What this category of data looks like
Photovoltaic investment research data primarily comes from securities firm industry research reports, public reports from domestic photovoltaic industry associations, listed company annual and semi-annual reports, ground power station operation and maintenance logs, and component manufacturer technical white papers. Update cycles cover monthly research reports, quarterly industry updates, annual financial reports, and real-time operation data. Document structures include structured parameter tables, long-form technical analysis, and semi-structured operation and maintenance logs. Core fields include module conversion efficiency, system installed capacity, and annual utilization hours. Corresponding units are percentage, megawatt, and hour.
What constraints do these characteristics impose on the document parsing and chunking link
Structured parameter tables in photovoltaic investment research documents must fully retain fields and units. Chunking must not break the contextual association of parameters. Long documents such as large-scale power station feasibility study reports may exceed 100 pages. The parsing component must support batch parsing without losing chapter logic. Semi-structured real-time operation and maintenance logs require distinguishing timestamps and operation data. Chunking must not disrupt log timelines. Frequently updated industry data requires the parsing component to support batch uploads. This adapts to centralized processing needs for monthly research reports.
How to configure the settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxChunkSize | 800–1200 characters | Adapts to the typical length of technical parameter blocks and analysis paragraphs in photovoltaic documents |
chunkOverlapRatio | 10–15% | Retains parameter associations across segments, avoids splitting core fields such as component efficiency and installed capacity |
enableTableExtraction | Enabled | A large number of parameter tables in photovoltaic documents require structured extraction to prevent loss of core investment research data |
ocrStrategy | auto | Covers two types of document formats: electronic research reports and scanned power station reports |
parseTimeout | 600 seconds | Adapts to the full parsing time of 100+ page feasibility study reports |
chunkDepthLimit | Calibrated per document hierarchy | Adapts to the chapter nesting logic of photovoltaic documents, matches the paragraph depth configuration rules of version 4.9.10 |
The parameter values provided on this page are common recommended 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 configuration mistakes
- The symptom is missing parsed document content, with the
PARSE_CONTENT_TRUNCATEDerror code in logs. The cause is failure to adjust theparseTimeoutparameter. Large-scale power station feasibility study reports trigger a timeout before parsing completes. - The symptom is inability to select table datasets during knowledge base construction. The cause is failure to enable the
enableTableExtractionconfiguration item. Table content is not recognized as extractable structured data. - The symptom is disrupted chapter logic of investment research documents after configuring
chunkDepthLimit. The cause is failure to adjust chunk length based on the nesting hierarchy of photovoltaic documents. This causes long technical paragraphs to be split forcibly.
How to verify successful configuration
- Upload a single 10-page photovoltaic component technical white paper. Check if all parameter tables are fully extracted in the parsing results, and unit information is not lost.
- Adjust the
maxChunkSizeandchunkOverlapRatioparameters. Verify that the segmented results retain complete contextual association between component conversion efficiency and corresponding models. - Upload a scanned power station annual report. Check if OCR-recognized text can be chunked normally, with no garbled characters or missing fields.
- Check parsing logs to confirm no
PARSE_CONTENT_TRUNCATEDerror codes. Verify the integrity of large document parsing.
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.