What the data for this category looks like
Data for this category primarily comes from tracking systems, data warehouses, CRM systems, and ticket systems. Data update cycles are typically daily or weekly. Supporting documentation includes usage analysis reports, tracking plans, indicator specification tables, and dashboard requirement forms. Common data fields include user identifiers, function modules, operation trigger counts, dwell time, associated customer IDs, and more. Units are mostly counts, minutes, and calendar days. Some indicators require unified units based on business specifications.
What constraints do these characteristics impose on HTTP interfaces and external systems?
Data sources for this category are scattered and have inconsistent formats. HTTP interfaces must support format adaptation and field mapping for multiple data sources. Data update cycles are daily or weekly. Interfaces must support batch pulling of historical data to avoid timeouts caused by overly large single requests. Supporting documentation includes multiple types of business specifications. Interfaces must support passing indicator specification parameters to ensure consistent data calculation. Data anomalies directly affect business judgments. Interfaces must include basic validation logic to filter invalid fields and abnormal values.
How to configure settings
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Authorization | Bearer {ACCESS_TOKEN} | Used for authentication when calling FastGPT application APIs externally, conforms to general HTTP authentication specifications |
variables | {"timeRange":"7d","metric":["triggerCount","stayDuration"]} | Used to pass the time range and indicator list for data queries, adapts to the weekly analysis requirements of this category |
responseData | {"format":"json","fields":["userId","moduleName","count"]} | Used to specify the format and fields of returned data, matches the field requirements of business analysis reports |
| Request timeout | 300–600 seconds | Adapts to the time required for batch pulling data from multiple data sources, prevents request interruptions caused by large data volumes |
| Single request data limit | 1000–5000 entries | Controls the amount of data returned by a single interface, prevents network transmission overload, and fits batch processing scenarios for daily or weekly data |
| Entries per pagination page | 100 entries/page | Adapts to segmented pulling of large data volumes, reduces network pressure from single requests |
Three common configuration mistakes
- Symptom: Calling the relevant API returns
Invalid URL, code: 500. Cause: AI Proxy environment variables are not configured correctly, preventing normal connectivity to the proxy service relied on by the API. - Symptom: Data analysis result fields returned by the API are missing or do not match expected values. Cause: Required indicator fields to return are not specified in
variablesor request parameters, causing default returned fields to not meet business specification requirements. - Symptom: API requests frequently trigger timeouts. Cause: The amount of data pulled in a single request exceeds the system processing limit, and a reasonable single request data limit or pagination parameters are not set.
How to verify successful configuration
- A test API request is executed. The returned HTTP status code is confirmed to be 200, and the response body is checked to contain expected data analysis fields.
- FastGPT server startup logs are reviewed, and any validation failure prompts for missing AI Proxy environment variables are confirmed to be absent.
- The data volume returned by the API is compared against the configured single request data limit, and the returned number of entries is confirmed to fall within the expected range.
- The API is called with different time range parameters, and the returned data interval is confirmed to match the parameter settings.
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 2026-10-19.