What This Category of Data Looks Like
Data related to precious metals financial reports comes from periodic public financial reports of relevant mining enterprises, industry disclosure data from the Shanghai Gold Exchange, the London Bullion Market Association, and third-party supply and demand analysis reports. Data update cadence is tiered: corporate financial reports are released quarterly, spot market trading data is updated daily, and industry supply and demand reports are released monthly. Document structures include fields such as variety details, holdings, average daily transaction price, supply and demand gap, and central bank operation data. Units are mostly ounces, grams, or tons, with some exchange rate conversion fields for RMB and USD pricing.
Constraints Imposed on Vector Models and Indexing
These data characteristics impose multiple constraints on the vector model and indexing workflow. First, the data includes structured financial report fields and unstructured analysis text, alongside multi-unit numeric fields. Vector models must support mixed-modal encoding to avoid semantic bias stemming from unit differences. Second, the tiered update cadence requires different index update strategies for daily market data and quarterly corporate financial reports. Combined configuration of incremental indexing and full indexing must be supported. Third, there are numerous detailed product categories, so the index must support filtering and recall by category and time dimension to prevent interference from cross-category irrelevant data. Fourth, some data has strong timeliness, so short-cycle index refresh mechanisms must be configured to maintain real-time recall results.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Precious metals financial reports contain long sections of supply and demand analysis and spliced structured data. This length preserves complete semantic units and avoids splitting that breaks field associations |
embedding_model | bge-large-zh-1.5 | This model delivers stable encoding performance for Chinese financial text and numeric semantics, supports mixed encoding of multiple fields, and adapts to the mixed data types of precious metals financial reports |
index_refresh_interval | 1 hour (daily data), 7 days (quarterly financial reports) | Tiered updates match the data release cadence, balancing index efficiency and real-time performance |
filter_field_list | ["variety", "report_date", "unit"] | Filtering by precious metals category, report time, and unit dimensions reduces recall redundancy and improves analysis accuracy |
recall_top_k | Top 10 results | Precious metals financial report analysis requires a balance between comprehensiveness and accuracy. Excessive recall increases context redundancy and reduces analysis efficiency |
similarity_threshold | 0.72–0.78 | The semantic similarity threshold for financial text must be higher than that for general scenarios, to avoid low-relevance data being included in analysis results |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Issue: Uploading locally encoded precious metals financial report data to the server returns a
400 Bad Requesterror. Cause: The embedding dimension of the vector model used locally does not match that used on the server, and the encoded vector format cannot be recognized by the indexing system. - Issue: A single financial report file over 50 MB remains in the "Indexing" status with no progress feedback. Cause: No reasonable
chunk_overlapparameter is set. Duplicate binding of split long text fragments causes the indexing process to block. - Issue: Knowledge Q&A recall results include content from two knowledge bases, and the preset specified knowledge base is not prioritized. Cause: No filtering rule for knowledge base priority is configured. The indexing system does not sort results by the specified priority field.
How to Confirm Proper Configuration
- Upload a test segment of a precious metals financial report, and verify that the dimension of the vector encoding matches the embedding dimension marked by the selected
embedding_model. This can be validated via system logs or debug interfaces. - After configuring tiered refresh rules, manually trigger incremental indexing for daily data, and check if index update logs execute according to the preset time cycle.
- Submit a test query covering multiple categories, and check if recall results are filtered by the fields specified in
filter_field_list. - Test cross-knowledge-base query requests, and confirm that recall results return according to the preset knowledge base priority.
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.