What this category's data looks like
Daily documents for this category include bath solution analysis records, process parameter cards, fixture records, film thickness and adhesion reports, hazardous waste ledgers, and wastewater monitoring records. Data sources cover customer surface treatment specifications, bath solution test results, rectifier operation data, online water quality monitoring records, and hazardous waste management ledgers. Update frequency follows bath cycles, shifts, or monitoring requirements. Individual documents include fields such as bath number, plating type, current density, temperature, film thickness, with standardized units attached, such as g/L, A/dm², μm.
What constraints these characteristics impose on the deployment and upgrade phase
Business data from multiple sources with high update frequency requires support for multi-format document parsing and incremental synchronization during deployment, to avoid resource waste from full repeated imports. Document fields include specific units, so metadata mapping rules must be configured to ensure accurate parameter extraction and avoid confusion between process parameters of different plating types. Hazardous waste and wastewater data involve compliance-sensitive content, so additional encrypted storage and permission verification logic must be configured. High-frequency updated bath solution and online monitoring data require the vector database refresh interval to align with business rhythms, to avoid data lag affecting the accuracy of Q&A responses.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | Point to an independently deployed replica set instance | Adapt to high-frequency data write and query requirements across multiple workshops and bath cycles, avoiding performance bottlenecks on single instances |
REDIS_URL | Configure as a cache instance with RDB persistence | Used to cache frequently queried process parameters and bath solution data, reducing database load |
DB_MAX_LINK | 100–150 | Adapt to concurrent request volumes from multiple data sources, avoiding database connection exhaustion |
STORAGE_S3_ENDPOINT | Point to the enterprise's internal object storage address | Adapt to storage requirements for batch-imported bath solution analysis records and online monitoring data, reducing external network transmission latency |
FILE_TOKEN_KEY | Generate in accordance with enterprise internal key specifications | Used to encrypt sensitive documents such as uploaded hazardous waste ledgers and film thickness reports, ensuring data security |
ROOT_KEY | Only used for initial deployment, then rotated to role-based permission keys | Follow the principle of least privilege, avoiding security risks from long-term use of superuser permissions |
Three common mistakes
- Phenomenon: Incorrect answers to consecutive questions, with no context association. Cause: Context association configuration is not enabled, or the context window parameter is set too small to carry context information from multi-turn conversations.
- Phenomenon: Error reported when deploying FastGPT v4.17.0 via docker-compose, containers fail to start normally. Cause:
MONGODB_URIorREDIS_URLparameters are not configured correctly, leading to failed connection to dependent services, and connection timeout errors are thrown during container startup. - Phenomenon: AI advanced configuration options cannot be displayed, only simple parameters can be adjusted. Cause: The deployed version does not have the advanced configuration switch enabled, or the permission verification logic corresponding to
ROOT_KEYis not configured, causing the frontend to fail to load the advanced configuration page.
How to confirm the configuration is complete
- Run the
docker pscommand to confirm that all dependent services (MongoDB, Redis) and FastGPT containers are in running state. - Upload a bath solution analysis record document, verify that fields such as bath number and nickel ion concentration can be extracted after parsing, with no missing or incorrect content.
- Submit two consecutive questions, verify that the second question can associate the context from the first question and return targeted answers.
- Access the frontend address corresponding to
FE_DOMAIN, confirm that the AI advanced configuration page can be entered and custom prompt templates can be modified.
The values provided are common starting points and should be measured against the reader's own samples.