What data for this use case looks like
Teams source customer requirement pool data from ticketing systems, customer interview records, CRM systems, and product roadmap documents. Daily maintained documents include requirement pool lists, requirement evaluation forms, customer feedback records, and version planning descriptions. Updates follow weekly or per-version iteration cycles. Single updates cover multi-dimensional fields: requirement source, priority, associated customer, evaluation cost, scheduling node, and more. No unified fixed units apply. Teams adjust fields dynamically based on business scenarios.
What constraints do these characteristics place on the database and operations workflow
Scattered data sources and concentrated update cycles require databases to support batch writing and associated multi-source data queries. This prevents query delays caused by excessive single-table data volume. Dynamic requirement pool data fields demand flexible database structures. Avoid overly rigid fixed table structures. Error outcomes such as distorted requirement commitments and unmet customer expectations require high availability for database operations. This ensures stable data writing and querying, and prevents requirement scheduling disruptions from database failures. Multi-dimensional requirement fields also need reasonable index configuration. This supports efficient completion of time-consuming tasks such as duplicate requirement merging and value evaluation.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://[auth-user]:[auth-pass]@[db-host]:[db-port]/fastgpt_crm?authSource=admin | Stores unstructured and semi-structured data including customer feedback records and requirement pool lists, adapts to multi-source imported document formats |
PG_URL | postgresql://[auth-user]:[auth-pass]@[db-host]:[db-port]/fastgpt_prd | Stores structured associated data including requirement evaluation forms and version planning descriptions, supports complex queries and transaction operations |
DB_MAX_LINK | 20–30 connections | Adapts to weekly or per-version batch data update requirements, avoids service interruptions caused by exhausted database connections |
REDIS_URL | redis://[redis-host]:[redis-port]/0 | Caches frequently accessed requirement pool priority data, reduces query pressure on the primary database |
| Database index configuration | Create joint indexes based on requirement source and priority fields | Supports fast filtering and sorting for duplicate requirement merging and value evaluation tasks |
Three Common Misconfigurations
- Symptom: FastGPT v4.17.0 fails to start with a MongoDB error prompt, and the service cannot launch normally. Cause: The
MONGODB_URIparameter is not configured correctly, or the database instance does not allow access to the corresponding port. - Symptom: An internal server error is displayed when installing the database connection plugin. Cause: The
PG_URLparameter contains invalid characters, or the associated PostgreSQL service is not running properly. - Symptom: Some records are not synchronized to the system after batch updating requirement pool data. Cause: A reasonable batch write timeout period (such as 60 seconds) is not set, causing some data write operations to time out and be interrupted.
How to Verify Configurations Are Correct
- Access the FastGPT v4.17.0 system management backend, navigate to the database configuration page, and verify that the
MONGODB_URI,PG_URL, andREDIS_URLparameters match the actual deployed database instance information. - Submit a simulated customer feedback record, perform a write operation, then query the corresponding database table to confirm that the data has been successfully stored.
- Review the system monitoring panel to confirm that the database connection count does not exceed the
DB_MAX_LINKsetting value, and there are no connection exhaustion or timeout alarms. - Trigger a simulated requirement merging operation, verify that the system correctly identifies duplicate requirements and completes sorting, and query results match the preset evaluation logic.
The values provided here are common starting points and should be evaluated 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 2026-10-19.