What the data for this category looks like
Jewelry financing daily report data comes from public trading data of domestic textile and apparel accessory trading markets, brand replenishment and payment collection records, and financing application and loan data from third-party supply chain finance platforms. Each daily report updates daily, and contains one or more jewelry financing entries. The document structure follows a fixed format: jewelry sub-category (such as hair accessories, brooches, belt decorations), raw material unit price, financing amount range, loan period, cooperating financial institutions, and regional market heat. Field units are: raw material unit price in yuan/gram, financing amount in ten thousand yuan, loan period in days, and regions as provinces or municipalities directly under the central government.
Constraints for Model Access and Configuration
The data draws from scattered sources, including structured trading fields and unstructured payment collection notes. This requires model access to support mixed multi-source data parsing and structured field mapping. The daily update frequency requires that vector models and large models meet real-time query response delay requirements, to avoid parsing timeouts that prevent timely synchronization of daily report data. Multiple sub-categories exist, and fields include range values, so configuration must accurately match jewelry-specific classification and numerical range fields, and cannot directly reuse general commodity financing field mapping rules. Dual-dimensional filtering requirements for region and category require the recall phase to initially filter by jewelry category fields to reduce invalid context input.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
ragRecallNum | Top 8-12 entries | Jewelry financing daily reports have few fields per entry. Too many recalled entries cause redundant context, too few fail to cover complete regional and category information |
embeddingModel | text-embedding-3-small or locally deployed bge-large-zh-v1.5 | Most fields in jewelry financing daily reports are structured text; these models meet accuracy requirements for vector representation of structured data |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Single monthly jewelry financing daily report has large parsing data volume; sufficient parsing time must be reserved to avoid timeouts |
rerankTopN | Top 4-6 entries | Jewelry financing daily reports have many regional and category dimensions; reranking filters redundant data from non-target categories |
systemPrompt | Only answer questions related to jewelry categories, financing amounts, and loan periods based on the provided jewelry financing daily report data | Limit model output scope to avoid generating irrelevant cross-category content |
localEmbeddingPath | /data/fastgpt/embedding_models/bge-large-zh-v1.5 | Locally deployed embedding models must match the text length of jewelry financing daily reports; this model has high adaptability |
The parameter values provided on this page are all 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 Configuration Mistakes
- Phenomenon: Local embedding model fails to load, and the console returns
500 Internal Server Error. Cause: The correct path of thelocalEmbeddingPathparameter is not configured inconfig.yaml, or the local model access switch is not enabled when the version is 4.9.9 or higher. - Phenomenon: Non-jewelry category financing data is mixed in recall results, and the number of results exceeds expectations. Cause: Recall filtering rules based on jewelry sub-categories are not configured, and general category recall parameters are directly reused.
- Phenomenon: Multi-turn dialogues fail to remember jewelry category keywords mentioned in the previous turn. Cause: The
maxContextparameter is not adjusted to cover the previous session context, or the system prompt does not require retaining category keywords in the session.
How to Confirm Successful Configuration
- Upload a single jewelry financing daily report document, check if the parsed result includes preset fields such as jewelry category, financing amount, and loan period. No missing fields indicates correct parsing configuration.
- Initiate a test query such as "What is the financing amount range for brooch financing in the Shanghai area this week?", check if the returned results only involve jewelry categories and have no cross-category content.
- Check the model response delay, confirm that single request time does not exceed the configured
PARSE_FILE_TIMEOUT_SECONDSthreshold, and no timeout errors occur. - Enable multi-turn dialogue testing: first ask "How long is the loan period for hair accessories financing?", then ask "What is the data for this category in the Beijing area?", check if the reply associates with the previously mentioned hair accessories category, confirming that context memory is effective.
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.