What this category of data looks like
Thermal industry data primarily comes from thermal plant SCADA monitoring systems, pipeline pressure and temperature collection platforms, user heating demand reporting systems, and quarterly heating planning documents. Real-time monitoring data includes metrics such as water supply temperature, return water pressure, and pipeline flow. Its update frequency is adjusted based on collection node density. Operational data consists of structured plant inspection logs, fault work orders, and unstructured pipeline renovation plan documents. Data fields include identification fields such as plant number and pipeline segment ID. Temperature is measured in degrees Celsius, pressure in megapascals, and flow in cubic meters per hour.
What constraints these characteristics impose on knowledge base retrieval and recall
High-frequency updates of real-time monitoring data require the retrieval system to support incremental synchronization mechanisms. This avoids resource consumption and data delays caused by full synchronization. The presence of structured fields and fixed units requires the retrieval link to support both exact field matching and unit verification. This prevents irrelevant data from being recalled. Long-text operational logs and planning documents need a reasonable segmentation strategy. This preserves contextual logic and avoids information fragmentation. The decentralized nature of multi-source data requires synchronization tasks to adapt to interface formats of different data sources. This ensures the completeness of recalled data.
How to set the configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
sync_interval | 10 seconds – 5 minutes | Adapts to the update frequency of thermal real-time monitoring data, ensuring timeliness of recalled data |
chunk_size | 800–1200 characters | Matches the information density of thermal operational logs and planning documents, avoiding damage to contextual associations from segmentation |
similarity_threshold | 0.75–0.85 | Balances matching accuracy and recall range for thermal data, filtering low-relevance non-target entries |
recall_top_k | Top 15–20 entries | Covers associated information across multiple plants and pipeline segments, meeting the comprehensiveness requirements of investment research scenarios |
rerank_top_n | Top 5–8 entries | Refines retrieval results, reducing secondary filtering costs for investment research personnel |
parse_chunk_overlap | 100–150 characters | Retains contextual coherence for long-text segmentation, preventing key logic from being split and fragmented |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common misconfigurations
- Phenomenon: After starting a private deployment using docker compose, accessing the knowledge base module returns a 502 Bad Gateway error. Cause: The default configuration does not map the vector database port to the host machine. The retrieval service cannot connect to the vector database instance.
- Phenomenon: Knowledge base retrieval response time exceeds 30 seconds, and the number of recalled results falls below the configured requirement. Cause: The vector database query concurrency parameters are not adjusted. GPU-accelerated vector retrieval optimization is not enabled. This leads to low retrieval efficiency for large-scale thermal data.
- Phenomenon: Retrieval results include thermal data entries with temperature units in degrees Fahrenheit. Cause: Field-level unit matching verification is not enabled. Only full-text retrieval is used, causing similar values with different units to be mistakenly recalled.
How to confirm the configuration is correct
- Run a curl command to request the vector database service port. Confirm a 200 OK status code is returned to verify normal database connectivity.
- Upload a single thermal operational log document. View the parsed segmentation results, and check whether the segmentation length and overlap parameters match the configured requirements.
- Enter retrieval keywords such as "pipeline pressure 0.5 megapascals". Initiate a simulated retrieval request, and check whether the number of recalled results and similarity meet the configured thresholds.
- View the synchronization task running logs. Confirm that the real-time monitoring data synchronization interval matches the configuration. Ensure there are no data delay alarm records.
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.