What the data for this category looks like
Data sources for industrial metals investment research cover official quotes from domestic and overseas futures exchanges, monthly supply and demand surveys from industry associations, public announcements from upstream mining and smelting enterprises, and tracking reports from third-party investment research institutions. Update frequencies vary across data types: quote data updates in real time intraday, industry surveys and corporate announcements release on a quarterly or monthly basis, and investment research reports are updated ad-hoc alongside market movements.
Documents include structured quote tables, semi-structured supply and demand analysis sections, and qualitative policy interpretation paragraphs. Fields cover delivery grade, total inventory, spot price, smelting capacity, and more. Common units are tons, yuan per ton, and ten thousand tons per year.
Constraints on vector models and indexing
Industrial metals investment research data includes a large number of structured fields and content with multiple update frequencies. First, vector models must support mixed embedding of structured and unstructured data to avoid losing structured information when using single-text embedding. Second, mixed update rhythms require the indexing system to support a strategy combining incremental updates and scheduled full refreshes, balancing real-time performance and indexing overhead. Third, delivery grades and capacity units differ across industrial metal types, so vector models must adapt to semantic and numerical feature fusion across multiple fields to prevent embedding bias. Fourth, single-document lengths vary widely, so adaptive segmentation rules must be configured to handle short quote snapshots and long in-depth research reports.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
segment_length | 800–1200 characters | Adapts to the semantic integrity of supply and demand analysis and policy interpretation paragraphs in industrial metals research reports, avoiding broken data associations from improper splitting |
embedding_model | bge-large-zh-v1.5 | Adapts to Chinese professional investment research terminology, delivering stable embedding performance for structured fields and text content related to industrial metals |
recall_count | Top 8–12 results | Industrial metals investment research requires coverage of multi-dimensional data including quotes, supply and demand, and policies. A reasonable recall volume balances information completeness and relevance |
similarity_threshold | 0.72–0.80 | Filters low-correlation historical quotes or unrelated research reports, retaining content that strongly matches current investment research queries |
incremental_index_toggle | Enabled | Matches the mixed update rhythm of real-time quotes and periodic reports for industrial metals, reducing redundant indexing overhead |
structured_data_embedding | Enabled | Industrial metals documents contain a large number of structured quote tables and capacity data. Enabling this option preserves semantic features of structured fields |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material formats, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Symptom: The
index_modeldropdown menu on the knowledge base configuration page is empty, and no indexing model can be selected. Cause: The local vector model service has not been deployed and connected in advance, or the service listening port has not been added to FastGPT's access whitelist. - Symptom: The number of vector recall results far exceeds the set value, and the
source_filefield of some traceable documents is empty. Cause: The metadata collection switch for document traceability has not been enabled, and the association mapping rule for segmented storage has not been configured, making it impossible to bind text blocks to source documents. - Symptom: After deploying the embedding model locally, the vector similarity calculation results deviate significantly from text semantic matching results. Cause: The embedding model and reranking model use different training corpus domains, leading to inconsistent mappings between the vector space and semantic space.
How to verify successful configuration
- Navigate to the configuration page of the target knowledge base, check that the values of core configuration items such as
segment_lengthandembedding_modelmatch the preset plan. - Upload a test document containing structured quote tables and research report text, trigger vector index construction, and check whether the indexing progress log includes successful structured data embedding markers.
- Submit a query targeting industrial metal quotes or supply and demand, verify that the traceable document metadata of the recall results is complete, and that the number of recall results falls within the preset range.
- Simulate an incremental update by uploading a latest industry survey document, check whether the indexing system only processes new content and does not trigger a full reindex.
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.