This page applies to the deployment and upgrade workflows for FastGPT v4.17.0.
What the Data for This Category Looks Like
Data for this category originates from formula management systems, raw material batch ledgers, internal mixer operation logs, Mooney viscosity and rheometer test records, vulcanizer process parameters, and laboratory finished product performance reports. Daily documentation includes formula sheets, weighing and feeding logs, mixing temperature curves, rubber compound quick test reports, vulcanization process cards, mold usage records, and finished product performance test reports. New data is generated for each raw material batch, each rubber compound mixing run, and each vulcanization cycle. Each data entry includes fields such as material ratio, process parameters, and test results. Formats adjust dynamically based on production scenarios.
Constraints Imposed on Deployment and Upgrade
High-frequency, small-batch data is generated dynamically per production run. Deployment must support real-time writing and linked retrieval of this data. Multi-source heterogeneous production documents use inconsistent formats. Upgrade workflows must adapt to document structure differences across equipment. Bid quotations must tie to latest raw material costs, process parameters, and test results. Deployment must set up real-time synchronization links between production systems and the knowledge base. Data entry fields change dynamically per production scenario. Upgrades must optimize knowledge base field mapping rules to prevent missing fields or incorrect matches during retrieval. Data scale grows with production volume. Deployment must reserve scalable storage and computing resources.
Configuration Recommendations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–150 | Matches the demand for high-frequency, small-batch data writing, prevents connection pool exhaustion |
REDIS_URL | Local Redis instance, port 6379 | Ensures low-latency data caching, meets requirements for real-time synchronization of production data |
STORAGE_S3_ENDPOINT | Configure per enterprise internal storage service address | Adapts to storage needs of multi-source heterogeneous production documents for this category, compatible with existing enterprise storage architectures |
FE_DOMAIN | Enterprise internal access domain name or publicly filed domain name | Supports internal collaborative review of bid documents, ensures access compliance |
ROOT_KEY | Generate a 32-bit random string per enterprise security policy | Ensures security of background management permissions, prevents unauthorized access |
FILE_TOKEN_KEY | Align with enterprise document system secret key | Enables seamless import of production documents, avoids token verification failures |
Three Common Configuration Mistakes
- Phenomenon: The frontend page fails to load, returning a connection timeout or 404 error. Cause:
FE_DOMAINandFILE_TOKEN_KEYare not configured correctly, leading to failed validation of frontend resource loading. - Phenomenon: A "connection refused" error appears when connecting an external model service. Cause:
REDIS_URLorSTORAGE_S3_ENDPOINTis not configured correctly, resulting in a failed data link. The cache or storage services required for model calls do not start properly. - Phenomenon: Knowledge base search results omit the latest batch production data. Cause: The
DB_MAX_LINKconfiguration value is too low, exhausting the database connection pool and preventing real-time retrieval of latest production data.
How to Verify Successful Configuration
- Access the configured
FE_DOMAIN, confirm the frontend page loads normally with no resource loading failure prompts. - Submit keywords from a batch mixing curve to the knowledge base search interface, confirm that corresponding production document search results are returned.
- Check the database connection pool status, confirm the current connection count does not exceed the
DB_MAX_LINKconfiguration value. - Upload a vulcanization process card document from this category, confirm it is successfully parsed and stored in the knowledge base.
The values listed are common starting points and should be evaluated against the applicable production samples.