What the data for this category looks like
The data for consumer electronics intelligent due diligence reports comes from four main sources: supply chain BOM systems, channel SKU management libraries, third-party quality inspection reports, and after-sales repair databases. Update cycles align with new product iterations. Full data updates are completed before new product launches. Routine SKU data is synchronized once per quarter. Documents are a mix of structured tables and parameter documentation. Core fields include material codes, batch numbers, quality inspection compliance records, repair statistics, and recommended retail prices. Units include sets, pieces, individual components, and other component-level units.
Constraints on multi-turn dialogue and prompt engineering
Fast data updates and frequent new product launches for consumer electronics require real-time retrieval of the latest SKU data during multi-turn dialogue. This prevents the use of outdated information. Structured fields and mixed document formats mean prompts must clearly define separate extraction rules for table parameters and text descriptions. This avoids field confusion. Cross-system associated data, such as BOM tables and after-sales repair records, requires multi-turn dialogue to guide users to gradually add query conditions based on associated fields. This reduces errors from vague searches.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
RECALL_TOP_K | Top 10-15 entries | Consumer electronics have a large number of SKU data entries. Too many recalled entries increase context burden, while too few will miss associated parameters |
SIMILARITY_THRESHOLD | 0.72-0.80 | Consumer electronics have many fields with similar names, such as "retail price" and "supply price". A higher threshold is needed to avoid false recalls |
PARSE_TABLE_STRICT_MODE | Enabled | Most consumer electronics data is stored in structured tables. Strict mode preserves field integrity and prevents parsing misalignment |
maxContext | 4096-8192 characters | Multi-turn dialogue needs to associate three types of data: BOM, quality inspection, and after-sales. A sufficient context window is required to carry associated information |
PROMPT_TEMPLATE | Gradually guide users to supplement associated fields based on their queries, prioritize extracting structured parameters | Consumer electronics due diligence requires multi-dimensional data association. Gradual guidance reduces information omissions |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Large BOM tables take longer to parse. Extend the timeout to avoid interruptions |
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 case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Errors
- Symptom: Empty parsed results are returned for uploaded consumer electronics BOM tables during dialogue, but the knowledge base backend upload verification passes normally. Cause:
PARSE_TABLE_STRICT_MODEis not enabled, causing structured tables in mixed documents to be misclassified as plain text. - Symptom: Multi-turn dialogue occasionally returns empty retrieval results, but target content can be retrieved via separate testing in the knowledge base. Cause:
SIMILARITY_THRESHOLDis set to an overly high range, and noRE_RANK_TOP_Kre-ranking mechanism is configured, causing valid recalls of similar fields to be filtered out. - Symptom: Vector index construction progress stalls with no updates for an extended period. Cause: No dedicated metadata index is set for consumer electronics SKU fields, and shard processing is not enabled for vector generation of large-volume BOM tables.
How to Verify Proper Configuration
- Upload a standard consumer electronics BOM table, check if the parsed result fully retains core fields, and confirm that
PARSE_TABLE_STRICT_MODEis enabled. - Initiate a multi-turn dialogue, query SKU parameters, quality inspection data, and after-sales repair status in sequence, check if the system can gradually guide users to supplement associated query conditions, and verify the guidance logic of
PROMPT_TEMPLATE. - Adjust
SIMILARITY_THRESHOLDto a reasonable range, initiate a query containing similar fields, check the accuracy of retrieved results, and confirm that the threshold setting matches the current data characteristics. - Upload a single large-volume BOM table, check if parsing completes within the preset timeout period, and confirm that the timeout setting matches the file size.
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.