What the data for this category looks like
Data sources for internal office and employee services in the coal chemical industry cover enterprise production management systems, OA platforms, employee attendance systems, equipment operation and maintenance logs, internal training documents, and more. Data update cycles vary significantly: real-time production data updates every 10–30 minutes, attendance and salary data updates daily, and training documents and internal notification data update irregularly. Document structures include structured reports (such as equipment operating parameter tables, energy consumption statistics sheets) and unstructured text (such as operation and maintenance manuals, job specifications). Fields include equipment numbers, operating temperature, pressure values, employee IDs, job levels, training duration, and more. Units include ℃, MPa, hours, person-times, and others.
What constraints these characteristics impose on deployment and upgrade
Since the data includes real-time production data streams and sensitive internal documents, compliance of data storage and access must be prioritized during deployment. Deployments must also adapt to access requirements for multi-source heterogeneous data. The high-frequency updates of real-time data require sufficient database connection pools and cache resources to avoid query blocking. There are many professional terms in unstructured documents. When upgrading the knowledge base, support for incremental updates and custom word segmentation configurations is required to prevent incorrect splitting of professional terms. The privacy of employee data requires restricting external access during deployment, only opening access to the internal office network. During the upgrade process, retain the original permission configurations to avoid data leakage risks.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 30–50 connections | Adapts to the concurrent query requirements of real-time production data streams for the coal chemical industry, avoiding database connection exhaustion |
REDIS_URL | Local Redis instance, memory configuration no less than 8 GB | Caches employee sessions and real-time production data snapshots, reducing repeated database query overhead |
STORAGE_S3_ENDPOINT | Enterprise internal compliant object storage endpoint | Stores sensitive production documents and employee data, meeting internal data storage compliance requirements |
FE_DOMAIN | Internal office exclusive domain name, such as internal.chemical-enterprise.com | Restricts access scope to only the enterprise internal network, ensuring data security |
FILE_TOKEN_KEY | Randomly generated 32-bit encrypted string | Encrypts file upload and access tokens, preventing unauthorized access to internal documents |
ROOT_KEY | Generated during initial deployment, retain the original key during upgrades | Avoids configuration chaos and permission loss caused by resetting system administrator permissions |
Three common mistakes
- Symptom: JS code execution node in workflow throws error "Cannot find module 'xxx'". Cause: The local deployment environment does not have the corresponding JS dependency package installed, or the dependency directory is not mapped to the container interior via docker-compose volume mounting.
- Symptom: Model call returns error "Model channel not configured". Cause: The model channel parameters associated with
MONGODB_URIare not correctly configured, or the internally deployed large model service endpoint is not bound. - Symptom: Knowledge base uploaded via file does not automatically sync updates. Cause: The volume mapping pointing to the local document directory is not set in docker-compose, or the
FILE_TOKEN_KEYconfiguration is incorrect, causing token verification failure.
How to confirm configurations are correct
- Access the internal domain name configured in
FE_DOMAIN, verify that the FastGPT front-end page loads normally, with no permission interception prompts. - Run the
redis-cli pingcommand, verify that the Redis connection is normal, returning a "PONG" response. - Upload a coal chemical industry operation and maintenance manual document, trigger the knowledge base indexing, verify that the corresponding file is generated in the internal object storage, and the corresponding embedding vector exists in the vector database.
- Run the test JS code node in the workflow, verify that there are no dependency errors and the expected execution result is returned.
The values provided are common starting points and should be measured 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.