What the data for this category looks like
Ledger data for new energy development primarily comes from project initiation approval systems, on-site SCADA monitoring platforms, and operation and maintenance inspection record systems. Data update frequency varies by project phase: weekly updates during the early initiation phase, daily updates during grid-connected operation phases.
Reference only; verify against the target FastGPT release and deployment documentation.
Constraints imposed on HTTP interfaces and external systems
The multi-source heterogeneous nature and phased update rhythm of new energy development ledgers create clear adaptation constraints for HTTP interfaces.
Minor format differences exist between ledger data from different sources. Interfaces must support custom field mapping to convert raw external system fields into a unified ledger structure.
Daily updated operation data generates high-frequency, small-batch requests. Interfaces need reasonable concurrency limits to avoid exceeding external system call thresholds.
Strong standardization of field units requires interface return values to strictly retain standard units such as MW and kWh, with no unauthorized modifications.
Queries for unique project identifiers require interfaces to support batch data retrieval by ID, adapting to batch processing scenarios for ledger summary.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer {AIPROXY_API_TOKEN} | Complies with standard HTTP interface authentication specifications to ensure legitimacy of external system calls |
variables | {"project_ids": ["Project ID List"], "time_range": "Last 30 Days"} | Adapts to the core requirement of filtering ledgers by project and time range, simplifying parameter passing logic for external systems |
responseData | Return only standardized ledger fields | Avoids redundant data interfering with subsequent report generation processes, unifies data formats |
| Interface timeout | 600 seconds | New energy ledger data may include extensive historical records; a longer timeout ensures complete data retrieval |
| Batch query limit | Top 20 entries | Balances interface call efficiency and external system load, prevents excessive data volume in a single request |
| Field mapping rules | Calibrated based on actual testing | Different external systems have varying field naming conventions; mapping relationships must be adjusted based on actual docking scenarios |
Three common errors
- Symptom: The interface returns the error
IPROXY_API_ENDPOINT or AIPROXY_API_TOKEN is not set. Cause: The proxy interface endpoint and authentication token were not correctly filled in the FastGPT configuration page, resulting in interface authentication failure. - Symptom: A large number of slow query operations appear in MongoDB logs, and the page fails to load normally. Cause: No batch query limit was set, and a single interface request retrieved excessive historical ledger data, exceeding database processing thresholds.
- Symptom: After starting a conversation via the API, an extra initial record appears in the conversation history. Cause: The conversation context filtering rule was not correctly configured in
variables, causing the system to automatically append redundant initialization information.
How to confirm the configuration is complete
- Call the test interface, check if the returned
responseDataincludes preset standardized ledger fields, with no redundant or missing items. - View the interface call logs to confirm that the
Authorizationheader correctly carries the authentication token, with no authentication failure records. - Simulate a batch query request, verify that the number of returned entries does not exceed the configured batch query limit, confirming that the concurrency limit takes effect.
- Adjust the time range parameter in
variables, confirm that the data range returned by the interface matches 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 2026-09-28.