What the data for this category looks like
Data related to equipment inspection and maintenance Q&A for welded structural component manufacturing comes from design drawings, material quality certificates, welding equipment parameters, filler metal inventory, non-destructive testing systems, and personnel qualification ledgers. The update frequency is per weld seam, per batch, or per shift. Document types include welding procedure qualification records, welding procedure specifications, filler metal issue records, welder qualification records, weld inspection reports, and repair orders. Data fields include weld number, filler metal batch number, welder certification number, test results, and more. There are no unified fixed units, and values adapt flexibly based on document type.
What constraints these characteristics impose during deployment and upgrade
Data sources for welded structural component manufacturing involve multiple industrial systems. During deployment, configure storage and database parameters for cross-system access to support multi-source data docking requirements. The high-frequency update characteristic increases database concurrent load, so adjust connection pool configuration to ensure system stability. Documents include rigid verification fields such as welder qualifications and non-destructive testing results. Knowledge base indexes must be bound to specific metadata fields, and configure verification keys for metadata storage. Data involves quality records related to special equipment. During deployment, ensure storage complies with industrial data compliance requirements, and configure object storage endpoints that fit the enterprise architecture.
How to set the configurations
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 connections | Adapts to the high-frequency data update scenario of welded structural component manufacturing, meeting database connection requirements for multi-system concurrent read and write operations |
REDIS_URL | redis://:your-redis-password@your-redis-host:6379/0 | Caches frequently accessed filler metal inventory and welder qualification data to reduce load on relational databases |
STORAGE_S3_ENDPOINT | Fill in according to the enterprise's internal object storage address | Stores industrial documents such as weld inspection reports and repair orders, adapting to the enterprise's existing storage architecture and compliance requirements |
FILE_TOKEN_KEY | 32-character random string | Generates verification tokens for uploaded welding procedure documents to prevent unauthorized document access and tampering |
FE_DOMAIN | https://your-welding-faq-domain.com | Configures the frontend access domain name, adapting to internal enterprise or external access scenarios for the equipment inspection Q&A system |
PG_URL | postgresql://your-pg-user:your-pg-password@your-pg-host:5432/welding_structure_db | Stores structured data such as equipment inspection logs and maintenance records, adapting to relational data management requirements in industrial scenarios |
Three common errors
- Phenomenon: After deploying via image packaging, uploaded welding procedure documents cannot be recognized by the system, with no error prompt. Cause:
STORAGE_S3_ENDPOINTwas not configured correctly, or file storage permissions were set incorrectly, causing the system to fail to read uploaded attachment data. - Phenomenon: FastGPT instances inside Docker containers cannot access enterprise internal welding equipment parameter systems or filler metal inventory interfaces. Cause: Container network configuration did not map the enterprise internal network segment, or access whitelists for databases and cache services were not configured correctly.
- Phenomenon: Code running plugin calls return AxiosError 404. Cause:
FE_DOMAINor internal interface access addresses were not configured correctly, causing plugin requests to fail to match the correct service endpoints.
How to confirm the configuration is complete
- Access the FastGPT v4.17.0 backend, upload a welding procedure specification document, check if it can be parsed normally and associated with corresponding metadata fields, confirming that storage and metadata configurations are effective.
- Execute network connectivity test commands inside the Docker container, verify access to enterprise internal welding equipment parameter systems and filler metal inventory interfaces, confirming network configurations are correct.
- Call the code running plugin, test access to preset business interfaces, confirming domain names and interface configurations are correct.
- Start an advanced orchestration workflow, input a simulated equipment inspection question, check if the initial user question is correctly passed to the AI dialogue stage, confirming context configurations are effective.
The values provided are common starting points and should be measured against the reader's own samples.