What the data for this category looks like
Power generation equipment manufacturing documents and materials mainly originate from equipment design and development documents, production manufacturing process specifications, factory quality inspection reports, bidding technical documents, operation and maintenance manuals, and similar materials. The data update schedule is adjusted based on project progress. Batch updates are performed when new models are initiated for development, and synchronized batch updates occur during mass production. Document structures vary widely. Some documents are long specifications spanning dozens of pages, while others are single-page quality inspection sheets or parts lists. Fields include dedicated parameters such as equipment model, serial number, rated power (unit: kW), working pressure (unit: MPa), and operating duration (unit: h).
What constraints these characteristics impose on deployment and upgrade
The high proportion of long documents and presence of professional parameter fields require adjustments to vector storage and chunking strategies during deployment, to avoid loss of key information. Data updates follow no fixed cycle, with frequent incremental synchronization requirements. Flexible data source access rules must be configured. The volume of project documents continues to grow alongside production progress, creating horizontal expansion requirements for database storage and connection capabilities. Semantic recognition of dedicated parameters requires embedding models adapted to industrial terminology, otherwise retrieval accuracy will decrease. During deployment and upgrade, mixed storage needs for structured quality inspection data and unstructured documents must be balanced, to avoid performance bottlenecks from single storage solutions.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 30–50 connections | Power generation equipment documents are mostly long texts, and database connections are frequent. This value avoids connection exhaustion and disk I/O overload |
STORAGE_S3_ENDPOINT | Storage endpoint adapted to files over 1000 MB | Documents contain large numbers of drawing attachments and process videos, with prominent demand for large file read/write operations |
PG_URL | Read-write separated PostgreSQL instance address | Structured data such as quality inspection reports is queried frequently. Separating read and write operations improves query performance |
REDIS_URL | Instance with RDB persistence | Caches vector retrieval intermediate results and user sessions to reduce repeated calculation overhead |
MONGODB_URI | Sharded cluster address | Project documents accumulate quickly in increments. Sharded clusters enable horizontal expansion of storage capacity |
FILE_TOKEN_KEY | 32-bit random string | Ensures signature security of file upload interfaces and prevents unauthorized access |
Three common mistakes
- Symptom: Disk read/write rate continues to exceed the threshold after the container starts. Cause: The
DB_MAX_LINKparameter is not restricted, causing the database connection pool to create connections without restraint and generating a large number of disk I/O operations. - Symptom: Vector database deployment fails, with a prompt indicating incorrect pg dependency configuration. Cause: A generic compose file is used directly, without replacing it with a configuration adapted to structured document storage for power generation equipment.
- Symptom: Professional parameter fields are missing from long document retrieval results. Cause: The embedding model is not adapted to the dedicated units of power generation equipment such as MPa and kW, resulting in failed field parsing.
How to confirm configurations are correctly set
- Check database connection pool monitoring to confirm that the number of active connections is within the range set by
DB_MAX_LINK. - Upload a 100-page process document containing drawing attachments to verify that the file upload and chunked storage processes operate normally.
- Perform a vector retrieval targeting equipment model parameters to confirm that the returned results include the target professional fields.
- Restart the FastGPT service, then check the logs for configuration loading errors to confirm that all parameters take effect.
The values provided are common starting points and should be measured 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.