What the data for this category looks like
Jewelry investment research data mainly comes from brand product manuals, publicly available industry exhibition materials, third-party quality inspection reports, supplier quotation sheets, and product photos. Update frequency adjusts with new product launches and seasonal promotions, with no fixed cycle. Document structures include structured Excel quotation tables, detailed PDF files for individual products, industry research report documents, and annotated product images. Fields cover materials such as K gold, silver, alloy, gram weight, dimensions including diameter and perimeter, design elements, origin, retail price, wholesale price, and more. Most fields have clear attached units.
Constraints for multi-turn dialogue and prompt engineering
The multi-dimensional, unit-tagged fields in jewelry investment research data require multi-turn dialogue to retain product style numbers, materials and other identifiers mentioned in context. This prevents parameter confusion across different products. Scenarios with mixed image and text inputs require the system to support both visual recognition and structured text extraction. Prompts must clearly define processing logic for each input type. Frequently updated new product data requires multi-turn dialogue knowledge base recall to target only recently updated content. This avoids using outdated information. The need for consistent professional terminology such as setting processes and quality grades requires prompts to include pre-set terminology explanation rules. This ensures consistent responses.
Configuration setup
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Jewelry investment research data includes multi-field parameters and long-text research reports. Retaining key information such as product style numbers and materials in multi-turn dialogue prevents context overflow |
enable_multi_modal | Enabled | Jewelry investment research often includes product photos and design drafts. The system must handle both text files and image inputs |
recall_top_k | Top 6–8 results | Jewelry products have multiple parameter dimensions. Sufficient relevant field information must be recalled to support accurate answers in multi-turn dialogue |
system_prompt | Permanently retain product style numbers and SKUs mentioned in context, standardize unit expressions (for example, use "gram" for gram weight instead of other abbreviations) | Most jewelry fields have attached units. Unit confusion and product misalignment must be avoided during multi-turn dialogue |
file_parse_chunk_size | 800–1000 characters | Jewelry quotation sheets and research reports are mostly structured text. Chunk length adapts to the completeness of field extraction |
available_models | Include GPT series and GLM series models | Must cover different model requirements for text processing and multimodal processing, adapting to calls for different scenarios |
The parameter values listed on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Each situation requires separate analysis. It is recommended to test on your own samples before finalizing settings.
Three common misconfigurations
- Phenomenon: Product style numbers mentioned earlier are lost after multi-turn dialogue, and parameter misalignment appears in responses. Cause: The requirement to retain context-based product identifiers is not clearly specified in
system_prompt. Key information is lost after the context window is truncated. - Phenomenon: After uploading both product images and quotation files, only text content is extracted, and product details in images are not recognized. Cause: The
enable_multi_modalconfiguration is not enabled, and multimodal processing capabilities are not activated. - Phenomenon: Only GPT series models appear in the model selection interface, and connected GLM models cannot be selected. Cause: GLM model interface information is not added to the
available_modelsconfiguration, and model mapping configuration is not completed correctly.
How to verify proper configuration
- Launch a multi-turn dialogue that includes product style numbers, materials and gram weight. Verify that the system correctly associates previously mentioned product information in follow-up questions.
- Upload both jewelry product photos and structured quotation files at the same time. Verify that the system extracts both design details from images and price and gram weight data from files.
- Enter the model configuration interface. Confirm that connected GLM models appear in the optional list and can be selected and called normally.
- Launch a workflow test. Verify that intermediate step processing results are not displayed in the final conversation interface, and only the output of the final model is returned.
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.