What the Data for This Category Looks Like
Data sources for wind power marketing content include product technical manuals from wind power enterprises, winning bid project case documents, industry policy interpretation copy, social media marketing short copy, and bidding project document fragments.
Update frequency fluctuates with project milestones. Bulk new content is generated when new models are released, winning bid projects are publicized, or policies are updated.
Document lengths vary widely, from several hundred-word short marketing scripts to thousands-word feasibility study reports and project plans.
Fields include professional parameters such as installed capacity (unit: MW), tower height (unit: meters), blade length (unit: meters). They also include marketing tags such as "offshore wind power" and "grid parity project".
Constraints for Vector Models and Indexing
The wide range of wind power marketing content creates segmentation constraints: long documents must retain semantic units to avoid splitting professional parameters.
A large number of professional terms requires vector models to have domain adaptation capabilities. General-purpose base models may fail to encode accurately.
Fluctuating update frequencies require indexes to support incremental updates, avoiding resource consumption from full reconstruction.
The mixed short and long text scenario requires indexes to efficiently retrieve texts of multiple lengths. This balances accurate matching of short copy and full coverage of long documents.
Some marketing content mixes structured parameters and unstructured copy. Indexes must support multi-field associated retrieval to ensure matching between parameters and marketing scenarios.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Wind power marketing content includes professional parameters and complete marketing scripts. This length range preserves single-category business semantic units and avoids splitting key information such as "10MW single-unit capacity". |
chunkOverlap | 50–100 characters | Compensates for semantic gaps in long segments, ensures professional terms across segments are fully covered, and improves retrieval accuracy. |
vectorModel | General-purpose vector models with professional domain fine-tuning, or industry-specific models | Wind power fields contain a large number of professional terms. General-purpose base models may fail to encode accurately, so matching the professional requirements of the business is necessary. |
batchSize | 16–32 | Adapts to the batch processing capabilities of vectorization services, reduces single request latency, and avoids timeouts during the vectorization phase. |
recallTopK | 10–15 | Wind power marketing content has relatively high business relevance. An appropriate number of recalls can cover relevant project and product information. |
similarityThreshold | 0.75–0.85 | Filters low-relevance retrieval results, retaining content that highly matches wind power marketing 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. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: Vectorization requests return timeouts or batch processing failures. Logs show excessive latency for single text slice requests. Cause: The
batchSizeparameter is not configured. The default single-text vectorization mode is used, failing to adapt to the batch processing capabilities of the vectorization service. - Phenomenon: Unable to add the
multimodal-embedding-v1vector model in FastGPT. Cause: Correct API access keys and service endpoints are not filled in the model configuration page, or the support switch for multimodal vector calculation is not enabled. - Phenomenon: The chunk indexing API returns empty results or a 400 status code in FastGPT version 4.8.10. Cause: The
indexNameparameter for chunk indexing is not set correctly, or chunk retrieval requests are not initiated via the/api/v1/index/retrieveinterface.
How to Verify Proper Configuration
- Upload a wind power product manual, review the segmented text fragments, and confirm that segment length matches the preset
chunkSizeandchunkOverlapparameters, with no key professional terms split. - Initiate a vectorization test request, check that the batch parameters of the request match the configured
batchSize, and confirm the vectorization service received the batch text slices. - Enter a wind power marketing-related query, such as "10MW offshore wind power project case", review the number of retrieval results and similarity scores, and confirm they match the
recallTopKandsimilarityThresholdconfigurations. - Call the chunk indexing retrieval API, enter a test query, and confirm that the returned chunked content contains correct wind power professional parameters and marketing copy.
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.