This document is written based on FastGPT v4.17.0.
What data looks like for this category
Data for this category includes animal quarantine information, supplier batches, production traceability records, cold chain temperature collection data, metal detection results, and laboratory microbial test reports. Daily documents include quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature logs, and microbial test reports. Data is updated per batch and per shift. Cold chain temperatures must be collected continuously. Fields include batch identifiers, quarantine numbers, temperature values, detection times, total bacterial counts, and more. Common units are degrees Celsius, colony-forming units per gram, and batch numbers.
What constraints do these characteristics create for database and operations workflows
Multi-source heterogeneous data for this category requires databases to support unified storage and efficient associated queries. High-frequency, small-volume data from continuous temperature monitoring needs to support high-concurrency write operations. Strong binding requirements between batches and quarantine information demand stable transaction consistency from databases. Data related to biosecurity and compliance traceability requires tamper-proof storage and audit logs. In production environments with cross-node deployment, connection stability after primary node failover must be managed to prevent traceability link interruptions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
| MongoDB Connection String | mongodb://[account:password@]node1:port,node2:port/?replicaSet=rs0&maxPoolSize=20 | Adapts to replica set deployment scenarios, resolves disconnection issues after primary node failover, and matches concurrent connection requirements for production data |
| PostgreSQL Connection String | postgresql://account:password@host:port/database_name?pgvector=true&pool_timeout=60 | Supports pgvector vector storage, enables similarity retrieval for microbial test data, and sets a 60-second timeout to accommodate bulk test data imports |
| Maximum Database Connections | 15–25 | Matches concurrent request volume for per-shift production batches, prevents service interruptions caused by exhausted connections |
| Redis Cache Address | redis://:password@host:port/0?maxmemory-policy=allkeys-lru | Caches frequently queried batch association information to reduce load on the primary database |
| Milvus Vector Database Address | Calibrated via actual testing | Adapts to vector retrieval needs for large-scale microbial test data, matching bulk test volumes at slaughter facilities |
| Vector Retrieval Similarity Threshold | 0.85–0.95 | Calibrated via actual testing, defines the similarity range for distinguishing qualified and unqualified test data |
Three Common Misconfigurations
- Issue: After deploying a MongoDB replica set, connection timeout errors occur during primary node failover. Logs display "Failed to reconnect to primary". Cause: The replica set name was not specified in the connection configuration, so the driver cannot automatically discover the new primary node and restore the connection.
- Issue: When integrating pgvector, vector retrieval functions fail to return results normally, and the interface displays "vector extension not enabled". Cause: The pgvector=true parameter was not added to the PostgreSQL connection string, so the database does not load the vector extension plugin.
- Issue: When importing bulk cold chain temperature data, the service returns a 503 status code with a "connection exhausted" prompt. Cause: A reasonable maximum database connection count was not set, and concurrent write requests exceed the connection pool limit, preventing new connection requests from being processed.
How to Verify Proper Configuration
- Run batch association queries to verify that association results between quarantine certificates and slaughter records are complete. Adjust association rules based on actual business requirements.
- Simulate a primary node failover operation to check that database connections automatically restore, with no disconnection error messages.
- Import bulk cold chain temperature data and monitor connection pool utilization to ensure no connection exhaustion occurs.
- Run vector retrieval tasks for microbial test data to verify that matching results align with business judgment standards.
The values provided here are common starting points and should be evaluated against samples appropriate to the specific use case.