What this category of data looks like
Photovoltaic investment research data originates from three main channels.
- Public specification documents from photovoltaic module manufacturers. These are mostly structured tables, with fields including installed capacity, power generation efficiency, service life and more. Common units are kW, %, and years.
- Real-time operational monitoring data from grid-connected power stations. This is time-series data updated hourly, with metrics including solar irradiance, power output, equipment temperature and more.
- Industry research reports and energy policy documents. These are semi-structured or unstructured text, with update frequencies ranging from real-time reports to quarterly policy updates.
Constraints for Retrieval and Recall
Multiple data types in photovoltaic investment research create several constraints for retrieval and recall workflows. Structured time-series operational data requires precise retrieval filtered by time ranges and indicator dimensions. General text matching cannot support this use case. Real-time power station data requires the recall pipeline to support incremental synchronization. This prevents returning outdated information. A mix of long-text research reports and short table specification documents needs adaptable segmentation rules. This ensures key parameters are not truncated during processing. Highly specific professional terms such as "solar irradiance" and "module efficiency" require the recall stage to prioritize domain term matching. This improves retrieval relevance.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
retrievalCount | Top 10-15 results | Photovoltaic data contains a large number of professional parameters and long-text research reports. Too many recall results will exceed context limits, while too few will fail to cover key information. The 10-15 range balances relevance and context capacity. |
similarityThreshold | 0.72-0.80 | Professional terms in the photovoltaic field have high specificity. A low threshold will introduce irrelevant results, while a high threshold will miss relevant detailed parameters. This range adapts to matching accuracy for professional scenarios. |
chunkSize | 800-1200 characters | Most photovoltaic research reports are long texts, and module parameter tables require complete row content to be retained. This length balances long-text segmentation and table field integrity. |
chunkOverlap | 100-150 characters | Professional photovoltaic terms often appear across segment boundaries. This overlap length ensures terms are not truncated at segment breaks. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large XLSX files from photovoltaic power station operational data have significant file size. Parsing requires extended time, and this duration prevents parsing failures for large files. |
rerankTopN | Top 5 results | The reranking stage should focus on the most relevant results. The professional relevance of photovoltaic data requires prioritizing the top 5 reranked results to improve final retrieval accuracy. |
The parameter values provided on this page are common starting points 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 Misconfigurations
- Symptom: Knowledge base retrieval deployed on a public cloud platform experiences response timeout, and the interface returns a
504 Gateway Timeoutstatus code. Cause: Incremental synchronization recall rules are not configured for real-time power station data. Full historical time-series data is loaded for each retrieval, leading to excessive pipeline load. - Symptom: After importing an online spreadsheet from a collaborative office platform, the 10th row content of the XLSX table cannot be extracted in retrieval results, and the corresponding field is empty. Cause: Line-by-line parsing configuration for structured documents is not enabled. Only table header information is extracted.
- Symptom: When retrieving photovoltaic module parameters, a large number of irrelevant electronic component documents are returned, with significant relevance deviation. Cause: The similarity threshold is set below 0.7, and non-professional domain matching results are not filtered.
How to Verify Proper Configuration
- Upload a structured document containing module parameters, review the parsed segmented content, and confirm that the segment length and overlap configurations take effect.
- Initiate a professional term retrieval, check the number of returned results, and adjust the recall count configuration to fit the business scenario range.
- View the knowledge base parsing logs, and confirm that no timeout errors occur when parsing large XLSX files.
- After importing an online spreadsheet from a collaborative office platform, retrieve the specified row data in the table, and confirm that the target field content can be read normally.
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.