What the Data for This Category Looks Like
Energy storage research report data primarily comes from public research reports and industry white papers published by securities research institutes, power equipment industry associations, and energy storage system integrators. Update cycles cover multiple timeframes: daily post-market industry trend data, monthly updated installed capacity statistics, quarterly disclosed corporate performance research reports, and annual released industry chain panoramic reports. Document structures include sections such as cover pages, core data tables, policy interpretations, industry chain breakdowns, and investment ratings. Fields include energy storage system shipment volume (unit: GW), cycle life (unit: cycles), levelized cost of energy (unit: yuan/kWh), publishing institution and publishing date, etc. Some research reports include Excel-format attachments with segmented data.
What Constraints Do These Characteristics Impose on Workflow Orchestration
The multi-source update rhythm of energy storage research reports requires workflows to support a hybrid mode of scheduled triggers and manual triggers, to accommodate data pull tasks for daily post-market real-time data and quarterly periodic data respectively. The long document structure requires workflows to configure segmented parsing parameters to avoid single text segments exceeding the large model's context window limit. The specific units of fields require workflows to add parameter validation rules to ensure unified units for extracted energy storage professional data, and avoid unit confusion issues. Research report attachments in different formats (PDF, Excel) require workflows to configure multi-type file parsing nodes to adapt to content extraction from different format research reports. In addition, the large number of professional terms in research reports requires a pre-workflow term standardization step to ensure semantic consistency in subsequent question-and-answer sessions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
trigger_mode | Scheduled trigger + manual trigger dual mode | Energy storage research reports include both daily post-market updated industry trends and quarterly released performance reports. This dual mode covers data pull for data sources with different update frequencies |
parse_chunk_size | 800–1200 characters | Energy storage research reports contain a large number of professional data tables and long analysis sections. This range balances context window usage and information integrity |
rag_retrieve_topk | Top 6–8 entries | Core data of energy storage research reports is concentrated in three modules: industry chain, cost, and policy. Too many recalled entries will introduce irrelevant information, while too few will lead to incomplete coverage |
rag_similarity_threshold | 0.72–0.78 | There are many professional terms in the energy storage field. A threshold that is too low will introduce noise, while a threshold that is too high will fail to retrieve research report content for relevant sub-scenarios |
http_body_template | {"source": "${input_source}", "date": "${query_date}"} | Adapts to parameter transfer requirements for multi-source research report APIs, and directly embeds variables from external requests into the request body |
workflow_timeout | 300 seconds | Research report parsing and multi-source data pull require a certain amount of time. This duration covers the execution cycle of most conventional tasks |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Output results include the full content of a prior AI conversation. The symptom is that the final returned text contains duplicate dialogue snippets from the AI node. The cause is failing to only extract the core reply content of the previous node in the input parameters of the second AI node, and instead passing the full context.
- The code execution node returns a 500 status code. The symptom is that the workflow fails immediately when reaching the code node, with logs showing memory usage exceeds the limit. The cause is failing to properly truncate parsing segments for long research reports, causing the data processed by the node to exceed memory limits.
- Uploaded research report attachments are incorrectly identified as images. The symptom is that the file parsing node only returns image metadata and fails to extract research report text content. The cause is failing to configure file type filtering rules, causing attachments other than PDF (such as CSV-format industry data) to be misclassified as image files.
How to Verify Correct Configuration
- Manually trigger the workflow once, and verify that the output only includes the core reply content of the second AI node, with no redundant dialogue fragments from prior nodes.
- Upload an energy storage research report with more than 30 pages, and check that the segmented results from the parsing node fall within the preset 800–1200 character range.
- Configure an HTTP request node, pass the output variable from the upstream node as the body parameter, then run the workflow to check that the request log correctly includes the variable values.
- Run the workflow that includes the code execution node, and check that the run logs contain no 500 status code or timeout errors.
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.