What This Type of Data Looks Like
Precious metals research report data comes primarily from public industry research reports, official market data from precious metals exchanges, and supply and demand statistical documents from industry associations. Two update cycles apply: market data is updated in real time, while macroeconomic and supply-demand reports are updated weekly or monthly. Documents include standardized fields: publishing institution, publish time, target category (gold, silver, platinum, etc.), unit price (unit: yuan/gram, US dollars/ounce), supply and demand gap analysis, policy impact interpretation. The main body consists of long, logically coherent paragraphs, with some including tabular historical price data.
Constraints on Vector Models and Indexing
The multi-dimensional characteristics of precious metals research reports impose clear constraints on the vector models and indexing workflow. Real-time market data updates require indexes to support incremental synchronization, avoiding performance loss from full reindexing. Unit price fields with multiple units (yuan/gram, US dollars/ounce) require vector models to retain unit association information during encoding, preventing semantic confusion across units. The mixed document structure of long paragraphs and structured tables requires separate vector chunking for table content, avoiding semantic breaks. Research reports for different precious metal categories need index partitioning by category to improve retrieval precision.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Length | 800–1200 characters | Precious metals research report bodies are mostly logically coherent paragraphs. This length preserves single-chunk semantic integrity and avoids cross-chunk semantic fragmentation |
Number of Retrieved Results | Top 10–15 results | Precious metals research reports have many specialized categories. Too many retrieved results introduce irrelevant category interference, too few fail to cover valid relevant content |
Similarity Threshold | 0.72–0.80 | This range balances retrieval precision and coverage, by distinguishing semantic similarity between reports of the same category across different time periods and irrelevant reports across categories |
Incremental Sync Interval | 5 minutes | Real-time market data has a high update frequency. This interval ensures the timeliness of retrieved data while controlling index write pressure |
Index Partition Field | Target Category | Partitioning by precious metal specialized categories quickly filters out irrelevant category reports and improves retrieval efficiency |
Vector Model Input Length Limit | Follow model official specification (e.g. 8192 tokens) | Avoids semantic loss from truncated long text, and adapts to the length characteristics of research report bodies |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by data format, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- A 401 error is returned when calling the vector model. Cause: The vector model's API key and request address are not configured correctly, or insufficient key permissions lead to authentication failure.
- Duplicate content is automatically deleted after custom-split documents are stored in the knowledge base, resulting in inconsistent index order with the original document split results. Cause: The knowledge base has duplicate document deduplication enabled by default, and no special configuration is made for block-level duplicates from custom splits.
- After configuring a custom vector model, retrieval requests still call the large language model. Cause: The vector model is not bound to the knowledge base's retrieval link, or the channel configuration does not explicitly specify it as a vector model type.
How to Verify Proper Configuration
- Upload a single precious metals research report, check if the chunked content encoded by the vector model retains unit price units and category information, with no obvious semantic truncation.
- Initiate a retrieval request for a specific precious metal category, verify that the partition fields of the retrieved results match the retrieval criteria, with no cross-category irrelevant content.
- Simulate incremental updates of real-time market data, check if the index only synchronizes new content without triggering full reindexing.
- View the vector model call logs, confirm that the request path and authentication parameters meet the configuration requirements, with no 401 or other authentication errors.
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.