What the data for this category looks like
Hazardous chemicals storage and transportation declaration materials and file review data primarily comes from three sources: internal enterprise hazardous chemicals business license declaration systems, in-and-out inventory ledgers during storage and transportation, and filing documents from emergency management departments. Document lengths vary widely. Some approval forms are only a few hundred characters, while storage safety assessment reports can exceed ten thousand characters. Document fields include generic hazardous chemical names, CAS registry numbers, storage bin numbers, approval serial numbers, validity periods, and contact information for storage and transportation responsible personnel. Professional measurement units such as tons, cubic meters, and hours are used. Data syncs in real time with individual declaration batches. File materials generated during daily inspections are updated weekly.
What constraints these characteristics impose on deployment and upgrade
The data for hazardous chemicals storage and transportation declaration materials and file reviews has many specialized fields and a wide range of document lengths. This requires deployments to adapt to database resources in internal network environments. Vector storage for long documents requires larger memory quotas. Incremental sync frequency must align with declaration batch rhythms. There are many specialized terms, so vector models must be able to accurately recognize hazardous chemical-related vocabulary. Internal networks cannot access public services, so all dependent database and cache services must use internally deployed instances. When upgrading plugins, consider vector compatibility with historical documents to avoid abnormal retrieval results caused by plugin version changes.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PG_URL | postgresql://fastgpt:[custom password]@internal PG server address:5432/fastgpt_hazardous_chemical_review | Stores vector data for hazardous chemical files and declaration materials. Use the internal network database address for internal deployments to avoid cross-network security risks |
REDIS_URL | redis://:[custom password]@internal Redis server address:6379/0 | Caches intermediate states of review workflows and retrieval results. Use locally deployed Redis services for internal network environments |
MONGODB_URI | mongodb://fastgpt:[custom password]@internal Mongo server address:27017/fastgpt?authSource=admin | Stores metadata for declaration materials and approval process records. Meets internal network database access requirements |
DB_MAX_LINK | 20–30 connections | Handles long documents and multi-file associated reviews. Requires sufficient concurrent database connections to ensure query efficiency |
ROOT_KEY | Random string with minimum 16 characters | Strictly controls administrator permissions in internal network environments to prevent unauthorized access to backend configurations |
STORAGE_S3_ENDPOINT | internal MinIO server address:9000 | Stores large-volume declaration attachments and safety assessment reports. Internal deployments avoid external network traffic costs |
Three common mistakes
- Symptom: Pulling the Redis image fails when running docker-compose up -d, with the error "Failed to resolve image repository address". Cause: The server is in an internal network environment and cannot access public Docker image repositories, and no internal image source has been configured.
- Symptom: When configuring the DeepSeek API channel, fill in the external network interface address. A connection timeout occurs after the application sends a query. Cause: The internal network cannot access external API interfaces. Use the internally deployed DeepSeek service address instead.
- Symptom: After upgrading the PgVector plugin, the number of retrieved hazardous chemical file results decreases significantly. Cause: Vector embeddings for historical documents were not regenerated. The vector encoding rules changed after the plugin upgrade, and existing vectors no longer match retrieval requirements.
How to verify the configuration is complete
- Run the docker ps command to confirm that the fastgpt-web, fastgpt-api, redis, and postgres containers are all in the Up state.
- Log in to the FastGPT backend, create a test knowledge base, upload a single hazardous chemical declaration material, and perform vector retrieval. Confirm that matching document fragments can be returned.
- Configure the internal network DeepSeek API channel, select this channel in application settings, and send a simple query. Confirm that a response can be returned normally.
- Check database monitoring logs to confirm that the number of database connections does not exceed the
DB_MAX_LINKconfiguration value, and no connection timeout errors occur.
Reference only; verify against the target FastGPT release and deployment documentation. The values provided here are common starting points and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.