What Data for This Category Looks Like
Data for this category comes from specialty chemical enterprises including coatings, resins, and agrochemical intermediates. Sources include formula and process libraries, raw material batch records, reactor DCS systems, weighing systems, LIMS, and warehouse systems. Daily documents include batch production instructions, feeding review sheets, reaction process records, cleaning records, inspection reports, and deviation change orders. Data is updated per batch production or key step. Fields include feeding volume, reaction parameters, cleaning confirmation records, and formula parameters. Units cover mass, volume, temperature, time, and other metrics. Due to multi-variety batch switching, data is linked to a unique batch identifier.
Constraints for Deployment and Upgrade
The multi-variety, batch-switching production characteristics of this category require deployment workflows to support batch data isolation, preventing formula leakage and cross-contamination. High-frequency batch data updates require stable, high-concurrency database connections to maintain read and write efficiency. The need to associate multiple production document types requires the file storage module to support different formats of production records and bid-related documents. Bid quotations must accurately match fields such as feeding volume and reaction parameters. Deployment must reserve resource margin for model calls to accommodate future upgrades to field extraction accuracy. Since data sources include industrial control and laboratory systems, the system must support compatible data import rules for different formats.
Configuration Settings
This configuration is tailored for deployment needs of batch fine chemical synthesis scenarios, and is compatible with FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PG_URL | postgresql://postgres:custom password@database address:5432/fastgpt?sslmode=disable | Adapts to structured storage needs of fine chemical batch data, ensuring stable multi-concurrency read and write operations |
REDIS_URL | redis://:custom cache password@cache service address:6379/0 | Used to cache batch data and bid document analysis results, reducing database pressure |
STORAGE_S3_ENDPOINT | http://internal enterprise storage service address:9000 | Adapts to batch storage of multiple types of production documents, supporting storage path partitioning by batch |
DB_MAX_LINK | 20–30 | Matches concurrency requirements of high-frequency batch data updates, preventing connection exhaustion |
FILE_TOKEN_KEY | Generate a 32-bit random string based on the business scenario | Ensures access permissions for bid-related documents, preventing unauthorized access |
ROOT_KEY | Use enterprise-grade strong key configuration | Ensures permission security for system background operations, adapting to enterprise-level deployment needs |
Three Common Mistakes
- When deploying Qwen3 series models via VLLM, target fields cannot be extracted from feeding review sheets parsed from PDF files, and the interface returns empty results. Cause: The Qwen3 series models have insufficient context understanding accuracy for industrial scenario professional fields such as feeding percentage and reactor number, or the deployment does not adapt to the format characteristics of industrial documents.
- After upgrading via docker-compose, the pg container fails to start, and the log displays "FATAL: role "postgres" does not exist". Cause: The original database role configuration was not retained during the upgrade, or the initialization script did not correctly execute the database user creation steps.
- When the
DB_MAX_LINKconfiguration value exceeds the database's carrying capacity, the database connection pool overflows, causing bid document generation tasks to time out. Cause: The concurrency volume of high-frequency batch data updates for this category was not matched, and blindly increasing the connection number leads to resource exhaustion.
How to Verify Successful Deployment
- Test batch import of one batch of production documents and formula data, check whether the database connection is normal, and there are no read and write timeout errors.
- Call the model interface, attempt to extract specified fields from a simulated feeding review sheet PDF, and verify that the model call link is unobstructed.
- Check the file storage module, confirm that the storage path partitioned by batch has taken effect, and documents can be read and downloaded normally.
- View the connection pool monitoring in the system background, confirm that the current connection number does not exceed the
DB_MAX_LINKconfiguration value.
The values provided are common starting points and should be measured against the reader's own samples.