What Data for This Category Looks Like
Data sources for railway and road investment research include official scheduling systems, road administration inspection equipment, maintenance ledger reports, route design drawings, monthly passenger and freight traffic statistical reports, and more. Update frequencies vary significantly. Real-time scheduling data updates as train or vehicle flow changes. Monthly maintenance reports and annual operation analysis reports are released per natural cycles. Route infrastructure files are static archived data. Document structures cover structured database tables, unstructured PDF/Word reports, and inspection images in image formats. Field units include multiple measurement types such as kilometers, trains per hour, and times per year.
Constraints Imposed on Database and Operations by These Characteristics
Data sources with varied update frequencies require the database to support layered synchronization mechanisms. These mechanisms distinguish real-time incremental tasks from scheduled full-volume tasks to avoid excessive resource usage. Unit differences in structured fields require unified mapping during data import. Without this, statistical deviations will occur during investment research analysis. Differences in length and format of unstructured documents require the parsing module to adapt to long-text chunking and multi-format parsing rules. This prevents parsing timeouts or content loss. As road network scale expands, data volume continues to grow. This requires configuring database sharded storage and hot/cold data separation strategies to maintain retrieval and operations efficiency. Investment research scenarios require retaining full historical data. Therefore, the operations link needs additional archived storage policies to prevent data loss.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGO_CONNECTION_STRING | mongodb://username:password@host:port/fastgpt?authSource=admin | Railway and road investment research data mostly uses MongoDB for archiving and retrieval. Specify the authentication source and target database to ensure connection legitimacy |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Railway and road route design drawings and annual maintenance report files have large sizes. This setting adapts to large file upload requirements |
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Long document parsing takes significant time. This prevents file parsing failures from timeout interruptions |
DB_SYNC_INTERVAL | 300 seconds for real-time tasks, 86400 seconds for full-volume tasks | Matches the update rhythms of real-time scheduling data and periodic reports for railways and roads |
TEXT2SQL_MAX_TABLES | 20 | Adapts to multi-table join query requirements in investment research scenarios, prevents generation of invalid SQL |
RECALL_CHUNK_SIZE | 1000–1200 characters | Adapts to paragraph lengths of railway and road technical documents, ensures completeness of retrieved content |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material formats, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Issue: Docker deployment fails to start, with logs showing
MongoDB connection failederror. Cause: TheMONGO_CONNECTION_STRINGconfiguration item was not modified. The default local test connection string was used, and it was not adapted to the actual deployed database address and authentication information. - Issue: Connection to the local database via MongoDB Compass fails, with authentication failure or connection timeout prompts. Cause: External access port mapping for MongoDB in the FastGPT container was not enabled, or database username and password were not configured correctly.
- Issue: Text2SQL-generated SQL returns empty results after execution. Cause: Field units were not unified, leading to database matching failures. Or the
TEXT2SQL_MAX_TABLESparameter limited the number of joined tables, and the generated SQL exceeded the limit.
How to Confirm Configurations Are Properly Set
- Run the built-in database connectivity test script to verify the validity of the
MONGO_CONNECTION_STRINGconfiguration. Adjust connection parameters based on test results. - Upload a single maximum-size railway or road-related document, check upload progress and parsing status, confirm that
UPLOAD_FILE_MAX_SIZEandPARSE_FILE_TIMEOUT_SECONDSlimits are not triggered. - Trigger a scheduled full-volume data synchronization task, check synchronization logs for abnormal interruptions. Adjust the
DB_SYNC_INTERVALvalue based on synchronization duration. - Submit a Text2SQL query request, verify that the generated SQL matches the expected number of tables and field formats. Adjust related configurations based on query results.
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.