What the data for this category looks like
Wind power investment research data sources include wind turbine real-time operation logs, regional meteorological observation data, grid dispatch instruction documents, equipment manufacturer technical manuals, industry research reports, and policy documents. Update rhythms vary significantly: real-time operation logs update minute-by-minute, equipment manuals and technical standards receive static updates, and industry research reports and policy documents are released irregularly. Document structures include structured time-series fields (such as turbine ID, operating power, wind speed, temperature, with units of number, kW, m/s, ℃ respectively), long analytical text, and tabular parameter tables.
What constraints do these characteristics impose on the vector models and indexing link?
The multi-source heterogeneous nature of wind power data requires vector models to support unified encoding of text, structured parameters, and time-series charts. This avoids retrieval bias caused by differences in data types. High-frequency updated real-time operation logs require incremental indexing strategies. These reduce resource consumption and time spent on full indexing. Long-text research reports and equipment manuals need reasonable chunking rules. This prevents splitting content that spans turbine IDs or key parameters. Unit information for structured fields must be included in vector encoding. This prevents reduced matching accuracy from missing unit data. Associated retrieval of multi-source data requires hybrid indexing. This supports both vector similarity and keyword matching logic.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the paragraph length of wind power industry research reports and equipment manuals, avoids splitting text blocks that span turbine IDs or key parameters |
chunk_overlap | 100–150 characters | Preserves contextual continuity for wind power time-series data and associated analysis, prevents loss of continuous equipment operation logic after segmentation |
embedding_model | multimodal-embedding-v1 or multi-modal models of the same dimension | Covers text, time-series charts, and structured parameter tables in wind power data, enables unified encoding of multi-modal information |
retrieve_top_k | Top 8–12 results | Balances multi-dimensional recall needs for equipment anomalies, meteorological correlations, and industry policies, avoids redundant or missing recall results |
SIMILARITY_THRESHOLD | 0.72–0.85 | Filters low-match irrelevant wind power data, ensures accuracy of investment research retrieval |
index_refresh_strategy | Incremental refresh | Adapts to the high-frequency update rhythm of wind power real-time operation data, reduces resource consumption of full indexing |
The parameter values provided on this page are conventional recommendations for establishing configuration starting points. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common errors
- Phenomenon: Calling the chunk indexing API of FastGPT 4.8.10 returns empty fields. Cause: The API exposure configuration for chunk indexing is not enabled, or chunk parameters do not correctly match the length characteristics of wind power documents, resulting in no index being generated.
- Phenomenon: Multiple duplicate entries for the same wind power document appear in retrieval results. Cause: The chunk overlap parameter is set too large, or document-level deduplication rules are not enabled, resulting in multiple redundant index blocks generated for the same content.
- Phenomenon: Documents remain in the indexing state for a long time with no progress updates. Cause: Incremental indexing refresh strategy is not configured, and full indexing is performed on high-frequency updated wind power real-time operation logs, leading to system resource exhaustion and blocking of the indexing process.
How to confirm the configuration is properly set
- Review the vector model configuration page. Confirm that
multimodal-embedding-v1or the corresponding multi-modal model has been added, and the model status displays as connected. - Perform a test index for a single wind power equipment manual. Check whether the chunking results are split according to the document structure, with no segmentation across key parameters.
- Submit a simulated investment research retrieval request. Verify whether the number and matching degree of recall results conform to the preset threshold rules.
- Check the index monitoring panel. Confirm that the incremental index refresh frequency matches the update rhythm of real-time operation data.
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.