What Data for This Category Looks Like
This category’s data originates from four core sources: art asset libraries, project management systems, engine resource packages, and outsourced delivery packages. Daily core documents include four types: art requirement forms, asset specification sheets, outsourced feedback forms, and acceptance records. Covered fields include asset unique identifiers, version numbers, specification parameters, acceptance statuses, delivery times, outsourcer information, and more. Data update frequency is typically daily or weekly. The volume of data updated in a single batch varies based on project scale. Core data consists of structured asset metadata and unstructured resource file association information.
Constraints Imposed on Database and Operations Workflows
Multi-source data access requires databases to support storage and synchronization of multiple data types. Databases must adapt to both document-style metadata and structured business records. High-frequency daily or weekly updates require configuration of incremental synchronization mechanisms. This avoids performance losses caused by full synchronization. Core fields such as asset unique identifiers and version numbers require indexes. This ensures query efficiency for time-consuming processes like specification confirmation and acceptance checks. Data inconsistencies directly lead to issues such as resource mismatches and engine import failures. Data consistency verification mechanisms must be configured. These periodically check for discrepancies between records in the asset library and database. Bulk imports of outsourced delivery packages create short-term high-concurrency write operations. Database connection pool parameters must be properly configured to avoid connection exhaustion.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://<db-user>:<db-pass>@<db-host>:27017/fastgpt_art_assets?authSource=admin | Art asset metadata and version records are unstructured document-style data. MongoDB is suited for storage and query of this type of data |
PG_URL | postgresql://<pg-user>:<pg-pass>@<pg-host>:5432/fastgpt_art_ops?sslmode=require | Structured business data such as outsourced feedback and acceptance records is well-suited for relational databases to manage transactions and associated queries |
DB_MAX_LINK | 20–30 | Art asset data is read and written frequently. This value balances concurrent processing capacity and database resource usage |
REDIS_URL | redis://:<redis-pass>@<redis-host>:6379/0 | Caches asset specification verification results and real-time outsourcer status. This reduces repeated database query pressure |
AIPROXY_API_ENDPOINT | http://aiproxy:3000 | Configure the root address of the AI Proxy service per official documentation. Do not append /v1 or /chat/completions. This address provides the basic AI call link for the server, and is distinct from single-model addresses such as VLLM/Ollama/Xinference |
AIPROXY_API_TOKEN | <proxy-admin-token> | Configure the administrator token consistent with the AI Proxy server per official documentation. The token cannot be empty. This token is distinct from application API Keys, model vendor keys, tokens issued per application, or token counts estimated per request. This ensures service startup verification passes |
Three Common Misconfigurations
- Symptom: The service fails to start with a MongoDB connection failure error. Knowledge base metadata cannot be written. Cause: Authentication parameters or database address for
MONGODB_URIare not configured correctly. The server cannot establish a database connection. - Symptom: Connection timeout errors occur during bulk import of outsourced delivery packages. Cause:
DB_MAX_LINKis set too low. Short-term high-concurrency write operations exhaust the database connection pool. New requests cannot acquire a connection. - Symptom: Asset specification verification processes frequently trigger repeated database queries. Cause: Redis cache expiration time is not configured based on asset update frequency. The cache expires prematurely, so it cannot reduce database query pressure.
How to Confirm Configurations Are Valid
- Start the FastGPT v4.17.0 server. Check startup logs for no database connection failure or AI Proxy configuration missing error messages.
- Upload a properly formatted outsourced delivery package. Verify that the system can correctly generate asset specification verification records and write them to the database.
- Log in to the Redis client. Confirm that the cache service properly stores temporary verification data and outsourcer status information.
- Trigger an incremental synchronization task. Verify that data from the art asset library and project management system can be correctly merged into the database.
Final Note
The configuration values listed above are common starting points. They should be evaluated against relevant operational samples and specific requirements.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.