What this category of data looks like
This category of data originates from the budget management integrated system, project declaration materials, fund payment data, business system ledgers, and satisfaction surveys. The update frequency is once annually. Processed daily documents include performance target forms, self-assessment forms, performance evaluation reports, supporting material lists, and rectification reports. Document fields cover project identifiers, fund disbursement nodes, performance indicator completion status, supporting material associated IDs, and other relevant items. Single document sizes vary widely, from hundreds of characters for self-assessment forms to tens of thousands of characters for complete evaluation reports.
Constraints during deployment and upgrade
Data sources are scattered and require integration with multiple internal business systems. Configure multi-source data access capabilities during deployment to ensure compatibility of field formats across different systems. Enable regular full or incremental data synchronization after deployment to meet the annual update requirement and prevent knowledge base content from lagging. Adapt text processing workflows to handle documents of varying sizes. Ensure precise extraction of performance indicators and supporting material associated fields to avoid association errors that reduce Q&A accuracy. Enforce data desensitization and permission control rules during deployment to block leakage of confidential or sensitive fiscal data, and comply with government data security standards for the party building learning Q&A scenario.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://admin:securepass@internal-db:27017/fiscal_perf?authSource=admin | Stores knowledge base metadata for party building learning Q&A, performance document association information, and conversation logs. Must be bound to a dedicated business database |
REDIS_URL | redis://:cachepass@internal-cache:6379/1 | Caches frequently queried performance indicators and party building policy-related content to reduce repeated calls to the vector database |
STORAGE_S3_ENDPOINT | http://10.10.10.5:9000 | Stores large-capacity attachments such as performance evaluation reports and supporting materials, adapting to storage needs for documents of varying sizes |
DB_MAX_LINK | 800–1200 MB | Limits the total size of documents imported in a single batch to avoid exceeding system storage limits |
ROOT_KEY | Randomly generated 36-bit UUID string | Configures platform management permissions, used to create and manage knowledge bases dedicated to fiscal expenditure performance evaluation |
| Single recall limit | Top 6 entries | Matches the accuracy of performance documents and party building Q&A, filters low-relevance entries to improve answer accuracy |
Three common configuration errors
- Container startup triggers a
connection refusederror. Logs indicate failure to connect to the database specified byMONGODB_URI. Causes include incorrect database access permission configuration, or the database service not opening the required port. - After importing a knowledge base, a large number of performance document entries show empty fields. The system cannot associate supporting materials with these entries. Causes include failure to adapt to field format differences across multi-source data, and lack of field mapping configuration.
- Platform containers enter periodic infinite restart cycles and fail to load the party building learning Q&A knowledge base normally. Causes include no access password configured for
REDIS_URL, or network isolation policies between the cache service and main service restricting cross-segment communication.
How to confirm successful configuration
- Access the FastGPT management backend, view the database connection status panel, and confirm that the connection status of
MONGODB_URIandREDIS_URLshows normal. - Upload a standard performance self-assessment form as a test document, and confirm that the system correctly parses the performance indicator and project name fields in the document.
- Initiate a test query such as "What are the performance self-assessment basis for a certain project", and confirm that returned results include the associated supporting material list and self-assessment form content.
- Check container runtime logs to confirm that there are no error messages such as database connection timeouts or storage service access failures.
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.