What this category of data looks like
Data sources for this category include procurement orders, product drawings and specification requirements, supplier quality assurance documents, data collected by online inspection equipment, historical incoming material quality records, and audit materials submitted via supplier portals. Daily associated documents cover incoming material inspection specifications, sampling plans, batch incoming material reports, annual supplier audit reports, quality abnormality notification forms, 8D rectification reports, and concession acceptance approval forms. Data updates are recorded upon completion of each batch of incoming materials. Supplier quality evaluations are conducted monthly or quarterly. On-site audits and rectification verifications are carried out irregularly. Data fields include incoming material batch number, supplier code, inspection item parameters, non-conformance type, rectification closure status, and others. There are no unified fixed units; adjustments are made dynamically based on inspection items and material types.
What constraints do these characteristics impose during deployment and upgrade
Data of this category is multi-source, scattered, and has fluctuating update frequencies. During the deployment phase, access configuration for multi-format documents and heterogeneous data sources must be adapted, and flexible adjustment space for parameters such as MONGODB_URI and REDIS_URL must be reserved. Daily document types are diverse and fields are dynamic. A storage path that supports multi-format parsing must be configured, such as settings related to STORAGE_S3_ENDPOINT, to avoid parsing failures caused by changes in document structure. Data updates have no fixed cycle. During upgrades, the stability of incremental data synchronization mechanisms must be ensured to prevent data backlog during batch incoming material processing. In addition, this category must balance production supply guarantee and risk control. During deployment, a knowledge base partition with isolated permissions must be separately configured to prevent leakage of sensitive supplier information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://username:password@host:port/dbname?authSource=admin | Adapts to persistent storage of multi-source incoming material quality data, supports quick querying of historical inspection records and supplier archives |
REDIS_URL | redis://:password@host:6379/0 | Caches frequently accessed sampling plans and inspection specification documents, reduces database query pressure, and adapts to rapid verification requirements for batch incoming materials |
STORAGE_S3_ENDPOINT | Fill in according to the access address of the enterprise's existing object storage service | Stores large-volume documents such as batch-uploaded incoming material reports and 8D rectification reports, adapts to unified storage requirements for multi-format documents |
ROOT_KEY | Randomly generated 32-bit string | Configures the system's initial administrator permissions, prevents unauthorized access to sensitive supplier quality data |
DB_MAX_LINK | 100–200 | Adapts to incoming material inspection queries and supplier audit operations initiated by multiple departments simultaneously, avoids database connection exhaustion |
FE_DOMAIN | FastGPT access domain name deployed on the enterprise intranet | Limits front-end access sources, ensures access security of the supplier quality knowledge base |
Three Common Mistakes
- The symptom is a 401 Unauthorized error returned when calling the RAG Q&A interface. The cause is that
ROOT_KEYis not configured correctly, or a valid authentication token is not carried in the request header, causing the system to reject unauthorized access requests. - The symptom is that new user accounts cannot be created after local Docker Compose deployment. The cause is that the default deployment configuration does not enable public registration, and no docking parameters for external user management are configured, only the initial administrator account permissions are retained.
- The symptom is that the Q&A interface cannot be called normally after configuring the large model key. The cause is that the access permissions of the corresponding knowledge base are not correctly bound in FastGPT's model configuration, or the key storage path does not match the configuration requirements of
FILE_TOKEN_KEY.
How to Confirm Configuration is Complete
- Log in to the FastGPT backend, check the connection status of
MONGODB_URIaccording to the official deployment documentation for FastGPT v4.17.0, confirm that the database connection is normal, and verify the reading capability of historical quality data by executing basic query commands. - Upload an incoming material inspection specification document, verify that the storage service configured with
STORAGE_S3_ENDPOINTcan normally receive and store files, and check whether the document parsing result contains correct inspection item fields. - Initiate a simulated incoming material inspection Q&A request, confirm that the interface returns a response status code of 200, and that the returned content matches the preset inspection specifications and sampling plans.
- Check the system permission configuration, confirm that non-administrator accounts cannot access the sensitive supplier quality knowledge base partition, and verify the permission isolation effect of the
ROOT_KEYconfiguration.
The values provided are common starting points and should be evaluated against the reader's own samples.