What Data for This Category Looks Like
The data for this category primarily originates from water source and raw material testing, formula systems, online sugar and acidity monitoring, filling equipment, visual inspection, and LIMS systems. Daily documentation includes formula sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspection forms, and finished product inspection forms. Data updates occur per batch, per shift, or through continuous collection. Fields cover raw material test values, formula parameters, equipment operating parameters, test results, product batch numbers, and more. Some parameters have defined units, such as sugar content percentage and filling liquid level in millimeters.
What Constraints These Characteristics Impose on Deployment and Upgrade
High-frequency continuous collection of equipment data and batch-level documents for this category requires deployment to support high-concurrency data writes and low-latency queries. Multiple heterogeneous data sources require configuring storage and access links compatible with multiple data types to avoid data synchronization interruptions. Frequent product switching requires upgrades to support quick switching of knowledge base datasets for different batches, preventing cross-batch data confusion. Records from critical inspection links require strict traceability, so deployment must include tamper-proof data storage configuration. It is also necessary to adapt to this category’s unique field formats and unit rules to avoid data parsing errors.
How to Configure Settings
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 200–300 | Adapts to high-frequency data connection requirements for per-batch continuous collection, prevents connection pool exhaustion |
MONGODB_URI | Points to the dedicated database instance connected to the LIMS system | Stores batch-level inspection records and formula data, isolates from business data |
REDIS_URL | Configured as the local cache service address, cache validity period 300 seconds | Caches frequently queried online monitoring data, reduces database access pressure |
STORAGE_S3_ENDPOINT | Points to the enterprise-compliant object storage service endpoint | Stores large-volume equipment operation logs and original inspection images, ensures data traceability |
FILE_TOKEN_KEY | Generates a random string in accordance with enterprise internal key specifications | Encrypts access tokens for stored inspection documents, prevents unauthorized access |
Three Common Misconfigurations
- Symptom: Unable to select a locally deployed model when creating a knowledge base, with a prompt stating the model is not configured or the address is invalid. Cause: Failed to configure the access domain name of the local model service via
FE_DOMAIN, or failed to correctly synchronize third-party API access configurations to FastGPT service configurations, resulting in FastGPT being unable to discover locally deployed model instances. - Symptom: Connection timeout error during bulk import of inspection records, with logs showing connection pool exhaustion. Cause: The
DB_MAX_LINKconfiguration value is too low, unable to support high-frequency bulk data write requests. - Symptom: Stored inspection images fail to load normally, returning a 403 permission error. Cause: The storage endpoint configured via
STORAGE_S3_ENDPOINTdoes not have document read permissions enabled, resulting in FastGPT being unable to access stored original inspection data.
How to Confirm Successful Configuration
- Run a test to bulk import a single batch of water treatment records, check database connection count monitoring metrics, confirm they do not exceed the threshold set by the
DB_MAX_LINKconfiguration, and verify that the connection pool configuration takes effect. - Access the configured
STORAGE_S3_ENDPOINTstorage endpoint, upload and read a test document, confirm that the storage link is operational, and that inspection-related files can be read and written properly. - Initiate a test call to the local model, check that the local service address configured via
FE_DOMAINis accessible normally, and confirm that the model access link is operational. - Generate an access token for inspection records, use the key generated via
FILE_TOKEN_KEYto verify the token’s validity, and confirm that the encryption configuration takes effect.
The values provided are common starting points and should be measured against the reader's own samples.