What this category of data looks like
Engineering consulting supervision data mainly comes from project supervision ledgers, on-site inspection records, acceptance reports, and rectification and closure documents. Data update rhythm follows supervision milestones. Daily on-site inspection records are updated each day. Acceptance documents are synced in real time after milestone completion. Most documents use structured fields, including supervision number, project name, construction unit, acceptance status, rectification item list and other fields. Units mostly use standard engineering domain units such as item, day, square meter. Some documents include high-definition drawing attachments.
What constraints these characteristics impose on the HTTP Interfaces and External Systems link
The structured fields of engineering consulting supervision contain engineering-specific units. HTTP interfaces must strictly match field formats and unit rules to avoid data parsing exceptions. Supervision documents include high-definition drawing attachments, so interfaces must support passing attachment addresses or configuring large file transfer parameters. Data updates fluctuate with supervision milestones. Call volume increases significantly during peak acceptance periods, so interfaces must adapt to elastic call thresholds. Project association attributes are strong. Interface requests must carry project numbers as core association parameters to ensure accurate data attribution and avoid cross-project data confusion.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Request timeout | 600 seconds | Engineering supervision documents include drawing attachments, so interface response times are relatively long. 600 seconds covers most synchronization scenarios, and adapts to the default configuration adjustment range of FastGPT v4.17.0 |
Authorization | Bearer ${internal system secret} | Internal supervision systems of Chinese state-owned enterprises and public institutions mostly use Bearer token authentication, which complies with internal system security specifications |
variables | {"projectCode": "${bound project code}", "syncType": "daily"} | Supervision data is grouped by project. Passing the project code via variables can accurately match corresponding data, adapting to the needs of multi-project parallel synchronization |
responseData | Extract the data field under the root node | Most internal engineering system interface return data is encapsulated in the root node data field, which facilitates subsequent process call parsing |
| Request retry count | 3 times | Network fluctuations may cause interface call failures. 3 retries can improve call success rate without affecting business |
Three common mistakes
- The symptom is that when FastGPT calls the interface of a cloud-deployed supervision system, a connection timeout is returned with status code 504. The cause is that the cloud security group did not open the corresponding port and did not configure the intranet access whitelist, so external requests cannot penetrate the network barrier.
- The symptom is that the Docker container deploying FastGPT restarts continuously, and the logs show interface call timeout errors. The cause is that the HTTP request timeout was not adjusted, and the response time of the supervision system interface exceeded the default timeout threshold of the container.
- The symptom is that the HTTP module returns empty fields after sending a request, making it impossible to parse supervision data. The cause is that the project code parameter was not passed via
variables, so the interface cannot match the corresponding data and returns an empty result set.
How to confirm the configuration is correct
- Initiate a single test request, check whether the returned
responseDatacontains the expected supervision fields, and whether the field units comply with engineering standards. - Simulate the call volume during peak periods, check whether the interface call success rate meets business expectations, and adjust the request retry count and timeout.
- View FastGPT logs to confirm that the
Authorizationtoken has been correctly carried, and no authentication failure errors appear. - Bind a test project, check whether the synced data is consistent with the ledger in the supervision system, with no missing fields or unit errors.
The values provided here 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.