What this category's data looks like
This category's data comes from customer color samples, formula libraries, dye chemical batch records, dye vat control systems, colorimeters, setting machines, and environmental monitoring equipment. Daily documents include dyeing formulas, chemical preparation ledgers, dye vat operation curves, proofing confirmation sheets, color difference reports, setting process sheets, and sewage discharge records. Data updates follow the rhythm of each vat, each batch, or each shift. Fields include dye vat operating temperature, chemical preparation concentration, batch numbers, color difference detection values, etc., and are synced in real time along with the production process.
What constraints these characteristics impose on deployment and upgrade
The batch-based high-frequency data and specialized process documents of this category require deployment to support fast vector ingestion of small-batch high-frequency documents, and adaptation to semantic encoding of specialized terms such as dyeing and color difference detection. Real-time data requirements are high, so deployment must reserve docking interfaces for equipment data sources. Upgrades must be compatible with new data formats from colorimeters and setting machines. Sewage ledgers and environmental monitoring data must be stored separately. Storage paths that meet business compliance requirements must be configured to avoid mixing with production data.
Configuration Specifications
This document applies to the deployment and upgrade process for FastGPT v4.17.0. The specific configuration items are as follows:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 100–150 个 | Adapts to the high-frequency data connection requirements of each vat or batch, and prevents database connection exhaustion |
MONGODB_URI | 指向独立业务数据库的专属连接串 | Separates production data and question-and-answer system data, and ensures storage security for specialized process documents |
REDIS_URL | 配置为本地内网地址，缓存时长设为 300 seconds | Caches high-frequency query inspection knowledge and real-time dye vat parameters, and reduces database pressure |
STORAGE_S3_ENDPOINT | 使用内网存储节点地址 | Meets the storage requirements of compliant data such as sewage ledgers, and ensures data transmission security |
FILE_TOKEN_KEY | 使用随机生成的 32 位密钥 | Ensures access security for uploaded documents such as dyeing formulas and color difference reports |
ROOT_KEY | 按团队权限配置分层密钥 | Adapts to knowledge access permission control for different departments such as production and quality inspection |
Three Common Misconfigurations
- Issue: After deploying the specified version, the
typeparameter of the MCP service is fixed to the string type and cannot be modified. Cause: The parameter type mapping rules for the MCP service were not correctly declared in the configuration file, causing the system to force the string format by default. - Issue: After uploading documents such as dyeing formulas and color difference reports, parsing times out and the request fails. Cause: The
DB_MAX_LINKparameter was not adjusted to fit the connection requirements of high-frequency document parsing, or the storage node bandwidth was insufficient, causing file transfer lag. - Issue: After updating the version, the inference length of the equipment inspection Q&A becomes shorter, and the complete process correction plan cannot be output. Cause: The valid duration of the context cache was not reconfigured, or the system default context loading parameter was reset to a shorter value, causing complete information from specialized process documents to not be loaded.
How to Verify Successful Configuration
- Log in to the FastGPT backend, and check whether the configuration addresses of
MONGODB_URIandSTORAGE_S3_ENDPOINTmatch the internal network storage nodes. - Upload a standard dyeing formula document, and verify that the parsing and vector ingestion processes complete normally.
- Call the MCP service to test the inspection parameter configuration, and confirm that the parameter type can be adjusted according to business requirements.
- Restart the FastGPT service, and check whether the cache connection status of
REDIS_URLis normal.
The values provided here are common starting points, and should be evaluated against the reader's own samples.