What the Data for This Category Looks Like
Data for infrastructure construction declaration materials and file audit originates primarily from documents submitted by construction parties, including project approval applications, environmental impact reports, construction logs, and acceptance files. It also includes structured file metadata from government archiving systems. Data updates trigger per project milestones, with multiple batches submitted during a single project cycle. Document formats are primarily PDF, CAD drawings, and Word. Individual materials can be hundreds of pages long, and include structured fields such as project number, construction location, budget amount, approval nodes, and rectification records. Units include ten thousand yuan, square meters, calendar days, and others.
What Constraints These Characteristics Impose on Deployment and Upgrade
Reference only; verify against the target FastGPT release and deployment documentation.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
STORAGE_S3_ENDPOINT | http://minio.infra.gov:9000 | Declaration materials for infrastructure construction include many large PDF and CAD drawings. Using S3-compatible object storage reduces local storage pressure and supports multi-node file access |
DB_MAX_LINK | 20–30 connections | File data for this category grows linearly with project count. Excessive database connections consume server resources, while insufficient connections cause batch audit task queuing |
REDIS_URL | redis://:infra-redis-pass@redis.infra.gov:6379/0 | Required to cache parsed long document shards and batch audit task status, ensuring stability for parallel multi-task scheduling |
PG_URL | postgresql://fastgpt:db-pass@postgres.infra.gov:5432/infra_audit | Structured file fields require relational database transaction guarantees to avoid data inconsistency during submission |
FILE_TOKEN_KEY | Randomly generated 32-bit string | Used to sign file access tokens, restricting unauthorized access to declaration materials and file data |
Three Common Misconfigurations
- After deployment, calling the model returns a 403 status code, indicating the token lacks permission to use the model. The cause is failing to bind the required model permissions for the infrastructure construction scenario to the corresponding token, and failing to correctly associate the model interface address in the FastGPT configuration.
- After Docker deployment, logging into the one-api backend fails. The cause is failing to change the default one-api administrator password in advance, or failing to correctly configure the model proxy address in the FastGPT configuration.
- Multiple accounts cannot submit declaration materials normally. The cause is failing to configure permission groups for multiple accounts during deployment, and failing to bind the file access scope for the corresponding construction party's files.
How to Confirm the Configuration Is Correct
- Execute the
docker-compose logs -fcommand, check that no error messages appear in the connection logs for the database, Redis, and object storage services. - Upload a typical infrastructure construction declaration PDF, verify that core structured fields such as project number and construction location can be accurately extracted after parsing.
- Initiate a batch file audit task, check that the task queue can schedule normally, and there are no timeout or parsing failure errors.
- Log in with test accounts of different permissions, verify that permission groups can correctly restrict file upload and audit operation scopes for different accounts.
All configuration values provided here are common starting points. Adjust them based on the reader's own specific samples and requirements.
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.