What the data for this category looks like
The data used for engineering consulting supervision declaration material and file review primarily originates from official documents across four project phases: initiation, bidding, construction supervision, and final acceptance. These documents include feasibility study reports, supervision logs, approval submission forms, and acceptance slips. Data sources are scattered across state-owned enterprise internal project management systems and offline paper archives. It updates per project phase, with up to dozens of updates per full project cycle. Each document has two components: structured fields and attachments. Structured fields include project unified code, supervision section number, construction unit, approval status, and others. Attachments are mostly PDF and Word files, with single documents ranging from several to dozens of pages.
What constraints these characteristics impose on the deployment and upgrade link
The multi-source scattered nature, phased updates, and industry-specific fields of engineering consulting supervision declaration material and file review data create multiple constraints for the deployment and upgrade process. Multi-source heterogeneous data requires configuring multiple types of database connections. Structured project metadata must connect to PG_URL, while unstructured case documents must connect to STORAGE_S3_ENDPOINT. High-frequency incremental updates require setting a reasonable synchronization frequency during deployment, to avoid excessive resource usage from full synchronization. Industry-specific fields such as supervision section number need precise mapping during knowledge base configuration. During upgrades, retain original field rules to prevent data import errors. Strict document format requirements during the review process require configuring file verification parameters during deployment, to ensure uploaded materials meet specifications.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 connections | Adapt to concurrent access volumes of engineering supervision projects, avoid exhausting database connections, and ensure stable review workflows |
PG_URL | postgresql://{username}:{password}@{database address}:5432/supervision project database | Store structured metadata such as project unified code and supervision section number, support precise field queries |
REDIS_URL | redis://:{authentication password}@{cache address}:6379/0 | Cache frequently accessed review rules and project metadata, reduce repeated query load on the database |
STORAGE_S3_ENDPOINT | Configure according to the actual endpoint of project file storage, set single bucket capacity limit to 1000 GB | Store PDF and Word attachments of supervision case files, adapt to storage needs for multiple large-volume documents |
FILE_TOKEN_KEY | Randomly generated 32-bit string | Encrypt file download tokens, prevent unauthorized access to confidential engineering declaration materials |
Three common mistakes
- Symptom: When importing knowledge base data exported from FastGPT v4.9.2 into v4.12.1, an error "field format mismatch" is prompted. Cause: The order and required items of project-specific fields were not adjusted according to the current version's CSV template. Engineering supervision declaration materials include non-general fields such as supervision section number, which were not synchronously updated after template changes.
- Symptom: The number of returned declaration material case files in knowledge base search is fixed, and the upper limit cannot be adjusted customarily. Cause: The corresponding parameter was not correctly linked in the system configuration, or the service was not restarted to make the new configuration take effect.
- Symptom: Custom models cannot be manually connected in the SaaS version, and a permission denied prompt is displayed. Cause: The super administrator identity was not verified via
ROOT_KEY, or model access permissions for the SaaS version were not enabled in the backend.
How to confirm the configuration is correct
- Log in to the FastGPT v4.17.0 system backend, check the
STORAGE_S3_ENDPOINTconfiguration item, and confirm it matches the actual endpoint of the project file storage. - Upload a PDF of engineering supervision declaration materials, test the file upload and parsing process, confirm no errors occur and field mapping is correct.
- Initiate a knowledge base search for file review, check that the returned result fields match the data in the project management system.
- View system operation logs, confirm that the number of database connections does not exceed the configured
DB_MAX_LINKthreshold.
The values provided are common starting points and should be evaluated 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.