest impurity profile report data. Cause: The DB_MAX_LINK parameter is not configured correctly, and insufficient database connection pools cause data synchronization interruptions.
  • Phenomenon: When configuring vector index models in FastGPT v4.17.0, vector indexes for process data cannot be built properly. Cause: Model adaptation parameters for structured data fields in active pharmaceutical ingredient production scenarios are not adjusted, leading to index matching failures.

How to confirm configurations are set correctly

  • Run the docker-compose up command, check service startup logs, and confirm there are no error messages.
  • Upload a batch production record document, initiate a process-related Q&A, and verify that the returned results contain key content from the document.
  • Attempt to access an uploaded process document attachment, and verify that permission checks function normally.
  • Simulate concurrent Q&A requests initiated by multiple departments, observe service operating status, and confirm that the connection pool configuration takes effect.

The values provided are common starting points and should be measured against the reader's own samples.