What this category’s data looks like
Core data for this category comes from customer surface treatment specifications, bath chemistry tests, rectifier data, online water quality monitoring, and hazardous waste management ledgers. Daily documents include bath analysis records, process parameter cards, fixture records, film thickness and adhesion reports, hazardous waste ledgers, and wastewater monitoring records. Fields include bath concentration (g/L), operating temperature (℃), output current (A), film thickness (μm), fixture number, discharge limit (mg/L), and others. Data updates follow per-batch, per-shift, or special monitoring requirements. Update frequencies vary significantly across different data types.
What constraints do these characteristics impose during deployment and upgrade
Data sources for this category are scattered, and update frequencies vary widely. Deployments must support connecting to multiple data source interfaces and adapt to different document parsing formats. High-frequency real-time data synchronization requires configuring dedicated internal network storage and cache resources to avoid public network transmission delays and security risks. Diverse document structures and specialized field requirements need pre-configured parsing rules to ensure the Q&A system can accurately extract core process parameters. Additionally, compliance storage requirements for hazardous waste and emission-related data restrict all data flows to the workshop internal network, and public network transmission is prohibited.
Configuration guidelines
Configuration guidelines apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 10–15 connections | Meets concurrent demand for high-frequency data synchronization across electroplating workshop equipment, prevents connection exhaustion |
MONGODB_URI | Point to the dedicated workshop internal network MongoDB instance address | Stores structured and unstructured data such as bath records and film thickness reports, avoids security risks from public network transmission |
REDIS_URL | Configure as the workshop internal network Redis cache service address | Caches real-time rectifier data and online water quality monitoring values, reduces query delay and resource consumption from repeated requests |
STORAGE_S3_ENDPOINT | Point to the workshop internal network object storage service address | Stores large files such as hazardous waste ledgers and high-resolution fixture photos, meets compliance storage and fast retrieval requirements |
ROOT_KEY | 32-bit random string generated by the internal network deployment administrator | Restricts background configuration page access to internal network IPs only, prevents unauthorized system modification operations |
FILE_TOKEN_KEY | Encryption key consistent with the internal network object storage service | Verifies the legitimacy of uploaded process parameter cards and bath analysis records, prevents malicious file injection |
Three common configuration errors
- Symptom: The
docker pullcommand returns the errorError response from daemon: Get https://registry-1.docker.io/v2/: net/. Cause: No internal network-compatible image source is configured, and public network access is blocked by the firewall. - Symptom: A permission error is prompted when testing a configured local DeepSeek model in FastGPT. Cause: The internal network permission whitelist for model access was not correctly added in the system configuration, or the oneAPI interface key was not synchronized to FastGPT environment variables.
- Symptom: The internally deployed aiproxy component cannot connect to the preset model service. Cause: The aiproxy internal network address was not added to FastGPT’s allowed access list, or correct internal network proxy routing rules were not configured.
How to confirm successful configuration
- Log in to the FastGPT background management page, verify that only internal network IPs can access the configuration interface, and public network IPs trigger permission interception.
- Upload a real bath analysis record document, confirm the system successfully parses and extracts core fields such as bath concentration and operating temperature, with no parsing failure prompts.
- Trigger a device inspection-related Q&A request, check background logs, confirm no public network resource call records, and all data comes from internal network storage and cache.
- View the database connection monitoring panel, confirm that the current concurrent connection count does not exceed the
DB_MAX_LINKconfiguration threshold, with no connection timeout or disconnection errors.
The values provided are common starting points and should be measured against the reader's own samples.