What This Category of Data Looks Like
Data for public transit declaration materials and file audit primarily comes from electronic files uploaded via internal approval systems of bus operation entities and government declaration platforms. Update frequency aligns with declaration batches. Submissions are concentrated within a single declaration cycle, and routine files are archived weekly. Individual documents are mostly in PDF or Word format. They include fields such as declaring entity name, project type, submission date, approval status, file number, and associated attachments. Attachments are often route maps, station planning documents, and similar materials. Units used include characters, MB, and date formats.
Constraints Imposed by These Characteristics on Deployment and Upgrade
This category of data mostly consists of unstructured office documents. Support for multi-format parsing including PDF and Word is required. Corresponding format parsing components must be provided during deployment. Concentrated declaration batches generate bulk data requests. Database connection limits must be adjusted to avoid connection exhaustion. File number acts as a unique identifier field. Database indexes must be configured to improve retrieval efficiency. Government materials involve compliance storage requirements. Data persistence configurations must comply with public service data management regulations. Bulk document vector retrieval requirements also impact vector database selection and resource allocation.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 50–80 connections | Addresses bulk data requests from concentrated submissions, avoids database connection exhaustion |
MONGODB_URI | mongodb://mongo:27017/fastgpt_bus_audit | Creates a dedicated database instance to store file metadata, isolates business data |
PG_URL | postgresql://pguser:securepass@pg:5432/bus_vector | Stores vector retrieval data for files, separated from other business databases |
REDIS_URL | redis://redis:6379/1 | Caches frequently accessed file metadata, reduces database query load |
FILE_TOKEN_KEY | Randomly generated 32-bit string | Ensures secure upload and download of declaration attachments, complies with public service data security requirements |
STORAGE_S3_ENDPOINT | http://192.168.1.100:9000 | Stores attachment files for declaration materials, meets data compliance storage requirements for public service scenarios |
Three Common Configuration Mistakes
- Issue:
MongoDB connection refusederror appears after container startup, and file metadata cannot be loaded. Cause: The corresponding database container was not started with theMONGODB_URIconfiguration, or the connection parameter format is incorrect. - Issue: The file parsing failure rate is high when uploading declaration materials in bulk, and some attachments cannot extract text content. Cause: Format parsing components adapted to PDF and Word were not provided, only the default basic parsing capability was used.
- Issue: Existing file vector data cannot be read after migrating from a PostgreSQL version to a Milvus version. Cause: Vector data stored in PostgreSQL was not exported in advance, or the correct vector database connection address was not configured.
How to Confirm Successful Configuration
- Run the
docker pscommand to confirm that dependent containers including MongoDB, PostgreSQL, and Redis are all running. - Upload a test PDF for a bus route adjustment declaration, check whether the file parsing result includes key fields such as declaring entity name and project type.
- Initiate a simulated bulk declaration request, confirm that the database connection count does not exceed the
DB_MAX_LINKconfiguration value. - Call the vector retrieval interface, pass in keywords from the test file, confirm that the number of returned results matches the expected configuration.
The values provided here are common starting points, and should be evaluated against samples appropriate for the specific deployment.
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.