What Data for This Category Looks Like
Data sources for the minor metals industry include spot price sheets, customs import and export declarations, capacity reports disclosed by mining enterprises, and weekly inventory reports and research reports released by industry associations. Data sources include domestic non-ferrous metal industry associations, commodity exchanges, publicly available General Administration of Customs data, and official enterprise announcements. Update frequencies vary significantly: spot prices are updated daily, inventory data is updated weekly, and industry policies and company updates are released irregularly. Document structures include both structured tables and unstructured text. Core fields include grade proportion, metal tonnage, tax-included unit price, and FOB price. Mixed unit usage exists for some fields.
Constraints Imposed by These Characteristics on Model Access and Configuration
The heterogeneous sources of minor metals data require configuring access validation rules for multiple data sources to ensure correct parsing of documents in different formats. The mixed unit usage of fields requires configuring field standardization mapping parameters for the model to unify unit and terminology expressions across different data sources. Differences in update frequencies require configuring multiple scheduled synchronization trigger interval parameters to match the actual update frequency of corresponding data. Individual industry research reports can reach tens of thousands of characters. This requires configuring context window parameters adapted for long texts to avoid information truncation. There are many dedicated indicators for individual sub-categories. This requires configuring dedicated prompt templates for the model’s sub-categories to ensure the model can recognize dedicated parameters for different minor metals.
How to Set Configuration Parameters
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
parseChunkSize | 800–1200 characters | Core information paragraphs of minor metals industry research reports and weekly inventory reports fall within this range. Avoiding overly fragmented segments preserves the integrity of technical terms, while avoiding overly long segments reduces recall accuracy degradation |
ragTopK | Top 8–12 entries | The number of relevant data sources for minor metal sub-categories is moderate. Excessive recall increases model inference load, while insufficient recall fails to cover all key information |
datasourceSyncInterval | Spot data sources set to 86400 seconds, inventory data sources set to 604800 seconds, policy data sources set to 2592000 seconds | Match the actual update rhythm of corresponding data to avoid ineffective synchronization or data lag |
similarityThreshold | 0.75–0.82 | Minor metal technical terms have high distinguishability. A threshold that is too low introduces irrelevant industry documents, while a threshold that is too high may miss relevant information for sub-categories |
embeddingBatchSize | 16–32 | Adapt to the number of fields and single-data length of minor metals data, balancing embedding efficiency and video memory usage |
parseTimeout | 300–600 seconds | Cover the parsing time of a single long research report, avoiding data synchronization failure due to timeout |
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 specific analysis. It is recommended to conduct tests on applicable samples before finalizing configuration.
Three Common Configuration Mistakes
- Phenomenon: An invalid token error is displayed in the interface when accessing a large model via a third-party transit service. Cause: The interface address of the transit service was not configured correctly, or the generated token was not bound to the model permissions required for minor metals due diligence.
- Phenomenon: A 400 error or no matching results are returned during search testing after adding an embedding model and completing indexing. Cause: The
parseChunkSizevalue used during indexing does not match the segmentation parameter used during search, or the vector database and embedding model dimensions do not match. - Phenomenon: A timeout error occurs when synchronizing minor metals data sources for a Docker-deployed FastGPT instance. Cause: The container did not open outbound ports for the corresponding data sources, or the
parseTimeoutconfiguration value is shorter than the parsing time required for long research reports.
How to Confirm Successful Configuration
- Execute a data source synchronization task, check the field parsing results in the synchronization log, and confirm that configuration parameters match the data source document structure.
- Initiate a search test for a single minor metal sub-category, verify that the number of recall results matches the configured number of recall entries, and confirm that the recall logic is working correctly.
- Call the large model interface for testing, check whether the returned results have completed unit unification and field mapping for minor metals data, and confirm that the model prompt template configuration is valid.
- Check the container port mapping configuration, confirm that access requests to data sources can be forwarded normally, and confirm that the network 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.