What data for this category looks like
Typical entities in this category are carbonated drink, juice, tea, functional beverage, and packaged drinking water factories. Daily documents include recipe sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspection reports, and finished product inspection reports. Data sources include water source and raw material testing systems, recipe management systems, online sugar and acidity monitoring equipment, filling equipment, visual inspection equipment, and LIMS systems. Data update frequency is per batch, per shift, or continuous collection. The most time-consuming business steps are reviewing concentrate and additive dosing, stabilizing water treatment blending and sterilization processes, and handling filling liquid level, sealing, and label batch number exceptions. Consequences of errors include product recalls due to recipe or additive over limits, microbial contamination, incorrect labels, and shelf life issues. Compared to dairy product categories, this category has high-speed liquid filling, synchronized recipe and packaging adjustments, prominent water treatment and aseptic control requirements, no raw milk acceptance steps, and higher product switching frequency.
Constraints on deployment and upgrade from these characteristics
Multi-source, high-frequency real-time data requires deployment to support multi-system integration configuration logic. This avoids data synchronization delays or loss. Frequent product switching requires upgrades to support fast knowledge base version switching and incremental updates. This adapts to process standard differences across categories. Strict water treatment and aseptic control requirements mean retrieval result confidence configuration must adapt to high-precision data verification logic. This prevents incorrect process parameters from being called. Large volumes of inspection reports and raw data files require deployment to configure sufficient storage resources and efficient file reading interfaces. This ensures the Q&A system’s response speed. Additionally, the high risk of serious business errors from mistakes requires upgrades to retain historical version rollback mechanisms. This reduces business risks from abnormal deployments.
How to set configurations
These configurations are adapted for FastGPT v4.17.0.
| Config Item | Suggested Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://fastgpt:${MONGODB_PASSWORD}@mongo:27017/fastgpt?authSource=admin | Adapts to storage needs for unstructured data such as beverage factory process records and inspection reports |
PG_URL | postgresql://fastgpt:${PG_PASSWORD}@postgres:5432/fastgpt | Stores structured process data such as recipe sheets and water treatment logs |
DB_MAX_LINK | 12–18 | Prevents database connection exhaustion during high-concurrency retrieval, adapts to high-frequency data requests from continuous shift collection |
STORAGE_S3_ENDPOINT | http://minio.internal:9000 | Stores visual inspection images and raw sugar and acidity data files generated by online testing |
REDIS_URL | redis://:${REDIS_PASSWORD}@redis:6379/0 | Caches frequently called process procedures and standard parameters, reducing database load |
Three common configuration mistakes
- Symptom: Docker container restarts continuously. Logs show MongoDB connection errors or authentication failures. Cause: Authentication information for
MONGODB_URIis not configured correctly, or sufficient storage is not allocated to MongoDB for storing large volumes of inspection record data. - Symptom: Container fails to start after configuring
PG_URLwhen deploying via Docker on a network-attached storage device. A database connection failure prompt appears. Cause: The default PostgreSQL port is not opened in the device firewall, or the container network is not connected to the local database instance. - Symptom: Number of retrieval results is far lower than expected. Full process procedure content cannot be matched. Cause: The
DB_MAX_LINKparameter is not adjusted to a reasonable range, leading to database connection pool exhaustion and failure to pull full process data.
How to confirm the configuration is complete
- Run the built-in database connectivity check command in the container. Verify the connection status of
MONGODB_URIandPG_URL, confirm no authentication or network exceptions. - Upload a simulated beverage factory inspection report file. Check if the storage service corresponding to
STORAGE_S3_ENDPOINTcan store and read files normally. - Initiate a retrieval request related to process procedures. Confirm the container has no connection exhaustion errors and the retrieval process executes normally.
- Access the configured frontend domain name. Verify that the Q&A interface loads normally and can return matching process standards and procedure content.
The values provided are common starting points and should be measured against the reader's own samples.