What the Data for This Category Looks Like
Photovoltaic intelligent due diligence report data comes from three main channels: record and acceptance documents for photovoltaic power station projects, operation and maintenance logs and grid-connected settlement data, and product parameter manuals from component and inverter manufacturers.
Data update cycles fall into three categories: record files are only updated when a project is approved or modified, operation and maintenance logs are synchronized daily based on the calendar day, and manufacturer product parameters are updated irregularly alongside model iterations.
Document structures include structured project logs, semi-structured due diligence PDF reports, and a small number of real-time monitored time-series data files. Field units follow industry standards: installed capacity uses megawatt-peak (MWp), power generation uses kilowatt-hours (kWh), and equipment operating duration uses hours.
Constraints Imposed on Knowledge Base Retrieval and Recall
The mixed structure and update differences of photovoltaic due diligence data create multiple constraints for retrieval and recall.
Mixed storage of structured logs and semi-structured PDFs requires the retrieval system to support both exact field matching and semantic recall, and adapt retrieval logic for both document types separately. Differences in update cycles across data sources require recall logic to prioritize recently updated data sources, avoiding outdated manufacturer parameter results.
The high token count of long time-series power generation documents requires limiting the segment length of single recalled documents, preventing exceeding the model context window. Unified industry field units require retrieval results to automatically align unit identifiers, avoiding content with inconsistent units.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall_count | top 3–5 results | Photovoltaic due diligence single documents have high token counts; excessive recall will exceed model context limits |
similarity_threshold | 0.72–0.85 | Photovoltaic professional terminology density is high; low-correlation fuzzy matching results must be filtered |
segment_length | 800–1200 characters | Photovoltaic due diligence documents contain long time-series data; overly long segments split semantic meaning, overly short segments lose contextual connections |
rerank_return_count | top 2–4 results | Core information must be retained while controlling the total token count of final output |
citation_limit | 1500–2000 characters | Must match the model's context window limit to avoid token overflow in single-round responses |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Large photovoltaic due diligence PDF files take longer to parse; sufficient parsing time must be reserved |
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 individual analysis, and it is recommended to test against your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: In version V4.8.10-fix2, knowledge base search results are directly output to the interface, and subsequent AI replies repeatedly display the retrieved content. Cause: The "directly return search results" configuration item was not disabled in the knowledge base search node of the workflow, causing both the search node and the AI generation node to output content.
- Phenomenon: After setting a fixed
citation_limit, the single-round response token count still exceeds the model limit. Cause: Total tokens were not calculated by combining the segment length of single documents and the recall count; only setting a fixed limit leads to total token overflow. - Phenomenon: Retrieval results return outdated manufacturer component parameters. Cause: No retrieval rules filtered by update time were configured, causing outdated document versions beyond the reasonable update cycle to be recalled.
How to Confirm Proper Configuration
- Upload a typical photovoltaic due diligence document, trigger knowledge base retrieval, and check whether the update time of returned results matches the preset time filtering rules.
- Adjust the
similarity_threshold, search for photovoltaic professional terminology, and check whether the relevance of returned results meets expectations. - Trigger a complete workflow call, view the returned token consumption, and adjust relevant configuration items to comply with the model's context window limits.
- Check the field units of retrieval results to confirm whether industry standard unit identifiers are automatically aligned.
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.