What the data for this category looks like
Computer equipment research report data primarily originates from official technical documents of hardware manufacturers, public reports from industry evaluation institutions, public procurement bidding announcements, and industry association standard documents. The update rhythm fluctuates with the hardware iteration cycle: update frequency increases during new product launch periods, and maintains stable updates during regular cycles. Document structures typically include four core content types: hardware core parameters, performance test indicators, applicable scenario descriptions, and compliance certification information. Fields include model, clock speed, video memory capacity, power consumption, interface type, and others, with corresponding units such as GHz, GB, W, units, and more.
What Constraints Do These Characteristics Impose on Workflow Orchestration?
The multi-source heterogeneous data of computer equipment research reports requires configuring multiple data source access nodes in workflow orchestration. These nodes adapt to various formats including PDF official documents, HTML evaluation reports, and Excel bidding data. The diverse field units require configuring unit normalization rules during the parameter parsing stage. These rules unify measurement standards for fields such as clock speed, video memory, and power consumption. The fluctuating update rhythm requires setting dynamic scheduling trigger rules. These rules increase data pull frequency during new product launch cycles. The high proportion of long document content requires configuring segment splitting parameters. These parameters adapt to the model context window limits of the retrieval link.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
multi_source_sync_interval | New product phase: 1 hour, Regular phase: 12 hours | Matches the fluctuating update rhythm of research reports, increasing synchronization frequency during new product phases to obtain the latest data |
doc_parse_segment_length | 800–1200 characters | Adapts to general context window limits while retaining the integrity of research report parameter paragraphs |
param_normalization_rule | Calibrated based on actual testing | Computer equipment research reports have diverse field units, requiring customized normalization rules for fields such as clock speed, video memory, and power consumption |
rag_recall_top_k | Top 8–12 entries | Computer equipment research reports have high precision requirements for parameters. Too many recalls will introduce irrelevant data, while too few will miss core parameters |
workflow_timeout | 600 seconds | The total time for multi-source data pulling and long document parsing is relatively long, preventing task premature termination |
plugin_input_schema | Configured as model, clock speed, video memory, power consumption | Matches the core query dimensions of user searches, standardizing plugin input parameters |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Issue: After adding a custom plugin to the workflow, the input and output parameter panel does not display, and no error logs are generated. Cause: The
plugin_input_schemaparameter is not correctly configured. Fields required for computer equipment research report retrieval, such as model and parameters, are not included in the plugin input rules. - Issue: Workflow execution fails in a private deployment environment, with a prompt indicating incorrect configuration file format. Cause: Data source access configuration items in
workflow_config.yamlwere not modified per official specifications. The system cannot properly load data source connection information. - Issue: After the workflow pulls research report data, the returned parameter field units are inconsistent and cannot be directly used for subsequent analysis. Cause: The
param_normalization_ruleparameter is not configured. Unit unification conversion is not performed for fields such as clock speed, video memory, and power consumption.
How to Confirm Proper Configuration
- Manually trigger the workflow once. Check if the custom plugin panel displays the preset input fields to confirm the
plugin_input_schemaconfiguration takes effect. - View the workflow run logs. Confirm that the multi-data source synchronization interval matches the current cycle settings, with no abnormal timeout errors.
- Randomly select three research report data samples from different sources. Verify that the parsed field units are unified to confirm the
param_normalization_ruleconfiguration is valid. - Adjust the
rag_recall_top_kparameter. Validate that the number of retrieval return results matches the configured value to confirm the retrieval logic works correctly.
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.