What the data for this category looks like
Auto parts research report data sources primarily include broker research institute industry reports, public data from domestic auto industry associations, supply chain disclosure documents from automakers, and reports from third-party supply chain consulting institutions. Update frequency fluctuates with industry events. Regular monthly tracking reports are updated monthly. Quarterly earnings companion research reports are released in concentrated batches. Temporary special reports are generated when sudden supply chain changes occur. Document structures include core business tables, supply chain association graphs, downstream supporting vehicle model data, and policy impact analysis. Fields cover component model, supplier name, supporting automaker, production capacity scale, gross profit margin, and more. Units include piece, set, ten thousand yuan, ten thousand units per year, and other specific types.
What constraints these characteristics impose on tool calling and plugins
Scattered data sources and large volumes of structured tables and segmented fields require tool calling to support multi-source API aggregation configuration, plus preset field mapping rules to adapt to unit differences across data sources. Sudden temporary content in research report updates requires plugins to support incremental pulling and real-time verification mechanisms, to avoid calling expired data. Hierarchical associations between component models and supporting information require tool calling to support nested parameter passing, to ensure accurate matching of upstream and downstream associated data during retrieval. Lengthy content in individual research reports requires plugins to support segmented retrieval and result merging, to avoid exceeding context window limits.
How to set configurations
| Config Item | Recommended Value | Rationale |
|---|---|---|
tool_call_max_retries | 2 retries | Auto parts research reports have many data fields. Single calls often fail due to incomplete parameters. 2 retries can cover temporary interface jitter and avoid excessive consumption of call quotas. |
structured_parse_threshold | 0.75 | Tables and text in research reports have high mixing rates. A threshold of 0.75 prioritizes recognition of structured table content and reduces invalid recall of unstructured text. |
plugin_timeout | 120 seconds | Some third-party supply chain data source interfaces have slow response times. 120 seconds covers the complete data pulling process and avoids early timeout leading to data truncation. |
context_window_size | 8000–12000 characters | Individual auto parts research reports have relatively long average lengths. This window can fully carry core retrieval content and tool return results. |
field_mapping_strategy | Preset rules per data source | Field naming varies across different data sources. Preset mappings unify output formats and avoid field conflicts in downstream processing. |
incremental_sync_interval | Every 6 hours | Supply chain changes in the auto parts industry occur relatively frequently. Pulling incremental data every 6 hours ensures the timeliness of retrieval content. |
The parameter values provided on this page are general recommendations for establishing configuration starting points. 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 mistakes
- Symptom: Tool calls return empty results, and interface logs show missing parameter fields. Cause: Parameter mapping rules were not configured in the workflow, causing retrieval parameters passed from the frontend to not be correctly transmitted to the plugin interface.
- Symptom: After calling a plugin bound with Python code, the workflow returns a
504status code with no valid results. Cause: No reasonable value was set for theplugin_timeoutparameter, causing long-running code execution to be terminated early. - Symptom: When calling across workflows, the target application's conversation logs cannot be obtained. Cause: The
log_sync_enabledconfiguration item for the workflow was not enabled, causing interactive data during the call to not be synchronized to the log storage.
How to confirm proper configuration
- Initiate a single tool call test, pass retrieval parameters that include specific auto part models, and check if the returned results include matching supplier, production capacity, and other data to confirm normal parameter transmission.
- Log in to the log management page, view the execution records of the corresponding workflow, confirm whether complete call links and returned data exist, and verify that the log synchronization configuration is effective.
- Call a plugin bound with Python code, wait for execution to complete, check the returned results, confirm that no
504timeout error occurs, and verify that the timeout configuration is reasonable. - Initiate a multi-data source joint retrieval test, check whether the field format of the returned results is unified, and confirm that the field mapping rules are effective.
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.