What this category of data looks like
Data for this category primarily comes from event tracking systems, data warehouses, CRM systems, and ticket systems. Update frequency is mostly daily or weekly. Daily produced documents include usage analysis reports, event tracking plans, metric specification tables, and dashboard requirement sheets. Data fields include customer unique identifiers, function module names, call counts, session duration, metric specification version numbers, and more. Field units are mostly counts or minutes; some identifier fields have no units. The data structure balances structured metric rules and unstructured user behavior details, and must adapt to storage requirements for both relational and non-relational databases.
What constraints these characteristics impose on the database and operations workflow
Multi-source data sources require compatibility with both relational and non-relational database read/write protocols. Corresponding connection parameters must be configured separately. Daily or weekly update rhythms require designing batch synchronization scheduling mechanisms to avoid excessive database load caused by too large a single synchronization data volume. Diverse daily documents require separate storage of metric specification version information to prevent specification confusion across analysis reports of different cycles. High time costs for abnormal data troubleshooting require databases to establish indexes for user behavior fields, and retain complete query logs for backtracking. Severe error consequences require configuring data verification mechanisms and backup strategies to avoid operational errors caused by misjudged metrics or missed risks in customer renewal reviews.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://[username]:[password]@[host address]:[port]/[behavior_db]?authSource=admin | Adapts to unstructured user behavior detail data storage, supports fast retrieval of session paths and operation logs |
PG_URL | postgresql://[username]:[password]@[host address]:[port]/[product_analysis_db] | Stores structured metric specification tables and customer segmentation rules, supports complex join queries to generate cross-customer usage insights |
DB_MAX_LINK | 10–15 | Balances database connection resource usage and concurrent query efficiency, adapts to daily or weekly batch data synchronization tasks |
REDIS_URL | redis://:[password]@[host address]:[port]/0 | Caches frequently queried metric specifications and temporarily calculated customer segmentation results, reduces repeated database access pressure |
Three common errors
- Large language models cannot reference original text fragments after calling database data: Phenomenon: Model responses only include abstract conclusions, and do not include original content or detailed data queried from the database. Cause: Database query results are not bound to the conversation context, or the context injection function for database data is not enabled.
- Full database scans are triggered for every conversation: Phenomenon: Each question takes a long time to respond, and background logs show repeated full table queries or full data loads. Cause:
REDIS_URLcaching for frequently queried results is not configured, leading to full data loading on each call. - Database connection plugin configuration reports errors: Phenomenon: The interface prompts connection failure, timeout, insufficient permissions, or fails to pull data normally. Cause: Authentication parameters for
PG_URLorMONGODB_URIare filled incorrectly, database ports are not open for external access, or access whitelists for the corresponding databases are not configured.
How to confirm the configuration is complete
- In FastGPT v4.17.0, run a test query, enter the specified metric specification keywords, and check if matching structured data is returned from the database.
- View database connection logs, and confirm that the connection number configured for
DB_MAX_LINKdoes not remain exhausted. - Trigger a daily data synchronization task, and check if incremental data from the event tracking system can be pulled normally.
- Repeatedly execute the same frequently queried request, and check if the return speed meets daily usage requirements.
The values given 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 2026-10-19.