What data this use case relies on
The data sources for this use case include medical insurance intelligent monitoring systems, fund supervision rule bases, medical insurance settlement data, drug and consumable traceability codes, and complaint clues. The update frequency is once per month. Daily processed documents include audit notices, inspection work drafts, suspicious item lists, interview records, handling decision letters, and rectification reports. Most document structures include audit target information, suspicious entries, compliance bases, rectification feedback, and other content. Most fields are directly related to medical insurance fund usage and violation determination results. Additionally, policy interpretation and standard documents related to party building learning must be associated.
What constraints these characteristics impose on deployment and upgrades
Data sources are scattered and update cycles are fixed. This requires configuring multiple data source connections and scheduled synchronization rules during deployment, to ensure that monthly updated supervision rules and settlement data are synchronized to the knowledge base in a timely manner. Daily documents have diverse types and obvious structural differences. This requires configuring knowledge base indexes to adapt to the field characteristics of different documents, to avoid retrieval matching deviations. Party building learning content and audit business content must be deployed in association. This requires retaining the permission and retrieval linkage configuration of both types of knowledge bases during upgrades, and configuring a data verification link to prevent audit conclusion deviations caused by incorrect data flowing in. In addition, storage and access control for sensitive audit materials require additional permission configuration, to avoid data leakage risks.
Recommended Configuration Values
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://[username]:[password]@[host address]:[port]/fastgpt?authSource=admin | Stores metadata and retrieval indexes for audit documents and party building learning materials. Requires a dedicated database instance. |
REDIS_URL | redis://:[password]@[host address]:[port]/0 | Caches retrieval results, session states, and execution markers for scheduled synchronization tasks. Requires a dedicated database to avoid resource conflicts. |
DB_MAX_LINK | 80–150 connections | Adapts to connection requirements for monthly batch data synchronization and multi-user concurrent retrieval. |
STORAGE_S3_ENDPOINT | Fill in according to the local object storage service address, such as http://192.168.1.100:9000 | Stores large documents such as audit reports and drug and consumable traceability code files. Adapts to locally deployed storage services. |
FILE_TOKEN_KEY | Randomly generate a string of 32 or more characters | Generates temporary access tokens for documents, to prevent unauthorized access to sensitive audit materials. |
ROOT_KEY | Custom complex string with a length of no less than 16 characters | Configures system administrator permissions, to prevent unauthorized users from modifying knowledge bases and deployment parameters. |
Three Common Misconfigurations
- Symptom: Image build fails when executing the
docker buildcommand. Logs prompt that the base image pull timed out or dependency package installation failed. Cause: No domestic mirror source is configured to accelerate pulls, or the local network cannot access the official image repository, causing the build process to interrupt. - Symptom: The knowledge base retrieval interface returns a
404 - Resource Not Founderror. Cause: TheMONGODB_URIorREDIS_URLparameters are not configured correctly. The system cannot connect to the metadata storage or cache service, and cannot load the document indexes required for retrieval.
Reference only; verify against the target FastGPT release and deployment documentation.
How to Verify Successful Configuration
- Execute the
docker pscommand. Confirm that all FastGPT-related containers are running, with no abnormal exit records. - Log in to the system backend, upload an audit notice document, and perform a retrieval test. Confirm that matching party building learning content and audit rules are returned.
- Manually trigger a medical insurance data synchronization task. Check the system logs for no errors, and confirm that the synchronized documents appear in the knowledge base list.
- Access the domain configured in
FE_DOMAIN. Confirm that the frontend page loads normally, and that retrieval requests can be initiated normally.
The values listed here are common starting points. They should be evaluated against the reader's own specific samples and deployment environment.
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.