What the data for this category looks like
The data for this category covers raw milk acceptance and the entire production process. Sources include milk source testing equipment, production MES systems, homogenization and heat treatment equipment, online temperature flow monitoring devices, CIP cleaning systems, and LIMS laboratory management systems. Data is generated per milk tank, per batch, or in real time. Corresponding documents include raw milk acceptance records, standardized ingredient lists, pasteurization/UHT heat treatment records, CIP cleaning records, filling operation records, reserved samples and microbial test reports. Fields include raw milk antibiotics and physicochemical indicators, heat treatment time and temperature parameters, aseptic filling verification data, and others. Units include ℃, seconds, CFU/mL, and more.
What constraints these characteristics impose during deployment and upgrade
The high-frequency, real-time, small-batch data characteristics of this category require low-latency streaming data access during deployment, to avoid batch misalignment caused by batch synchronization. The data includes professional testing indicators and equipment parameters, so a dedicated professional terminology thesaurus must be configured to match industry keywords. For intranet deployment scenarios, HTTP proxy configuration with username and password must be supported to ensure access to external models and knowledge bases. During upgrades, historical traceability data associated with batches must be retained, to avoid losing the binding relationship between production data and tender documents. At the same time, the strong compliance requirements of the data require stable storage and database connection configuration during deployment, to ensure that verification data is not tampered with or lost.
Configuration Settings
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–150 connections | Adapts to high-frequency data write requests per milk tank and per batch, to avoid database connection exhaustion |
MONGODB_URI | Configure intranet-accessible sharded cluster address | Store batch-associated production traceability data, ensuring historical data bound to tender documents is queryable |
REDIS_URL | Configure intranet Redis instance with authentication | Cache frequently called professional terminology thesauri and real-time detection data, reducing database query pressure |
STORAGE_S3_ENDPOINT | Configure intranet-compatible object storage endpoint | Store large-volume raw milk test reports, filling records and other documents, ensuring tender attachments can be accessed normally |
| Proxy Configuration | Set to http://username:password@proxy-ip:port | Adapt to external model and knowledge base access requirements in intranet environments |
| Professional Terminology Thesaurus | Upload dairy processing exclusive terminology file | Match industry-specific keywords, avoiding recognition errors from general thesauri |
Three Common Mistakes
- Phenomenon: After upgrade, the configured dedicated model is automatically replaced with another model. A model mismatch occurs when saving and chatting. Cause: The application's model binding configuration was not synchronized correctly during upgrade, causing the frontend service to load the default model.
- Phenomenon: Unable to access external resources after configuring HTTP proxy during intranet deployment, returning connection timeout or authentication failure errors. Cause: The proxy configuration does not include username and password parameters, or the proxy port is not granted intranet access permission.
- Phenomenon: Professional terms in generated tender documents have incorrect matches, such as "UHT sterilization" being recognized as "high-temperature sterilization". Cause: A dedicated professional terminology thesaurus for dairy processing was not configured, causing the general thesaurus to fail to accurately match industry-specific keywords.
How to Verify Proper Configuration
- Log in to the FastGPT backend, check the database connection status, and confirm that the database maximum connection count configuration has no exhaustion alerts.
- Initiate a knowledge base recall test for raw milk acceptance records, verifying that the professional terminology thesaurus correctly matches keywords such as "raw milk antibiotic residues" and "CIP cleaning verification".
- Test the proxy configuration in the intranet environment, attempt to call external model interfaces, and confirm there are no connection timeout or authentication failure errors.
- After the upgrade is complete, check the application's model binding configuration, and confirm that the saved dedicated model has not been automatically replaced.
The values given are common starting points and should be measured against the reader's own samples.