What the data for this category looks like
Power investment research data primarily comes from public grid dispatch reports, power market transaction quotes, technical documents from power equipment manufacturers, industry policy documents, and professional research reports. Update cadences vary significantly. Power market quotes update per trading session. Annual industry reports release per fiscal year. Policy files update irregularly based on industry regulatory requirements. Document structures include structured Excel quote sheets with fields such as unit ID, trading session, electricity price; semi-structured technical white papers; unstructured PDF research reports. Core fields include specialized units and identifiers like megawatt (MW), yuan per megawatt-hour, and unit ID.
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
The multi-format structure and specialized fields of power investment research data require retrieval systems to support both full-text semantic matching and structured field retrieval. This avoids invalid recalls caused by mismatched units or identifiers. Frequently updated market quote sheets need incremental synchronization mechanisms. These mechanisms prevent retrieval delays caused by full parsing. Long research reports and technical documents require appropriate segmentation strategies. These strategies ensure recalled segments cover core investment research information while avoiding redundant context. Data with different update frequencies need adapted index refresh cycles. These cycles maintain the timeliness of retrieval results.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall count | Top 10-15 results | Power investment research data has strong segmentation. Too many recalls introduce irrelevant content, while too few fail to cover core retrieval results |
similarity threshold | 0.72-0.85 | Power terminology is highly specialized. A threshold that is too low mixes in irrelevant general industry content, while a threshold that is too high misses precisely matched segmented data |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Large power equipment technical documents have lengthy content, with longer parsing time than general documents. Extend the timeout period to avoid parsing failures |
maxContext | 800-1200 characters | Core paragraphs of power research reports are moderately long. Excessively long context introduces irrelevant content, while excessively short context loses key technical or market information |
incremental sync trigger interval | Every 6 hours | Power market quote sheets have a high update frequency. Incremental synchronization reduces resource overhead from full scans and maintains data timeliness |
rerank return count | Top 5 results | Investment research decisions rely on accurate core data. Too many returned results distract decision-making focus and reduce retrieval efficiency |
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 case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Retrieval of power quote sheets takes more than 30 seconds. Cause: No incremental sync trigger interval is configured. Full scanning of a knowledge base with large volumes of historical quotes leads to excessive retrieval overhead.
- Symptom: After associating two knowledge bases, returned results for shared content have no priority distinction. Cause: No knowledge base weight parameters are set. Default sorting only uses match degree, which fails to meet the weight requirements of the business scenario.
- Symptom: Retrieval results include power data with mismatched units, such as recalling electricity prices listed in yuan per kilowatt-hour as yuan per megawatt-hour. Cause: No field-level retrieval configuration is enabled. Relying solely on full-text semantic retrieval leads to semantic matching deviations.
How to Verify Proper Configuration
- Initiate a retrieval targeting power quote sheets, check response latency, and adjust relevant timeout parameters until they meet business expectations.
- Query shared content from two associated knowledge bases simultaneously, verify whether returned results are sorted according to preset knowledge base weights.
- Check field matching status of retrieval results, confirm that core identifiers like correct units and unit IDs are included.
- Trigger an incremental sync task, verify that only newly added power data is updated, and no full re-parsing of the entire knowledge base is performed.
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.