What the data for this category looks like
Data sources for engineering consulting intelligent due diligence reports typically include official industry documents such as project approval letters, geological survey reports, architectural construction drawings, cost budget documents, and bidding documents. Update frequency adjusts dynamically based on project progress, with on-demand updates from pre-planning through completion stages. Document structures generally include four core modules: project overview, technical parameters, cost details, and compliance explanations. Fields include project number, construction area, project cost, survey point coordinates, compliance clause numbers, and more. Most use standard engineering industry units such as square meters, ten thousand yuan, and meters.
What constraints these characteristics impose on HTTP interfaces and external systems
The multi-source structured nature of engineering consulting due diligence reports requires HTTP interfaces to support file upload and synchronization with custom metadata. The mixed structure of long text and structured fields requires interfaces to support chunked transfer of large files, to avoid single-request timeouts. The dynamic updates tied to project progress require interfaces to support incremental pull and version tagging, to avoid repeated full data synchronization. The specialized engineering unit system requires interface parameters to support custom unit mapping rules, ensuring field units after data parsing match the original documents. The number association design for compliance clauses requires interface return results to retain field associations, avoiding misalignment of parsed data.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500–1000 MB | Engineering consulting reports often include large construction drawings and cost list attachments, requiring support for large-file uploads |
PARSE_FILE_TIMEOUT_SECONDS | 600–900 seconds | Engineering documents contain multi-format drawings and long text content, with significantly longer parsing times than general documents |
API_REQUEST_TIMEOUT | 300 seconds | Synchronizing engineering data with external systems requires transferring complete metadata and associated files, leading to longer request cycles |
CUSTOM_FIELD_MAPPING | Configure according to common engineering industry practices | Engineering consulting reports include specialized fields and units, requiring custom mapping rules to match original document formats |
SYNC_INCREMENTAL_ENABLE | Enabled | Engineering project progress updates dynamically, and incremental synchronization reduces repeated transfers and resource usage |
API_AUTH_TYPE | Bearer Token | Complies with FastGPT official interface authentication specifications, and adapts to the authentication logic of most external engineering management systems |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common errors
- Symptom: A custom external interface call returns a 404 status code, but the interface works normally when tested with a standalone tool. Cause: FastGPT configuration does not include the full path for
{{baseURL}}, or the{{authorization}}parameter does not carry a valid authentication token, resulting in interface requests failing to match the correct route. - Symptom: A call to the
/api/core/dataset/updateinterface returns a 500 status code, but the corresponding function works normally in the browser. Cause: The request parameter format for this interface in FastGPT 4.9.0 does not match the official specification, or the response data structure returned by the external system does not match the parsing rules expected by FastGPT. - Symptom: Fields such as construction area in synchronized engineering due diligence reports lose their unit information. Cause: The
CUSTOM_FIELD_MAPPINGparameter is not configured, and no specialized engineering unit rules are mapped, resulting in the interface automatically discarding non-standard unit fields during parsing.
How to confirm the configuration is correct
- Use the interface debugging tool provided by FastGPT, pass test engineering due diligence report metadata, and confirm the interface returns a normal response.
- Check the external system synchronization logs, confirm that synchronization operations only process recently modified project data, and do not fully overwrite all historical data.
- Export the parsed data returned by the interface, verify that field names and units match the format of the original engineering documents.
- Check the API authentication configuration in the FastGPT backend, confirm that authentication parameter values meet the requirements of the external system.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.