What the data for this category looks like
Data sources for energy storage research reports include public documents from power equipment industry associations, detailed power equipment segment research reports from securities firms, and technical white papers and operating reports publicly released by energy storage enterprises. Updates align with industry events and periodic report launches, including regular quarterly reports and temporary documents for sudden policy updates and installed capacity data changes. Most documents combine structured parameter tables with paragraph analysis. Fields covered include energy storage system cycle times, rated capacity, conversion efficiency, unit cost, and more. Common units include watt-hours (Wh), gigawatts (GW), yuan per kilowatt-hour, and similar units.
What constraints do these characteristics impose on the vector models and indexing link
Energy storage research reports contain a large number of structured parameters and professional units. Vector models must adapt to mixed text and numerical features across multiple fields, to avoid vector deviations caused by unit differences. Unstructured analysis paragraphs and structured tables are mixed together. Segmentation rules must be configured to distinguish the vectorization logic for the two types of content. The update rhythm of industry research reports combines suddenness and periodicity. Indexes must support a hybrid mode of incremental updates and regular full refreshes, to prevent a disconnect between old data and new policies or installed capacity data. At the same time, a single research report has many parameter dimensions. When performing vector recall, priority must be given to matching the similarity of core business fields. Index weight configurations must be adjusted accordingly.
How to set the configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
embedding_model | Prioritize Doubao-embedding-v2 or general embedding models adapted for the power industry | Energy storage research reports contain specialized terminology and numerical units. Industry-adapted embedding models can improve the accuracy of vectors for semantic and numerical features |
chunk_size | 800–1200 characters | Energy storage research reports include both long technical analysis sections and compact parameter tables. This range balances segmentation integrity and contextual relevance |
chunk_overlap | 100–150 characters | Prevents core parameter combinations from being split after segmentation, and retains contextual connections between adjacent segments |
recall_top_k | Top 8–12 results | Energy storage research reports have many core parameter dimensions. A sufficient number of candidate segments must be recalled to cover all business fields |
similarity_threshold | 0.75–0.85 | Filters irrelevant research report segments with low similarity, while retaining subtle differences in similar parameters across different vendors |
index_incremental_refresh_interval | Calibrated based on actual testing | Adapts to the sudden update rhythm of energy storage industry research reports, avoiding index update delays or excessive resource usage |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: After switching the vector model, the index progress display stalls in the interface, and it is not possible to revert to the original model. Cause: The trigger logic for index reconstruction after model switching is not configured. The old index and the new model have mismatched vector dimensions, which prevents the system from loading or updating normally.
- Symptom: After configuring a custom embedding interface, the test returns "404 page not found". Cause: The interface address is configured incorrectly, or the corresponding call permission is not enabled, which prevents the platform from initiating requests normally.
- Symptom: The parameter-based recall results for energy storage research reports do not match expectations, and the similarity matching accuracy of core numerical fields is low. Cause: Segmentation rules are not adjusted for structured parameters in the research report, leading to loss of contextual connections after parameters are split.
How to confirm the configuration is complete
- Navigate to the vector model and indexing configuration page, verify that the selected
embedding_model, interface address and key information are filled correctly. - Upload a single sample energy storage research report, check whether the automatically segmented content retains the complete structure of the core parameter table, with no unnecessary splits.
- Initiate a retrieval test for energy storage research reports, confirm whether the number of recall results matches the configured recall quantity rules.
- Trigger an incremental index update, check whether the system operation log shows normal refresh progress with no error prompts.
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.