What Data Looks Like for This Category
The core data for this category comes from formula systems, raw material batch records, internal mixers, Mooney viscometers, rheometers, vulcanizing machines, and laboratory testing equipment. Daily documents include formula sheets, weighing records, mixing curves, rubber compound quick test reports, vulcanization process cards, mold usage records, and finished product performance reports. Data updates occur per truckload of rubber, per batch of materials, or per mold production cycle. Documents include numeric fields and identification fields. Examples include mixing curve temperature (unit: ℃), rotor speed (unit: r/min), vulcanization time on vulcanization process cards (unit: seconds), vulcanization temperature (unit: ℃), and associated identifiers such as batch numbers and mold numbers.
Constraints on Deployment and Upgrade From These Characteristics
The high-frequency data updates, multi-type documents, and multi-data source traits of this category create three core constraints for deployment and upgrades. First, the update cadence of per truckload of rubber, per batch of materials, or per mold production cycle requires enabling incremental synchronization mode during deployment. Upgrades must be compatible with legacy incremental synchronization logic to avoid production data interruptions caused by full knowledge base reconstruction. Second, the storage and parsing requirements for multi-format documents including mixing curves and process cards require deploying industry-specific document parsing plugins during deployment. Upgrades must verify plugin compatibility to ensure retrieval accuracy. Third, the data source docking requirements for multiple equipment systems require configuring sufficient concurrent connection parameters during deployment. Upgrades must synchronously update connection configurations for all data sources to prevent data synchronization failures.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
REDIS_URL | redis://:custom password@deployed server IP:6379/0 | Adapts to cache current limiting for high-frequency data updates, ensures synchronization stability for multi-equipment data sources |
MONGODB_URI | mongodb://username:custom password@deployed server IP:27017/fastgpt?authSource=admin | Stores large volumes of unstructured documents including mixing curves and vulcanization process cards, adapts to high-frequency write requirements |
STORAGE_S3_ENDPOINT | Access address of the local object storage service | Stores attachments such as batch-uploaded formula sheets and finished product performance reports, adapts to multi-format document storage |
| Knowledge base incremental synchronization frequency | Every 15 minutes | Matches the update rhythm of per truckload of rubber or per batch of materials, avoids data lag |
DB_MAX_LINK | 100 | Adapts to concurrent connection requirements for multiple internal mixers and vulcanizing machines, prevents connection exhaustion |
FE_DOMAIN | Frontend access domain for local deployment | Configures cross-domain whitelists for API calls, ensures normal interaction between the frontend interface and backend services |
Three Common Mistakes
- Phenomenon: When calling the Q&A API bound to the vulcanization process knowledge base, the returned results only contain model-generated text, with no details such as the corresponding referenced document name or paragraph location. Cause: The configuration item for returning reference data via the API was not enabled during deployment, or the permissions for the knowledge base and API interface were not correctly associated.
- Phenomenon: After upgrading to FastGPT v4.17.0, chat records disappear after refreshing the frontend chat window, but complete chat records can be queried in the background database. Cause: The session cache configuration corresponding to
REDIS_URLwas not synchronously updated during the upgrade, causing the frontend session cache to not synchronize with updates from the backend database. - Phenomenon: Local deployed large models cannot be accessed via AIproxy. Cause: The access key and address for the local large model were not correctly configured, or cross-domain access permissions corresponding to
FE_DOMAINwere not enabled.
How to Confirm Proper Configuration
- Run a local API call test, pass in a question containing vulcanization process keywords, and check if the returned results include detailed information about referenced documents.
- Upload a latest mixing curve document, trigger incremental synchronization, wait 15 minutes, then search for keywords from the document, confirm that results can be retrieved normally.
- Restart the FastGPT service, access the frontend address corresponding to
FE_DOMAIN, start a chat session, refresh the page, and confirm that chat records are displayed normally. - Check the connection logs for
MONGODB_URI, confirm that there are no frequent connection timeout or disconnection errors.
The values provided here are common starting points, and should be evaluated against the reader's own samples.