What data for this category looks like
Precious metals investment research data primarily comes from Shanghai Gold Exchange (SGE) real-time quotes, London Bullion Market Association (LBMA) international pricing, domestic futures exchange contract data, industry research reports, and macroeconomic related data. Real-time quote data updates at a second-level frequency. Spot prices generate fixed snapshots after daily market close. Research reports are released irregularly alongside industry developments.
Single quote data includes fields such as product identifier, timestamp, opening price, highest price, lowest price, closing price, trading volume, and position volume. Price units are mostly yuan/gram or US dollars/ounce. Trading volume units are kilograms or ounces. Research report data is structured long text with embedded chart data and core viewpoint summaries.
What constraints these characteristics impose on database and operations work
Second-level real-time quote updates require the database to support high-concurrent write capabilities. Otherwise, data delays or loss may occur. Multi-source data integration requires the database to support flexible field mapping and associated queries, to avoid data redundancy.
Precious metal prices are strongly linked to exchange rates. An additional exchange rate benchmark table must be stored, which increases cross-table query complexity. Vector indexing for long-text research reports requires higher dimensionality to preserve semantic details. Historical quote data archiving requires a tiered storage strategy, to separate storage media for hot and cold data.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
mongodb_version | 6.0 or 7.0 | MongoDB 6.0 and above support vector search plugins, adapting to multi-source data writing and querying. 7.0 optimizes sharding performance to handle high-frequency quote write pressure |
vector_dimension | 1536 or 3072 | Semantic retrieval of quote metadata uses 1536 dimensions to cover core features. Embedding for long-text research reports requires 3072 dimensions to retain more details |
milvus_collection_partition | Partition by daily_quotes, research_reports, exchange_rates | Access frequency varies significantly across data types. Partitioning avoids full table scans and improves query efficiency |
redis_cache_ttl | 30 seconds | Real-time quote data has strong timeliness. Caching for 30 seconds reduces database query pressure while ensuring timely data updates |
PARSE_FILE_TIMEOUT_SECONDS | 1200 seconds | Precious metal research reports often contain multiple embedded charts and long text, leading to long parsing times. Setting a longer timeout prevents task interruptions |
retrieve_top_k | 10–15 | Investment research scenarios require a balance between comprehensive recall and result relevance. A reasonable number of recall results reduces the processing burden on large models |
The parameter values provided on this page are common recommendations to serve as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common misconfigurations
- Symptom: A
connection timeouterror occurs after deploying MongoDB 7.0. Cause: The MongoDB vector search plugin is not enabled, or the sharding configuration is not adapted to high-frequency write quote data. - Symptom: Uploading a precious metal research report returns
0 matching results. Cause: The vector dimension configuration does not match the dimension output by the embedding model, so the index cannot match semantic retrieval requests. - Symptom: Memory usage of Docker Compose-deployed services continues to rise. Cause: Automatic eviction policy for Redis cache is not configured, and real-time quote cache is not cleaned in a timely manner, exhausting system memory.
How to confirm configurations are properly set
- Run the
mongo --eval "db.stats()"command to view database write throughput. Adjust parameter thresholds based on actual quote update frequency. - Upload a standard precious metal research report, check the vector database index logs to confirm that the embedding vector dimension matches the configured parameters.
- Simulate high-frequency quote data write requests, check database response latency, adjust shard count or cache strategy based on business requirements.
- Call the retrieval interface, view the number of returned recall results to verify that the
retrieve_top_kparameter configuration 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.