What this type of data looks like
Ledger data for power generation equipment manufacturing comes from four main sources: equipment operation log systems, equipment maintenance work order systems, spare parts inventory management systems, and quality inspection report systems. Data updates occur per shift or daily. A single structured ledger document includes standard fields: equipment ID, model, rated power, cumulative operating hours, last maintenance time, and replaced spare parts model. Supported units include kW, hours, counts, and others. Aggregated reports integrate operation data and maintenance status for multiple pieces of equipment. These reports require processing multi-source structured and unstructured information.
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 |
|---|---|---|
PG_URL | postgresql://username:password@host:5432/equipment_ledger | Adapts to relational storage for structured ledgers, matches standardized fields such as equipment ID and operating hours |
MONGODB_URI | mongodb://username:password@host:27017/report_archive | Stores unstructured aggregated reports and archived logs, supports reading and writing of massive small files |
REDIS_URL | redis://:password@host:6379/0 | Caches frequently queried ledger data and retrieval results, reduces response latency for report generation |
DB_MAX_LINK | 80–120 concurrent connections | Adapts to daily high-frequency updated ledger data access, prevents database connection exhaustion |
STORAGE_S3_ENDPOINT | http://local-storage:9000 | Stores batch-generated aggregated report files, adapts to large-capacity archiving requirements |
FILE_TOKEN_KEY | Random 32-character sequence | Encrypts access tokens for ledger files, ensures access security of sensitive equipment data |
Three common mistakes
- Phenomenon: Hybrid retrieval response time exceeds the preset threshold, and the number of results returned by a single query does not match the preset value. Cause: The incremental sync cycle of the vector database is not adjusted according to the update frequency of ledger data, resulting in loading excessive historical archived data during retrieval.
- Phenomenon: The associated channel interface fails to load in the FastGPT service started via Docker. Cause:
REDIS_URLorMONGODB_URIis not configured correctly, causing abnormal cache and storage links for channel configuration, and failing to load interface resources normally. - Phenomenon: The first question answering call works normally, but subsequent requests with multi-round context directly return an error. Cause: The concurrent connection number of
DB_MAX_LINKis not adjusted, and multi-round requests exhaust database connections, making it impossible to load ledger data associated with the context.
How to confirm the configuration is correct
- Perform a database connectivity test to verify that connections for
PG_URLandMONGODB_URIare normal, and confirm that preset power generation equipment test ledger data can be read. - Submit a small-scale ledger aggregation task, check the cache hit rate, and confirm that response latency meets business expectations.
- Upload a standard ledger document to verify that file storage and reading functions for
STORAGE_S3_ENDPOINTwork normally. - Log in to the platform backend using
ROOT_KEY, confirm that permission configuration is normal, and that knowledge base and model parameters can be managed normally.
The values provided here 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.