What the data for this category looks like
White goods smart due diligence data comes from official brand product parameter manuals, national energy efficiency label databases, and fault feedback records from home appliance maintenance platforms. Updates to stock model parameters sync with new product launches, and after-sales data for standard models is updated quarterly. Most documents combine structured parameter tables with long-form explanatory text, with fields including model number, energy efficiency rating, rated power, dimensions, warranty period, and common fault codes. Some models include PDF attachments of disassembly reports.
What constraints these characteristics impose on tool calling and plugins
The coexistence of structured parameters and long-form explanatory text requires tool calling to prioritize matching standardized fields, to avoid parameter misalignment caused by fuzzy retrieval. Differences in data formats across multiple sources require plugins to support parsing of structured tables and PDF text, and to configure unified field mapping rules. The dynamic update schedule tied to new product launches requires tool calling’s data source pulling to bind to brand new product launch events; fixed periodic triggers cannot adapt to real-time update needs. PDF attachments of disassembly reports included with some models require plugins to support batch parsing of multi-format files, to avoid missing key maintenance data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 120–180 seconds | Disassembly report PDFs for white goods typically have a high page count and long parsing times, so this range accommodates long document parsing needs |
maxChunkSize | 800–1200 characters | White goods product documentation contains long paragraphs of technical parameters. This chunk size preserves the integrity of parameter groups and avoids semantic breaks |
similarity_threshold | 0.75–0.85 | White goods model names have similar naming conventions. This threshold filters low-match results while retaining accurate model parameters |
RECALL_TOP_K | Top 8 results | White goods have a large number of parameter fields. Recalling 8 results covers retrieval needs for core parameters and common faults |
tool_call_max_history | Top 6 entries | Due diligence reports require association with multi-dimensional data. Retaining 6 context entries covers associated information about brands, models, and after-sales service |
plugin_node_context_count | 6 entries | Text content extraction plugins need to associate model information from chat history. 6 context entries covers current and historical product query logic |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Calling the
/api/v1/chat/completionsAPI returns a400 Bad Requesterror. Local debugging works normally but deployment fails online. Cause: No dedicated data source domain whitelist for white goods is configured, so the online environment cannot access official brand parameter libraries. - In multi-turn conversations, subsequent questions cannot reuse previously selected white goods model parameters. Cause: The number of retained entries for
tool_call_max_historyis not configured, leading to truncated context and loss of the previously selected model identifier. - The text content extraction plugin returns empty fields and fails to extract rated power parameters. Cause:
maxChunkSizeis not set to accommodate white goods long text segmentation, resulting in truncation of the paragraph containing the parameter, so the plugin cannot match the corresponding field.
How to Verify Proper Configuration
- Upload a white goods disassembly report PDF, check that the parsed text chunks retain complete parameter groups without obvious truncation.
- Initiate two or more due diligence queries, verify that subsequent questions can correctly associate the previously selected home appliance model parameters.
- Call the tool API, check that the returned parameter fields include white goods-specific fields such as model number, energy efficiency rating, and rated power.
- Adjust the similarity matching threshold, verify that the matching accuracy of retrieval results meets business requirements.
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.