What the data for this category looks like
Energy metals investment research data comes primarily from industry association monthly reports, exchange spot daily reports, public company announcements, policy documents, and supply chain quotation data sources. Update frequencies cover real-time (spot quotations), daily (industry updates), weekly/monthly (research reports and policies). Document structures fall into three categories: structured spot quotation tables with fields such as product, specification, delivery location, quotation unit, etc.; semi-structured industry research report paragraphs; unstructured policy and announcement texts. Fields and units have differentiated characteristics. For example, lithium salt quotations use yuan/ton as the unit, precious metal quotations use US dollars/ounce as the unit. Some research reports include professional indicators such as grade and production capacity.
Constraints imposed on vector models and indexing by these characteristics
The multi-field and differentiated units of structured quotation tables require field-level structured extraction and normalization first, to avoid unit confusion affecting vector similarity calculations. Daily updated spot data has large incremental volumes, so the index must support incremental writing instead of full reconstruction, to reduce operational overhead. Research reports and announcements have wide length ranges, from hundreds to tens of thousands of words, so an adaptive segmentation strategy is needed to avoid truncating core logically related content. Professional indicators across different categories require field mapping to ensure consistency during vector matching, and avoid invalid recall of cross-category data.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | bce-embedding-v1 | Supports accurate vectorization of Chinese industry terminology, adapts to the characteristics of energy metals research reports and structured quotation data |
chunk_size | 800-1200 characters | The core logical unit of energy metals research reports mostly falls within this range, avoiding truncation of key content such as industry chain linkages |
incremental_index_enable | true | Adapts to the daily incremental update demand of spot data, reduces the time spent on full index reconstruction |
recall_top_k | Top 10-15 results | Covers multi-dimensional quotation and research report data required for investment research, avoids insufficient recall |
filter_by_time | Enabled, retain data from the last 90 days | Matches the timeliness requirements of energy metal prices and policies, filters outdated data |
similarity_threshold | 0.75-0.85 | Based on the field discrimination of energy metal data, this range effectively filters irrelevant results |
The parameter values provided on this page are all conventional recommendations used 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 the settings.
Three common misconfigurations
- Symptom: Calling
bce-embedding-v1returns "This token does not have permission to use the model". Cause: The authentication parameters for the corresponding channel were not correctly configured in FastGPT's model settings, or the channel platform has not enabled usage permissions for this model. - Symptom: The
bce-embedding-v1for the One API channel is configured, but FastGPT prompts "No available channels". Cause: The model name was not correctly bound in the channel configuration, or the channel's access key does not have the correct access scope. - Symptom: Segmented vectors of long-text research reports show logical breaks. Cause:
chunk_sizeis set too small, truncating core analysis content such as upstream and downstream industry chain linkages in the research report.
How to verify correct configuration
- Run a vectorization test for a single spot quotation data entry. Verify that the returned vector dimension matches the official parameters of the selected model to confirm normal model invocation.
- Trigger an incremental index task. Check that the index log only displays newly added data source entries from the current day to confirm the incremental index configuration is effective.
- Initiate a retrieval request containing "lithium price" and "copper production capacity". Verify that the recall results include documents of the specified type to confirm field weighting and filtering configurations are effective.
- Review the model call return logs. Confirm that no permission errors or channel unavailable prompts appear to confirm the authentication configuration is correct.
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.