What the data for this category looks like
Data for wind power intelligent due diligence reports comes from real-time wind turbine operation logs, regular inspection reports for towers and blades, grid connection dispatch records, on-site operation and maintenance inspection records, and meteorological observation datasets. Update frequencies vary: operation logs are updated hourly, regular inspection reports are updated quarterly or annually, and operation and maintenance records are submitted in real time as on-site work is completed. Document structures include unique device identifiers, operation parameters, defect detection items, operation and maintenance rectification records, and timestamp fields. Wind speed is measured in meters per second, power is measured in kilowatts, and defect levels are identified using standardized numbering.
What constraints do these characteristics impose on the vector models and indexing workflow
The multi-source heterogeneous data characteristics of wind power due diligence reports require vector models to adapt to semantic encoding of professional domain terms, to avoid semantic deviation for exclusive terms such as "tower yaw" and "blade flutter". Data with different update frequencies requires differentiated indexing strategies: real-time operation logs need to support incremental index construction, while regular inspection reports can use full batch indexing. Mixed structured and unstructured content across multiple fields requires indexes to support associating paragraphs by device ID, while balancing semantic integrity between long document splitting and short parameter records.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Wind power due diligence reports contain both long sections of inspection analysis text and short single-item operation parameter records. This length balances semantic integrity and recall accuracy |
chunk_overlap | 150–200 characters | Wind turbine equipment parameters have time-series correlations. The overlapping portion preserves context coherence and avoids breaking parameter context |
vector_model | Calibrated via actual testing | Wind power scenarios contain a large number of professional terms. A vector model fine-tuned for the power equipment domain must be selected to avoid semantic deviation from general-purpose models |
index_shard_count | 4–8 shards | The volume of single-batch imported data for wind power due diligence reports is large. Sharding improves index construction speed and concurrent query capability |
similarity_threshold | 0.72–0.85 | Semantic similarity differentiation for wind power professional terms is high. This range filters irrelevant recall results and retains highly matched professional content |
recall_top_k | Top 10–15 results | Due diligence reports need to cover multi-dimensional equipment data. Excessive recall increases context redundancy, while insufficient recall fails to cover complete business requirements |
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 specific analysis, and it is recommended to confirm via actual testing on your own samples before finalizing.
Three common mistakes
- Issue: After a custom-split wind power due diligence report document is stored in the knowledge base, duplicate content is automatically removed, and the original split paragraph order does not match the index storage order. Cause: The knowledge base's document deduplication configuration is enabled by default, and this function is not disabled for multi-source associated duplicate parameter records in wind power scenarios.
- Issue: After a custom vector model is configured, actual vector generation requests still point to the default large language model. Cause: The call parameters for the vector model are not separately bound in the channel configuration, causing requests to be uniformly forwarded to the LLM channel.
- Issue: After the similarity threshold is adjusted to 10000+, no matching results are returned for knowledge base queries. Cause: The valid value range for the vector similarity threshold is 0–1. The scaled value was directly entered, causing the filter condition to strictly exceed the reasonable range.
How to confirm the configuration is correct
- Upload a single wind power due diligence report document, check that the number of split paragraphs matches the custom split rules, to confirm the split configuration is effective.
- Initiate a vector generation test, check that the vector model endpoint in the call logs matches the configured custom channel, to confirm the model binding is correct.
- Batch import similar wind power due diligence reports, check that the index construction progress matches the number of shards, to confirm the index sharding configuration is reasonable.
- Enter a wind power professional term to initiate a query, check that the similarity value of the returned results falls within the 0–1 range, to confirm the threshold configuration is valid.
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.