What this type of data looks like
Data sources for wind power investment research include public technical parameter documents from wind turbine manufacturers, wind power project operation and maintenance logs, research reports released by industry associations, grid connection dispatch data, and time-series meteorological observation data. Update rhythms vary significantly: operation and maintenance logs and real-time grid connection data are updated near-real-time. Industry research reports are updated quarterly or annually. Wind turbine technical parameter documents are only updated when manufacturers iterate products.
Document structures include structured parameter tables (with fields such as fan ID, rated power, hub height), unstructured operation and maintenance case texts, and time-series meteorological and power generation data. Field units cover professional measurement standards such as meters, kilowatts, megawatts, and kilovolts.
What constraints these characteristics impose on model access and configuration
The multi-type and multi-update rhythm characteristics of wind power investment research data impose multiple constraints on model access and configuration.
First, the mixed document structure of structured parameters and unstructured text requires model access to adapt to multi-modal document parsing logic. This avoids information loss caused by splitting structured fields.
Second, data sources with large differences in update frequency need incremental synchronization trigger rules. These rules distinguish synchronization cycles between real-time operation and maintenance data and static industry reports.
Third, the existence of professional terms and multi-unit fields requires the model to have accurate professional vocabulary recognition ability. This avoids unit confusion and semantic deviation. The model must also support tool calls to pull real-time meteorological and grid connection data to supplement investment research information.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Wind power operation and maintenance cases and project feasibility study reports have relatively long single-piece length, complete context must be retained to associate multi-dimensional professional information |
parseSegmentSize | 1000–1500 characters | Avoid splitting structured parameter tables and causing field breaks, while adapting to semantic coherence of unstructured operation and maintenance texts |
similarityThreshold | 0.75–0.85 | Filter low-relevance general documents, retain retrieval results with matching degrees suitable for wind power professional scenarios |
recallCount | Top 8–12 entries | Cover key information from multiple data sources including fan parameters, operation and maintenance data, and industry reports |
rerankCount | Top 3–5 entries | Perform secondary screening on initial recall results, focus on wind power professional content most relevant to investment research needs |
toolCallEnabled | Enabled | Support real-time pulling of meteorological data and grid dispatch data to supplement real-time dynamic information required for wind power investment research |
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 specific analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Vector matching deviation occurs when calling the embedding model. The cause is failure to select an embedding model supplier adapted to energy and power professional terms, resulting in insufficient vector representation accuracy for wind power-specific vocabulary.
- The execution result of tool calls is directly displayed in the chat window. The cause is failure to turn off the tool call output transparency switch, causing the original results of the model calling external interfaces to be returned without filtering.
- Semantically generated SQL statements cannot match the exclusive fields of wind power data. The cause is failure to clearly mark the field names and units of wind power data in the system prompt, causing the model to generate query statements that do not conform to the data structure.
How to confirm the configuration is complete
- Upload a wind power technical document containing a structured parameter table. Check the parsed segment results, and adjust the segment length until no field splitting breaks occur.
- Initiate a retrieval request containing wind power professional terms. Verify the quantity and relevance of recall results, and adjust the similarity threshold and number of recalled entries to meet investment research needs.
- Configure tool calls and initiate a real-time data pull request. Confirm that tool calls are triggered normally and results are correctly integrated into the response. Turn off the tool output transparency switch to verify the display logic.
- Test the semantic SQL generation function. Verify that the generated statements match the fields and units of wind power data, and adjust the prompt configuration to ensure accurate field recognition.
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.