What the data for this category looks like
Data for automotive service research reports primarily comes from national automotive industry associations, passenger car industry bodies, public annual reports of leading automakers, and public reports from third-party automotive industry consulting institutions. Update frequency falls into three categories: monthly retail sales data is updated on a fixed monthly cycle; quarterly after-sales operation data is updated in the month following each quarter; special trend research reports have no fixed release cycle. Each individual document includes five sections: core data summary, vehicle-by-vehicle analysis, regional market data, policy impact interpretation, and trend forecasting. Core fields include monthly retail volume, terminal transaction price, after-sales customer unit price, with corresponding units of vehicles, yuan per vehicle, and yuan per service, respectively.
What constraints these characteristics impose on tool calling and plugins
Decentralized data sources with significant format differences require tool calling to support multi-source data format adaptation and unified mapping. Unfixed update cycles and partial data lag require plugins to support incremental pulling and scheduled triggering mechanisms to avoid resource waste caused by full pulling. Document structures include structured tables and unstructured text, requiring tools to support mixed-format parsing and adaptive chunking for long documents. Field units have subtle differences, requiring unit unified conversion before tool calling to avoid data calculation errors.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
plugin_data_source_timeout | 300 seconds | The multi-source data interfaces for automotive service research reports have long response cycles; 300 seconds covers the return time of most data sources |
rag_chunk_size | 800–1200 characters | Automotive service research reports contain a large number of professional technical parameters and table fragments; this chunk length preserves complete parameter context |
plugin_max_retries | 3 times | Some third-party consulting institution interfaces have temporary current limiting; retrying 3 times reduces call failure rates |
tool_call_field_mapping | Configure according to the data source preset mapping table | Field naming differs across data sources; for example, the "retail volume" from passenger car industry bodies corresponds to "delivery volume" in automaker financial reports, requiring unified mapping in advance |
parse_file_max_length | 50 pages | Most individual automotive service research reports are 20–50 pages in length; documents exceeding this threshold require splitting to avoid parsing timeouts |
rag_similarity_threshold | 0.75–0.85 | Automotive research reports contain many professional terms; a threshold that is too low will introduce irrelevant results, while a threshold that is too high will miss relevant content; adjust based on actual testing. |
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 targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: Calling the research report retrieval plugin returns empty results, and the interface displays the
search_result_emptystatus code. Cause: Automatic switching logic for multiple data sources is not configured, and only a single data source is connected, resulting in no updated data during this period. - Phenomenon: A
request_timeouterror is triggered during tool calling, and the backend logs record interface response timeout. Cause: Theplugin_data_source_timeoutparameter is not adjusted, and the default threshold fails to cover the long response cycle of automotive industry data sources. - Phenomenon: The automotive market analysis chart generated by the chart tool has no data points, outputting an empty canvas. Cause: Field mapping rules are not configured, and naming differences across data sources are not unified, preventing the tool from extracting valid data.
How to confirm proper configuration
- Manually trigger a tool call for a single data source, check if the returned results include the core fields of automotive service research reports, and adjust field mapping rules based on the returned results.
- Configure a scheduled pulling task, check if the pulling logs update data according to the preset cycle, and verify that the timeout parameter adapts to the data source response speed.
- Call the chart tool to generate an analysis chart for a specified dimension, check if valid visual content is generated, and confirm that the field mapping and chunking parameters match the document structure.
- Simulate an abnormal call scenario, such as temporarily disconnecting the data source interface, check if the plugin executes retries according to the configured retry times, and verify the rationality of the retry parameter.
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.