What the data for this category looks like
Data for internal office and employee services for building construction general contracting comes primarily from project schedule management systems, labor attendance systems, material requisition ledgers, and safety inspection records. Data is updated based on project nodes. Labor attendance data is synced daily. Material requisition ledgers are updated weekly. Documents are in structured table format, including fields such as project number, team name, attendance days, material code, requisition quantity, and inspection date. Units include days, pieces, cubic meters, square meters, and others.
What Constraints These Characteristics Impose on Deployment and Upgrade
Labor attendance data is synced at high frequency daily, so database connection pool configuration must be adapted to high-frequency read and write requirements. Material ledgers have multiple structured fields, so vector database indexes must match the field types. When project nodes are dense, data update frequency fluctuates sharply, so dynamic expansion configuration entry must be retained during upgrades. Attendance and salary queries related to employee services must be bound to project permissions, so permission verification rules must be configured synchronously during deployment. Offline deployment requires pre-caching localized project data dictionaries to avoid reliance on external interfaces.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 | Supports concurrent connection requirements for multiple teams and projects, adapted to high-frequency daily read/write operations for building construction project data |
MONGODB_URI | Points to a local or private cloud MongoDB instance, with access credentials | Stores structured project ledgers and inspection records, requires stable non-relational database support |
PG_URL | Points to a dedicated PostgreSQL instance, with connection timeout set to 60 seconds | Stores sensitive structured data such as employee attendance and salary, requires transaction support from relational databases |
REDIS_URL | Points to a local Redis cache instance, with memory limit set to 1000 MB | Caches frequently queried project permissions and employee information to reduce database read/write pressure |
STORAGE_S3_ENDPOINT | Configured as an internal object storage endpoint | Stores large files such as project drawings and inspection photos, meets internal data security requirements |
Three Common Mistakes
- After deployment, response timeout exceeds
40 seconds, shown as504 Gateway Timeouterror. The cause is thatDB_MAX_LINKis configured too low. Database connections are exhausted during concurrent read/write operations, unable to respond to employee query requests in a timely manner. - Local debugging works normally, but the Docker image fails to connect to the database after startup. The interface displays "Database connection failed". The cause is that access credentials for
MONGODB_URIorPG_URLare not correctly configured inside the image, or database port mapping is not enabled. - After upgrading to FastGPT v4.17.0, multi-question retrieval only displays a single question, with abnormal number of historical conversation retrieval results. The cause is failure to adapt to the new version's retrieval optimization logic, and failure to adjust the parameter range of the corresponding configuration.
How to Confirm Configuration is Complete
- Run a database connection test script to verify that
MONGODB_URIandPG_URLcan establish normal connections. - Check the Redis cache monitoring panel to confirm that memory usage of the
REDIS_URLconfiguration does not exceed the preset limit. - Submit high-frequency query requests to check whether response duration meets the threshold range expected by the business.
- Upload test project drawing files to verify that the storage service configured via
STORAGE_S3_ENDPOINTcan perform normal write and read operations.
The values provided are common starting points and should be measured 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.