What data looks like for this use case
Data for the real estate registration scenario comes from the real estate registration information system, cadastral survey database, housing transaction online signing system, tax system, and electronic certificate repository. It is updated fully or incrementally every day.
Daily processed documents include registration applications, ownership certificates, survey results, tax payment vouchers, real estate ownership certificates, and property registers. Fields cover real estate location, ownership number, right type, tax payment status, and more. Standard real estate-related units such as square meters and ten thousand yuan are commonly used. Document volume varies widely, ranging from tens of KB for applications to tens of MB for survey results.
What constraints do these characteristics impose on deployment and upgrade
The multi-source data linkage feature of this scenario requires that access permissions for multiple internal systems be connected during deployment, and cross-system data synchronization rules be configured. The daily update frequency requires that scheduled synchronization tasks be configured during deployment to avoid data lag affecting office efficiency.
There are many document types, including sensitive electronic certificates. During deployment and upgrade, compatible file storage and parsing capabilities must be configured, while ensuring permission security for data access. Time-consuming steps such as verifying ownership sources and comparing survey data require reserving sufficient computing resources during deployment. During upgrade, concurrency settings for model calls must be optimized to avoid task timeouts.
In FastGPT v4.17.0, the configuration logic for multi-source data synchronization has been adapted to the update frequency requirements of this scenario.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–200 connections | Address concurrent connection needs for daily synchronization across multiple systems, avoid exhausting database connections, and ensure stable execution of synchronization tasks |
MONGODB_URI | Full connection string configured per cluster nodes | Store real estate registration historical archives and real-time data, adapt to multi-node high-availability deployment, and meet data persistence requirements |
PG_URL | Connection string for local PostgreSQL instance | Store structured data such as system configurations and task logs, and adapt to relational database query needs |
REDIS_URL | 127.0.0.1:6379 (standalone deployment) or cluster connection string | Cache frequently queried ownership status and certificate information, reduce delay from cross-system calls, and improve internal office response speed |
STORAGE_S3_ENDPOINT | Address of local object storage or cloud storage node, such as http://192.168.1.100:9000 | Store large documents such as survey results and electronic certificates, and adapt to persistence and fast access of multi-format files |
FILE_TOKEN_KEY | Encryption key calibrated via actual testing | Protect access permissions for sensitive files such as electronic certificates and tax payment vouchers, and avoid information leakage from unauthorized access |
Three common mistakes
- Phenomenon: FastGPT container fails to start with a database connection timeout error, and the log shows
connection refused. Cause: The access address forMONGODB_URIorPG_URLis not configured correctly. The container internal network is not connected to external database instances, preventing connection establishment. - Phenomenon: The file parsing module returns a connection failure, and attempts to use local 127.0.0.1 are invalid. Cause: FastGPT runs in a Docker container. The local loopback address points to the container's internal network, and the host machine's file parsing service port is not mapped, preventing access to externally deployed parsing tools.
- Phenomenon: Database data is not updated after a scheduled synchronization task runs. Cause:
DB_MAX_LINKis not set to a reasonable value, leading to exhausted database connections during synchronization, and the task interrupts without completing data synchronization.
How to confirm configurations are correct
- Log in to the FastGPT backend system configuration page, and check that configuration values for parameters such as
MONGODB_URIandREDIS_URLmatch the actual deployed service addresses. - Upload a standard real estate ownership certificate scan, verify that file storage and parsing functions work normally, and the uploaded file can be viewed in the document management list.
- Manually trigger a ownership data synchronization task, check that the task log shows successful execution, and the data update time in the corresponding database meets expectations.
- Check the container's internal port mapping configuration, confirm that external access ports match the domain name configured in
FE_DOMAIN, and the frontend management page loads normally.
The values provided here are common starting points, and should be evaluated against samples from the actual deployment.
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.