Reference only; verify against the target FastGPT release and deployment documentation.
What Data for This Category Looks Like
Data for this category primarily comes from the integrated budget management system, project declaration materials, fund payment data, business system ledgers, and satisfaction surveys. Daily processed documents include performance target forms, self-evaluation forms, performance evaluation reports, supporting material lists, and rectification reports. These documents contain fields such as project number, fund amount, performance indicators, completion status, and supporting material number. Data updates occur annually. The volume of documents processed in a single task varies with the number of projects. No high-frequency updates happen within fixed cycles.
What Constraints Do These Characteristics Impose on Deployment and Upgrade
Multiple business systems serve as data sources. Deployment requires configuring permission settings and interface adaptation rules for cross-system data synchronization. The low-frequency annual update pattern demands setting a reasonable incremental synchronization trigger mechanism during deployment to avoid excessive system resource usage from full data pulls. Diverse document types include structured fields and unstructured supporting materials. This demands adapting parsing and storage logic for multi-format data. Project-associated fields must align strictly with business system fields. Deployment requires verifying field mapping rules to prevent data misalignment that harms performance evaluation accuracy.
Configuration Recommendations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 10–15 | Meets concurrent requirements for annual batch data pulls and real-time question answering, and prevents database connection exhaustion |
MONGODB_URI | Points to a dedicated MongoDB instance storing performance documents and evaluation records | Isolates business data from other system data, ensuring the security and traceability of performance evaluation data |
PG_URL | Address of the PostgreSQL database linked to the integrated budget management system | Directly retrieves structured business data such as budgets and payments, reducing errors in data synchronization links |
REDIS_URL | Address of a Redis instance deployed in a local private network | Caches frequently queried policy documents and performance indicator data, reducing database access latency |
STORAGE_S3_ENDPOINT | Endpoint address adapted to the internal object storage service | Stores unstructured documents such as supporting materials, and uniformly manages files across the entire performance evaluation process |
Three Common Misconfigurations
- Database tool calls fail in workflows, returning connection timeout or permission denied errors. This occurs when
PG_URLorMONGODB_URIis incorrectly pointed to a non-business system database, or whenDB_MAX_LINKis set too low, causing connection pool exhaustion. - All model calls fail, returning a 500 status code. This occurs when
REDIS_URLis misconfigured, preventing the cache service from starting normally and affecting context management and data caching for model calls. - Official website example workflows cannot be reused, with field mismatch prompts appearing. This occurs when data import is not configured according to the category-specific field mapping rules, and a generic workflow template is used directly without adapting to the unique fields of performance target forms and self-evaluation forms.
How to Verify Proper Configuration
- Run a single data synchronization test to verify that structured data pulled from the integrated budget management system fully aligns with locally stored fields.
- Submit a question about performance policies to confirm that returned results contain accurate content from the corresponding documents, and that cache hit rates match expected configuration.
- Upload a performance supporting material document to confirm that the file can be stored normally and correctly called during the question answering workflow.
- Run a complete workflow test to verify that no errors occur across the full process of tool calls, data pulls, and question answering generation.
The values provided are common starting points and should be evaluated against applicable specific 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.