This configuration applies to FastGPT v4.17.0.
What data for this category looks like
Data sources for this category include formula management systems, raw material batch tracking tools, internal mixers, Mooney viscometers, rheometers, vulcanizers, and laboratory testing equipment. Daily documents include formula sheets, weighing logs, mixing curves, rubber compound quick test reports, vulcanization process cards, mold usage records, and finished product performance reports. Data updates occur for each raw material batch, each production material batch, and each molded product run. Document fields include rubber type identifiers, feeding parameters, equipment operating parameters, process temperature and duration values, performance test metrics, and some time-stamped curve or chart files.
What constraints do these characteristics impose during deployment and upgrade
Multiple heterogeneous data sources require adapting to industrial device protocols and business system interfaces during deployment to ensure normal data access. High-frequency data updates require the system to have sufficient concurrent processing capacity. Real-time data synchronization cannot be interrupted during upgrades, as this will lead to production data loss. Diverse document structures require configuring parsing rules tailored to different formats, and upgrades must be compatible with newly added device export document formats. Industrial parameters with clear units require the knowledge base to unify field mapping rules to avoid parameter mismatches during retrieval.
How to Configure
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 | This category has frequent data updates, so a sufficient database connection pool is needed to handle concurrent writes and queries |
MONGODB_URI | Enterprise intranet private MongoDB cluster connection string | This category includes production batch and performance parameter data, which must be deployed on the intranet to ensure data security |
REDIS_URL | Local Redis instance with persistent configuration | High-frequency retrieval of vulcanization process cards and mixing parameters needs to be cached to reduce database load |
STORAGE_S3_ENDPOINT | Enterprise self-built object storage endpoint | File data such as mixing curves and finished product reports has large volume, so intranet storage is needed to reduce bandwidth costs |
ROOT_KEY | 32-bit random string | Ensures access permission security for API interfaces, preventing unauthorized calls to knowledge base data |
Three Common Mistakes
- Phenomenon: An upgrade prompt error appears after startup, and API calls return a 500 status code. Cause:
MONGODB_URIis not correctly configured to point to the enterprise intranet MongoDB instance, resulting in failed database connection. - Phenomenon: System memory usage continues to rise during operation, leading to lag. Cause: The value of
DB_MAX_LINKis not restricted, causing the database connection pool to be exhausted and a large amount of memory resources to be consumed. - Phenomenon: Mixing data in the existing knowledge base is lost after migrating deployment from a containerized environment. Cause: Business data and vector database information were not backed up using associated database tools before migration, resulting in incomplete data migration during the process.
How to Verify Successful Configuration
- Execute the FastGPT built-in database health check interface to confirm that services corresponding to
MONGODB_URI,PG_URL, andREDIS_URLall return normal responses. - Upload a mixing curve document and a vulcanization process card to verify that the knowledge base can parse normally and generate the corresponding question and answer vector database.
- Simulate high-frequency production data writes to confirm that the system can maintain stable connections and responses, with no connection timeout errors.
- Verify the configuration of
FILE_TOKEN_KEYto confirm that uploaded files such as finished product performance reports can be correctly associated with corresponding knowledge base entries.
The values provided are common starting points and should be measured against the reader's own samples.