Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Data sources for advanced chemical materials include internal production ledgers, quality inspection reports, R&D experiment records, equipment operation and maintenance logs, and compliance approval documents. Update frequency varies with R&D batches, quality inspection cycles, and equipment inspection plans. There is no fixed uniform update cycle.
Documents are divided into two categories: structured and unstructured. Structured documents include parameters with dedicated units such as aggregate strength and tensile modulus. Fields include batch number, raw material ratio, detection time, and production equipment number. Unstructured documents are mostly long-text experiment records and compliance PDFs.
What constraints these characteristics impose on deployment and upgrade
The dedicated fields and units of structured data require precise field mapping rules during deployment, to prevent format errors during data import. Mixed-type data requires adaptation to both relational databases and vector storage. During upgrades, the old storage structure must be compatible to prevent loss of historical data.
Data sources with no fixed update frequency require flexible incremental synchronization configurations during deployment. Fixed-cycle full synchronization solutions cannot be used. Batch-uploaded long documents consume significant storage resources. Storage expansion requirements must be evaluated in advance during upgrades.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 10–15 connections | Advanced chemical materials data has high associated query requirements, prevents database connection exhaustion |
MONGODB_URI | Access address of the internal unstructured document cluster | Stores long-text data such as R&D experiment records and compliance PDFs |
REDIS_URL | Local Redis service 6379 port address | Caches frequently queried equipment operation and maintenance and quality inspection data to reduce database load |
FE_DOMAIN | Official domain name of the internal office system | Binds the access path for the employee service frontend, ensures normal access for internal employees |
ROOT_KEY | Randomly generated 32-character string | Initializes system administrator permissions, avoids security risks from using default keys |
STORAGE_S3_ENDPOINT | Service address of the internal object storage | Stores batch-uploaded R&D documents and compliance approval files |
Three common mistakes
- Symptom: System fails to log in with initial credentials after starting via docker-compose, with an error prompt indicating insufficient permissions. Cause: The
ROOT_KEYparameter was not configured correctly, and the default value was used, causing administrator permission verification to fail. - Symptom: Multiple independent employee accounts cannot be created, and the system prompts permission restrictions. Cause: Multi-account deployment configuration was not enabled, or the internal office system's permission synchronization interface was not bound.
- Symptom: Cloud version login cannot use account password login, and there is no initial password entry for QR code login. Cause: The account password login function was not enabled in the deployment configuration, causing the corresponding verification logic to not take effect.
How to confirm the configuration is complete
- Run the
docker-compose pscommand to confirm that all service containers are in a running state. - Access the domain name configured in
FE_DOMAIN, and use the administrator account corresponding toROOT_KEYto complete login verification. - Upload a standard advanced chemical materials quality inspection report, confirm that the data is successfully synchronized to the specified storage repository.
- Initiate an internal employee service query request, confirm that the returned results include correct equipment operation and maintenance and quality inspection data.
The values provided 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.