What the Data for This Category Looks Like
Jewelry category financial report data mainly comes from periodic regular reports of listed companies publicly disclosed by domestic and overseas stock exchanges, as well as segmented category operation data released by industry associations. Update cadence follows quarterly, semi-annual, and annual regular disclosure rules. Monthly terminal sales data of some leading brands is non-public information. Document structure includes fields such as revenue breakdown (by jewelry material and sales channel), inventory scale, store operation data, profit-related details, etc. Some field units are ten thousand yuan, square meters, quantity, etc. A single annual report document is usually dozens of pages long.
What Constraints These Characteristics Bring to Multi-turn Dialogue and Prompting
Data for jewelry category financial reports has scattered sources, including publicly disclosed periodic reports and non-public terminal operation data. Explicit specification of retrieval document type and scope during multi-turn dialogue prevents the recall of irrelevant content. Update cadence varies widely across different cycles. Multi-turn dialogue must guide users to clearly specify the report cycle currently being analyzed to avoid mixing quarterly and annual data. Field units include multiple types. Prompt templates must unify field unit definitions in advance to prevent the large model from misunderstanding the meaning of numerical values. Single annual report documents have relatively long lengths. The context window for multi-turn dialogue must support segmented retrieval of split long documents to avoid missing key information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Adapts to the relatively long length of single jewelry financial report documents, and accommodates multi-turn dialogue history and recalled segmented content |
recall_top_k | Top 8–12 entries | Covers multi-dimensional segmented fields such as revenue, inventory, and store operations in jewelry financial reports, avoiding missing key information |
similarity_threshold | 0.75–0.85 | Balances recall precision and coverage, filtering industry-general data unrelated to the target jewelry enterprise |
prompt_template | Answer solely based on the provided target jewelry enterprise financial report documents, and clearly mark the cited report cycle | Explicitly constrains the large model’s retrieval scope to avoid mixing financial report data from different enterprises or cycles |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapts to the length of single annual reports, ensuring completion of long document parsing |
enable_rerank | Enabled | Reranks recalled segmented content, prioritizing returning highly matching fragments relevant to jewelry financial report analysis |
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 case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: The large model recalls documents containing financial reports of non-target jewelry enterprises. Cause: The prompt template fails to explicitly specify that only documents of the target enterprise should be retrieved, and the document grouping function is not used to limit the recall scope.
- Phenomenon: The open-source version V4.8.22 cannot configure custom prompt templates and citation templates. Cause: This version does not have the advanced configuration module enabled, and only supports basic parameter adjustments. Upgrading to a compatible version or passing custom prompt content via the API is required.
- Phenomenon: The chatId parameter corresponding identifier is not displayed in the conversation log. Cause: The chatId field is not correctly included in the API request, or the chatId log recording function is not enabled in the background, resulting in the parameter not being properly captured.
How to Confirm Proper Configuration
- Initiate a test dialogue, enter the specified query content, and verify that the recalled content only includes the corresponding reports of the target jewelry enterprise.
- Check the parameter settings in the configuration interface to confirm that the values of all core parameters meet current analysis requirements.
- Initiate a multi-turn dialogue, and verify that subsequent conversations can reference the analysis results of the previous round as context basis.
- Call the API with the chatId parameter included, check the background conversation log, and confirm that the parameter has been correctly recorded.
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.