What data for this category looks like
Data for this category originates from the formula and process library, raw material batch records, reactor DCS systems, weighing systems, LIMS, and warehouse management systems. Documents such as batch production instructions, feeding verification slips, reaction process logs, cleaning records, inspection reports, deviation and change orders are generated daily. Data is updated for each batch and critical step. Documents contain fields such as batch number, raw material batch information, reaction parameters, operation logs, and deviation explanations. Field units cover category-specific identifiers like reaction temperature, duration, and feeding weight. Data updates progress with production batches, and multiple related documents are generated within a single production cycle.
What constraints these characteristics impose during deployment and upgrades
The multi-source, heterogeneous data sources of this category require adapting to multiple system integrations during deployment to avoid data synchronization interruptions. The batch-updated document structure and sensitive formula requirements demand configuring fine-grained access permissions and data isolation rules. The production mode of switching between multiple product varieties requires compatible document parsing rules for different categories during upgrades to prevent configuration conflicts related to product change cleaning. The high-frequency data writes within a single production cycle require reserving sufficient database connection resources during the deployment phase to ensure system stability.
How to set configurations
These configurations are based on FastGPT v4.17.0 and adapted to the production scenario of this category:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | Point to a dedicated MongoDB instance storing process documents and batch data | This category's data is stored in batch-isolated partitions; a dedicated database ensures data security and query efficiency |
REDIS_URL | Configure as a local cache instance with 800–1200 MB of allocated memory | Used to cache real-time reaction parameters and feeding records, adapting to high-frequency read production scenarios |
STORAGE_S3_ENDPOINT | Bind to the enterprise's internal compliant storage node | Used to store sensitive formula documents and exception reports, complying with data confidentiality requirements |
DB_MAX_LINK | Set to 200–300 | Adapts to concurrent connection requirements for parallel document generation across multiple batches, preventing connection exhaustion |
FE_DOMAIN | Configure as the enterprise's internal access domain name | Restrict external access to prevent unauthorized acquisition of process data |
FILE_TOKEN_KEY | Generate a dedicated key in accordance with enterprise key management specifications | Used to encrypt document access tokens, preventing unauthorized access to sensitive documents |
Three common mistakes
- Pulling the
arm64architecture image fails. Cause: The corresponding architecture's image tag is not specified; the default image only supports x86 architecture. - Dependency version mismatch occurs during pure source code deployment. Cause: FastGPT v4.17.0's dependency versions are not locked, leading to conflicts with the system environment.
- Document parsing exceptions occur after upgrading from an older version to 4.12.3. Cause: Custom document parsing templates were not migrated during the upgrade process; the new version's parsing logic is incompatible with the old configuration.
How to confirm the configuration is complete
- Run the
docker pscommand to confirm that the architecture identifier of the corresponding container matches the deployment server. - Import a test batch's feeding verification slip to check that the field completeness of the parsed document conforms to preset rules.
- Initiate an exception report generation request to verify that the response time falls within 10–30 seconds.
- Access the address bound to
FE_DOMAINto confirm that only authorized accounts can access the system.
The values provided here are common starting points and should be evaluated against the reader's own samples.