Data Characteristics of This Category
Energy metals financial report data mainly comes from exchange public announcements, industry association monthly reports, and annual and quarterly reports of listed companies. Update rhythms are divided into fixed cycles and event triggers. Annual financial reports are updated once a year, quarterly financial reports are updated every quarter, and temporary announcements such as capacity changes and price adjustments are released in real time as events occur.
The document structure mixes structured fields and unstructured text. Structured fields include metal grade, capacity, reserves, spot prices and other items. Units are mostly ten thousand tons, grams per ton, and US dollars per ton. Unstructured text includes long-form content such as industry analysis and enterprise operation interpretations.
Constraints Imposed on Vector Models and Indexing
The mixed structure and update characteristics of energy metals financial reports impose multiple constraints on the vector models and indexing link. First, the coexistence of long text and structured fields requires vector models to support long-context embedding, while indexes need to support filtered recall by fields. Second, the real-time update requirement for temporary announcements requires indexes to support incremental updates without full reconstruction, avoiding repeated calculation of embedding vectors for full datasets. Third, the mixed deployment scenario of multiple energy metal categories (lithium, cobalt, nickel, etc.) requires indexes to support precise filtering by category fields to prevent recall of irrelevant content.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | m3e-base | Energy metals financial reports contain a large number of industry-specific terms and quantitative data. The m3e model's Chinese semantic understanding capability adapts to the text embedding needs of this scenario |
chunk_size | 800–1200 characters | The length of single-segment business descriptions in energy metals financial reports mostly ranges from 500 to 1000 characters. Avoid splitting to damage the semantic integrity of core data such as capacity and reserves |
recall_top_k | Top 10–15 entries | Financial report analysis needs to cover multi-dimensional data including upstream raw materials, downstream demand, enterprise operations and other dimensions. Too few recalls will miss key information |
vector_db_index_type | HNSW | The vector data dimension of energy metals financial reports is mostly 768 dimensions. The HNSW index balances recall accuracy and query speed, adapting to high-dimensional vector scenarios |
incremental_update_interval | 2:00 AM daily | Temporary financial report announcements are mostly released outside trading hours. Daily incremental updates can ensure data timeliness and reduce resource consumption of full index reconstruction |
filter_field_enable | Enabled | Supports filtering by report_type (annual report/quarterly report/temporary announcement) and metal_type (lithium/cobalt/nickel) fields to accurately recall financial report content of target categories |
cache_ttl | 3600 seconds | Financial report data has a low update frequency. Setting a 1-hour cache validity period can reuse vector recall results of repeated queries and reduce the load on the vector database |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: A large number of financial report contents from non-energy metal categories are mixed in vector recall results. Cause: The
filter_field_enableconfiguration is not enabled, and themetal_typefiltering field is not specified, resulting in a recall range covering all category documents. - Phenomenon: The vector database cannot synchronize data normally after source code deployment, and the log returns the connection error code
ETIMEDOUT. Cause: Only the MongoDB and vector database images are started, and the sandbox network policy is not configured, causing the data parsing link to fail to access the financial report data source. - Phenomenon: After uploading financial reports through "Add Data to Collection", usable vector indexes cannot be generated. Cause: The "Create Training Order" process is not executed. This process triggers vector model embedding and index construction. Uploading text alone will not automatically generate vector indexes.
How to Confirm Proper Configuration
- Upload a sample annual financial report of a lithium mining enterprise, perform vector recall, and check whether the recall results contain content where
metal_typeis lithium. - Log in to the vector database management interface, view the index list, and confirm that there is an HNSW type index corresponding to the energy metals financial report collection.
- Execute the same financial report keyword query twice, check whether the second query reuses the cache. This can be confirmed by checking whether the request latency is significantly shortened through the vector database's query logs.
- Execute the "Create Training Order" process, check whether the status in the task management interface is displayed as "Completed", and confirm that the vector index construction is successful.
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.