What this type of data looks like
Refining and chemical investment research data primarily comes from unit operation logs, crude oil quality inspection reports, process procedure documents, upstream and downstream supply and demand ledgers, and publicly available industry research reports. Structured data includes parameters such as unit temperature, pressure, and throughput, with dedicated units including ℃, MPa, and tons per hour. Unstructured data mostly consists of long documents, including process optimization plans and fault troubleshooting records. Real-time operation data updates at minute-level intervals. Industry research reports are updated weekly or monthly. Process documents are revised and synchronized quarterly.
Constraints on deployment and upgrade
Refining and chemical investment research data includes a large number of structured parameters with dedicated units and long-text unstructured documents. Real-time operation data has a high update frequency. During deployment, access formats for multi-source heterogeneous data must be adapted. Parsing failures caused by unit mismatches must be avoided. During upgrades, the real-time data link must remain uninterrupted. Disruptions to real-time investment research analysis must be prevented. A high proportion of long documents increases single-file parsing duration. Sufficient service resources must be reserved. The field structure of legacy data must be pre-compatible to avoid failed recall of historical data after an upgrade.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Individual refining process documents or operation logs often exceed the length of conventional documents. Extending parsing duration avoids timeout failures |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Refining research reports and unit log collections often reach hundreds of MB in scale. Raising the upload upper limit supports complete knowledge base imports |
SIMILARITY_THRESHOLD | 0.75–0.85 | Matching accuracy requirements for refining structured parameters are high. Low-match irrelevant data must be filtered |
CHUNK_SIZE | 1000–1200 characters | Refining process documents include continuous step descriptions. Adjusting segment length balances semantic completeness and recall accuracy |
PYTHON_EXEC_TIMEOUT | 300 seconds | Refining investment research often requires running batch data calculation scripts. Reserving sufficient duration prevents early termination of long-running tasks |
VECTOR_DB_REFRESH_INTERVAL | 60 seconds | Refining real-time operation data requires high-frequency synchronization to the vector database to ensure timeliness of investment research analysis |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- The model backend generates response logs, but workflow conversations display a failure. The cause is that the model call link of the workflow is not correctly configured during deployment, or the corresponding port is not opened for the workflow module to access the model service.
- Knowledge base data is lost after upgrading from versions V4.9.3 and V4.9.13 to V4.12.2. The cause is that the directories of the vector database and file storage are not mounted to the host machine. Executing
docker-compose downdeletes stored data in the temporary container. - No output or an error occurs after the Python code node is executed. The cause is that dependencies of the Python runtime environment are not configured, or the set
PYTHON_EXEC_TIMEOUTduration is insufficient. This leads to early termination of long-running calculation tasks.
How to confirm the configuration is correct
- Upload a typical refining process document. Check if the parsed segments retain key parameters and units. Verify that no timeout errors appear in the parsing progress.
- Run a test workflow that includes structured parameter retrieval. Confirm that the returned result fields and units conform to refining data specifications.
- Execute a Python code test node. Verify that the script can normally read knowledge base data and output expected results.
- Execute the version upgrade process. Check that the recall results of the original knowledge base are consistent with those before the upgrade. Confirm that the storage mount configuration takes effect.
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.