What the Data for This Category Looks Like
Data for general equipment research reports primarily comes from securities firm machinery industry research reports, industry association monthly operation data, and listed companies’ regular announcements. Updates are adjusted to align with industry events. Regular tracking reports are published on a periodic basis, while in-depth reports are updated in line with earnings disclosure cycles. Document structures include overall industry overviews, segmented supply and demand data, and operating indicators for key enterprises. Fields include equipment output, revenue, production capacity, and unit cost, with corresponding units being units/sets, ten thousand yuan, sets, and yuan per unit.
Constraints on Workflow Orchestration Posed by These Characteristics
Data sources for general equipment research reports are scattered. This requires workflow configurations to include multi-source data access nodes that connect to securities firm report libraries, industry databases, and listed company announcement interfaces. Update rhythms fluctuate with industry events, so workflows must support on-demand triggering to adapt to temporary event-driven retrieval needs. A high share of long documents requires configuring segment parsing parameters to avoid overly long single segments that impair retrieval recall. The presence of multiple dimensions of operating fields requires adding field mapping nodes in the workflow to convert original report fields into a unified retrieval format.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Recall Count | Top 8-12 entries | General equipment research reports have many segmented dimensions. Too many recalled entries will increase context processing load, while too few will fail to cover key operating data |
| Segment Length | 800-1200 characters | Technical parameter paragraphs in general equipment research reports are relatively long. Too short segments will split data associations, while too long segments will exceed model context limits |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Single in-depth general equipment research reports contain many segmented data points, leading to longer parsing time. The default timeout duration is insufficient for complete parsing |
| Similarity Threshold | 0.72-0.78 | General equipment research reports are dense with industry terminology. A higher threshold is needed to filter irrelevant content while retaining associated data for segmented categories |
| Rerank Return Count | Top 4-6 entries | Core operating data in general equipment research reports is concentrated in a small number of paragraphs. Retaining a small number of highly relevant entries after reranking is sufficient to meet retrieval needs |
| Text Splicing Order | Reverse chronological order by publish time | Latest data from general equipment research reports has greater reference value. Reverse chronological arrangement prioritizes display of the most recent content |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- A
Cannot convert undefined or null to objecterror is thrown during workflow runtime. The cause is that no field mapping node is configured, causing empty fields in the research report to be passed directly to subsequent nodes and triggering a type conversion error. - AI conversation output is displayed directly on the page without passing through a text splicing component. The cause is that the native AI output node is not hidden, or spliced text is not bound to the custom reply output port.
- After knowledge base retrieval results are passed to an HTTP request, the AI conversation fails to correctly associate research report sources. The cause is that reference data is not passed in the format
{"title":"Research Report Title","content":"Retrieval Fragment","publishTime":"Publish Time"}, preventing the AI from recognizing source information.
How to Verify Proper Configuration
- Execute the workflow and review node logs to confirm multi-source data access nodes successfully retrieve general equipment research report data from corresponding sources.
- View segmented and parsed text fragments to confirm single segment length aligns with the range set by the Segment Length configuration parameter.
- Trigger a test retrieval and view the reference source fields of the AI conversation to confirm that the reference data format meets requirements.
- Modify the recall count configuration to verify that the number of retrieval results returned by the workflow matches the configured value.
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.