FastGPT v4.17.0
What the data for this category looks like
Business data sources for the pulp and paper category include wood pulp and waste paper batches, DCS/QCS systems, paper machine online measurement equipment, laboratories, energy and water treatment systems. Data update frequencies fall into three categories: real-time (paper machine operating parameters, online quality curves), per-shift (beating records, stock preparation orders), per-roll (sheet break and product change records). Daily documents include structured stock preparation orders, beating records, plus unstructured online quality curves and laboratory reports. Fields cover stock ratio, beating degree, sheet grammage, moisture content, sheet break duration, and more. Units include g/㎡, %, minutes, and other standard units.
Constraints on Deployment and Upgrade Posed by These Characteristics
Multi-source heterogeneous data structures require flexible field mapping rules during deployment. These rules adapt to structured operation records and unstructured curve reports. Data sources with different update frequencies need differentiated synchronization tasks. Real-time data requires low-latency cache links to avoid impacting equipment inspection response speeds. Roll-generated sheet break records must be linked to corresponding equipment IDs. Deployment must ensure consistent unique identifiers across data sources. During upgrades, compatibility with legacy data formats is required. This prevents historical inspection records from becoming unsearchable due to format changes. Upgrade window duration must be controlled to avoid interrupting high-speed continuous production workflows.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 10 | Limits the number of device-related documents retrieved in a single call, avoiding context overload that impacts model generation quality |
MONGODB_URI | mongodb://[username:password@]host1:port1,host2:port2/[authSource=admin] | Stores multi-source historical inspection records, sheet break and product change records, and supports horizontal expansion of storage capacity |
REDIS_URL | redis://:your_password@host:port/0 | Caches real-time device status and vector retrieval results, keeping retrieval latency under 2 seconds |
STORAGE_S3_ENDPOINT | Enterprise internal object storage dedicated domain name | Stores unstructured documents such as online quality curves and laboratory reports, adapting to read/write requirements for large files |
FILE_TOKEN_KEY | 32-bit random encrypted string | Secures document download links, preventing unauthorized access to production-sensitive data |
Three Common Misconfigurations
- Phenomenon: Project compilation fails on Windows environments, with terminal prompts indicating missing required compilation dependencies. Cause: The required version of Visual Studio is not installed, and correct system compilation environment variables are not configured.
- Phenomenon: After deploying a large language model locally, inconsistent results are returned for the same equipment inspection query. Cause: The
REDIS_URLcache for conversation context is not configured, leading to full data retrieval on each request and fluctuations in model generation logic. - Phenomenon: A
WARNING: current commit information was not captured by theprompt appears during local image packaging. Cause: The Git repository is not initialized in the project root directory, causing the packaging script to fail to retrieve version information and impacting integrity checks for image construction.
How to Confirm Successful Configuration
- Access the FastGPT v4.17.0 admin backend, navigate to the data source management page, and check the synchronization status of configured data sources such as DCS and laboratory systems. Confirm that data update frequencies match preset rules.
- Submit a test query such as "What are the sheet break records for a specific paper machine model". Verify that returned results include category-specific documents such as stock preparation orders and sheet break and product change records.
- Review system operation logs. Confirm that connection statuses for configuration items including
MONGODB_URIandREDIS_URLare normal, with no timeout or authentication failure errors. - Simulate the configuration upgrade process, perform a rolling upgrade operation, and verify that the rollback function works normally. Ensure existing equipment data retrieval services are not impacted.
The values provided here are common starting points, and should be assessed against relevant specific samples.