What the data for this category looks like
Data sources for refinery and chemical production cover equipment operation logs, patrol records, equipment ledgers, safety operation procedures, employee scheduling documents, and training materials. Update rhythms vary widely: equipment operation logs update in real time, patrol records are submitted per shift, and ledgers and procedure documents update quarterly or after major changes. Document structures include structured fields (such as equipment ID, operating pressure, temperature values, with units of MPa and ℃) and unstructured text (such as patrol reports, training courseware). Single document lengths range from tens of characters of alarm information to thousands of characters of complete operation procedures.
What constraints these characteristics impose on deployment and upgrade
The multi-source and heterogeneous data characteristics of refinery and chemical production require the deployment phase to adapt storage and retrieval configurations for structured and unstructured data. The high-frequency updates of real-time equipment data require adjusting the refresh frequency of vector indexes to avoid index lag. The large difference in document lengths requires chunking configurations to balance adaptation for short alarms and long procedures, avoiding overly fragmented or overly long chunks that affect retrieval effectiveness. Under state-owned enterprise compliance requirements, data must be stored locally. Deployment requires configuring storage endpoints that meet security specifications. The upgrade phase must ensure historical indexes are not lost, to avoid affecting query needs for daily employee office work and production patrols.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 concurrent connections | Adapts to concurrent access needs of multi-source data for refinery and chemical production, avoids connection pool exhaustion |
MONGODB_URI | Cluster connection address with read-write separation | Supports high-availability storage of massive structured and unstructured data for refinery and chemical production |
REDIS_URL | Primary-replica replication cluster address, memory configuration ≥1000 MB | Caches vector retrieval results and employee session data, ensures high-concurrency response |
STORAGE_S3_ENDPOINT | Local storage endpoint that meets state-owned enterprise security compliance requirements | Meets security compliance requirements for local storage of refinery and chemical production data |
| Chunk length | 800–1200 characters | Adapts to mixed documents of varying lengths such as refinery and chemical production patrol records and operation procedures |
| Single search limit | Top 10 results | Balances recall range and response speed of refinery and chemical production knowledge base retrieval |
Three common errors
- Symptom: After reranker model deployment, connectivity tests pass, but the rerank marker field in each retrieval return result is always false. Cause: The rerank switch is not enabled in the knowledge base configuration, or the reranker model is not bound to the corresponding dataset.
- Symptom: Chinese garbled characters appear after importing CSV-format refinery and chemical production ledger files. The character encoding setting has been confirmed to be correct. Cause: The CSV file uses GBK encoding but the corresponding encoding format was not specified during import, or the file contains invisible control characters.
- Symptom: After upgrading FastGPT from version 4.9 to 4.10, the knowledge base with original indexes cannot trigger vector retrieval, and searches return no results. Cause: The vector index format of the old version is incompatible with the new version, and no index reconstruction or migration operation was performed.
How to confirm the configuration is properly set
- Log in to the FastGPT admin backend database monitoring panel, check the connection status of
MONGODB_URIandREDIS_URL, confirm there are no abnormal errors. - Upload a test file of refinery and chemical production patrol records, perform vector retrieval and trigger the rerank operation, confirm that the rerank field of the returned results is true.
- After upgrading to FastGPT v4.17.0, select a historical knowledge base to perform manual index reconstruction, verify that the retrieval function returns to normal after completion.
- Initiate 10 concurrent employee query requests, confirm that the system response has no timeouts, and the connection count does not exceed the
DB_MAX_LINKconfiguration value.
The values provided above 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.