What this category of data looks like
Grain reserve declaration materials and file review data mainly come from four sources: daily warehouse ledgers, rotation plan documents, third-party quality inspection reports, and filing receipts from local grain reserve departments. Data updates trigger with reserve rotations, quarterly inspections, and annual verification nodes. No fixed update cycle exists, but most updates occur during annual rotation seasons. Each material combines structured ledgers and unstructured documents. Structured fields include grain reserve variety, storage quantity, warehouse ID, and quality inspection indicators. Supported units include tons, kilograms, warehouse IDs, and others. Unstructured content mostly takes the form of multi-page scans or official approval documents, including approval numbers, seal information, and scanned paper documents.
What constraints these characteristics impose during deployment and upgrade
The multi-source mixed structure of grain reserve declaration materials and files requires deployment to support two key functions: precise field mapping for structured ledgers, and OCR processing for unstructured scanned documents. Sufficient local storage resources must be reserved. Data updates concentrate in bulk import scenarios during reserve rotation seasons. Database connection configurations must adapt to high-concurrency requests to avoid connection pool exhaustion. The diversity of fields and units requires knowledge base chunking to retain original field identifiers. This prevents data parsing errors. Local deployment requirements for public sector and state-owned enterprise scenarios mean all configurations must support offline operation without public network dependencies. Configurations must also meet SSO integration permission control requirements.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 80–120 operations per second | Adapts to the bulk import rhythm of grain reserve declaration materials, avoiding database connection pool exhaustion |
REDIS_URL | local Redis instance, memory ≥4 GB | Caches frequently queried quality inspection indicators and approval records, supporting multi-node concurrent processing |
STORAGE_S3_ENDPOINT | local object storage endpoint, such as http://intranet IP:9000 | Stores scanned file materials, meeting the local deployment requirement of no public network access |
FILE_TOKEN_KEY | 32-bit random string calibrated via actual testing | Encrypts access tokens for file materials, protecting sensitive grain reserve data |
ROOT_KEY | custom complex key, length ≥16 characters | Manages system-level permissions, preventing unauthorized modification of knowledge base configurations |
Three common configuration errors
- Phenomenon: After upgrading to FastGPT v4.17.0, the reranking model fails to start when using offline configuration, and the interface returns a 500 status code. Cause: The storage path for local model caching is not specified in the
STORAGE_S3_ENDPOINTconfiguration, causing model files to fail to load properly. - Phenomenon: After associating a knowledge base, locally stored file materials cannot be accessed, with the prompt "Resource unavailable". Cause:
FILE_TOKEN_KEYis not configured to generate access tokens, or the token expiration time is set too short, causing material access permissions to expire. - Phenomenon: During bulk import of declaration materials, some images fail to load. Cause: The intranet access address is not specified in the
FE_DOMAINconfiguration, causing the frontend to fail to resolve local stored image paths.
How to confirm the configuration is properly set
- Access the FastGPT backend system monitoring page to check database connection status, confirming that the connection count set by
DB_MAX_LINKdoes not trigger overload alerts. - Upload a scanned copy of a single grain reserve quality inspection report, verifying that the image loads normally with no "Resource unavailable" prompts.
- Trigger a bulk import task for declaration materials, and check the Redis cache monitoring panel to confirm that frequently queried quality inspection data and approval records are properly cached.
- Switch to offline mode, call the reranking model to process test file materials, and confirm that no errors are returned and results meet expectations.
The values provided here are common starting points and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.