What the Data for This Category Looks Like
Jewelry investment research data sources include category monitoring reports released by industry associations, official new product launch announcements from brands, daily price lists from upstream precious metal and alloy suppliers, SKU sales and review data from e-commerce platforms, and design patent documents from the National Intellectual Property Administration. Update frequency varies significantly by content type: new product announcements update randomly per brand launch schedules, raw material prices update daily, and industry reports are released on a monthly or quarterly basis. Document structures include structured parameter tables, long-form market analysis, and supply chain flow details. Fields include weight, purity, SKU code, and listing period. Units correspond to grams, percentage, pieces, and yuan.
Constraints on Context and Token Handling
The high-frequency update characteristic of jewelry investment research data requires frequent refreshing of context slices, increasing token consumption per conversation session. Multiple structured fields and differences across segmented jewelry categories demand precise matching of context content to avoid irrelevant information occupying tokens. A high share of documents combine text and images, so text extracted via image OCR will add extra token load. Long-form analysis documents that are not properly segmented will exceed the model's token limit, resulting in information truncation. Additionally, parameter differences across different jewelry categories are significant. Context for the corresponding category must be retrieved specifically; otherwise, invalid tokens will be introduced, reducing query efficiency.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Jewelry investment research data includes multi-category parameters and daily updated raw material prices. This range can cover valid information from 3 to 5 core reports for a single category, avoiding exceeding the model's token limit |
chunkSize | 1000–1500 characters | Jewelry investment research documents have both structured parameter tables and long-form analysis. This segment length balances token consumption and information integrity, avoiding redundant recall caused by overly long single segments |
recallTopK | Top 3–5 results | There are many segmented jewelry categories. Precise retrieval of context for the corresponding category reduces irrelevant token occupancy, while covering relevant associated information for core competing products and raw material data |
similarityThreshold | 0.75–0.85 | Jewelry has many detailed parameter fields. A higher threshold can filter non-matching SKU and raw material data, reducing invalid token consumption |
maxTokenPerQuery | 15000–20000 | A single round of investment research query may need to combine raw material prices, new product data, and historical sales data. This range can carry the total token count of multi-source context, avoiding truncation |
PARSE_IMAGE_OCR_ENABLE | Enabled | Jewelry investment research documents contain a large number of design drawings and patent drawings. OCR-extracted design descriptions can supplement context information, and OCR token consumption needs to be controlled reasonably |
The parameter values provided on this page are all conventional recommendations used to determine the starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: When calling the API to initiate an investment research query, the returned results do not include historical context information. Cause: The
chatIdorcontextIdfield was not correctly carried in the API request parameters, resulting in failure to associate the conversation context. - Phenomenon: The OCR text output of jewelry design images in the knowledge base is truncated. Cause: The
maxTokenPerChunkparameter was not adjusted, and the token consumption of single-segment image OCR exceeded the default limit, resulting in partial text being discarded. - Phenomenon: The token consumption of a single round of investment research query exceeds the model's limit, triggering a request error. Cause: Reasonable
recallTopKandmaxContextparameters were not set, and too many unnecessary jewelry parameters and industry report content were retrieved, resulting in token overload.
How to Confirm Proper Configuration
- Initiate a single-category jewelry investment research query, check whether the context information included in the returned results matches the preset
maxContextrange, with no obvious truncation. - Upload a jewelry patent document containing design drawings, check whether the OCR-extracted text is complete with no missing content.
- Simulate a high-traffic query scenario, observe the token consumption data returned by the API, and confirm that the
maxTokenPerQuerylimit error is not triggered. - Adjust the
similarityThresholdparameter, verify whether the retrieved context content highly matches the queried jewelry category, with no irrelevant data mixed in.
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.