What data for this category looks like
Ledger data for pharmaceutical distribution comes from internal inventory management systems, cold chain temperature and humidity monitoring terminals, and pharmaceutical regulatory reporting interfaces. Update frequencies fall into three categories: real-time (cold chain environment temperature and humidity data), daily (inbound and outbound ledgers), and periodic (batch summary reports).
Single ledger entry fields include generic drug name, approval number, batch number, inbound and outbound quantity, inventory balance, ambient temperature and humidity, operation time, associated document number, and more. Units include boxes, vials, ℃, %RH, cases, and others. Single entries are short in length, but aggregated batch datasets per task can reach tens of thousands of entries.
Reference only; verify against the target FastGPT release and deployment documentation.
Constraints on database and operations workflows
Multi-source heterogeneous data access requires compatibility with both relational and non-relational database storage. High-frequency real-time temperature and humidity data generates a large volume of small write requests, which places demands on the concurrent processing capacity of database connection pools. Pharmaceutical batch numbers and operation time are high-frequency query fields, so targeted indexes must be created to improve aggregation efficiency.
Batch ledger aggregation tasks often involve associative calculations on tens of thousands of entries, which consumes significant database resources. Reasonable concurrent allocation quotas are required. Pharmaceutical distribution data must meet regulatory retention requirements, so periodic full backup and incremental synchronization mechanisms must be configured.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PG_URL | postgresql://med_user:med_pass@db-host:5432/med_trade | Pharmaceutical distribution inventory ledgers and batch data are stored in PostgreSQL, using standard PostgreSQL connection format |
MONGODB_URI | mongodb://temp_user:temp_pass@mongo-host:27017/med_env?authSource=admin | Unstructured time-series data such as cold chain temperature and humidity is stored in MongoDB. Specify the authentication database to ensure access security |
DB_MAX_LINK | 20–30 connections | The peak concurrent query volume for a single batch aggregation task is approximately 15. Reserve redundant connections to handle temporary peak scenarios |
REDIS_URL | redis://:redis_pass@redis-host:6379/1 | Used to cache high-frequency queried basic drug information and intermediate ledger aggregation results. Use an independent database number to avoid data interference |
Three Common Configuration Mistakes
- Symptom: Large model interfaces return 429 status codes, or workflow executions prompt call frequency restriction alerts. Cause: No concurrency rate limiting rules are configured. Batch ledger aggregation tasks trigger a large number of large model calls simultaneously, exceeding interface call quotas.
- Symptom: PostgreSQL queries return empty results or show significantly increased latency. Cause: No indexes are created for drug batch numbers and operation time fields. Full table scans are triggered during batch aggregation, consuming excessive database resources.
- Symptom: Database connection components return the
Access denied for usererror during debugging. Cause: Incorrect username, password, or authentication database parameters in the configuredPG_URLorMONGODB_URI. Corresponding database read/write permissions are not granted.
How to Verify Correct Configuration
- Run a single ledger aggregation test task, review database connection logs, and confirm that the connection status of
PG_URLandMONGODB_URIis normal. - Adjust the value of
DB_MAX_LINK, simulate concurrent query scenarios, observe database resource usage, and confirm that the connection count configuration matches current task peak loads. - Execute a batch aggregation task, check whether intermediate results are generated in the Redis cache, and confirm that the read/write function of
REDIS_URLoperates correctly. - Review large model call logs, confirm that concurrent request counts meet interface quota requirements, and verify that rate limiting configurations are active.
The values provided are common starting points and should be measured 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.