What the data for this category looks like
Data sources for industrial metal research reports cover domestic nonferrous metal industry associations, Shanghai Futures Exchange, London Metal Exchange, top brokerage research institutes, and international metal information platforms. Update frequency primarily follows weekly market reports and monthly supply and demand balance sheets, supplemented by special research reports on sudden policies and capacity adjustments. Document structures typically include market overview, core data tables, supply and demand analysis, price trend interpretation, and future outlook. Core fields include electrolytic aluminum inventory (unit: 10,000 tons), LME copper price (unit: USD/ton), monthly crude zinc output (unit: 10,000 tons), and others. Individual document lengths range from thousands to tens of thousands of words.
What constraints these characteristics impose on model access and configuration
Industrial metal research reports have numerous structured numerical fields and significant cross-market unit differences. This requires models to accurately identify and retain the association between fields and units. Embedding models must therefore adapt to semantic encoding of professional terminology. Frequently updated documents require vector databases to support incremental index configuration, to avoid excessive computing resource usage from full reindexing. The coexistence of long documents and short weekly reports requires chunk splitting to balance context integrity and recall accuracy, preventing disruption of the binding relationship between supply and demand data and corresponding analysis after splitting. Cross domestic and international market unit differences require unified unit expressions for fields during model calls, to avoid mismatched values and units in responses.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embeddingModel | shaw/dmeta-embedding-zh | Supports semantic encoding of Chinese industrial metal professional terminology, with higher recognition accuracy for fields such as inventory and prices |
maxContext | 8000–12000 characters | Adapts to the average length of industrial metal special research reports, retaining complete context for supply and demand analysis and market correlations |
recallTopK | Top 8–12 entries | Covers multiple dimensions of research reports including market, policy, and supply and demand, while avoiding exceeding the context window limit |
similarityThreshold | 0.72–0.78 | Filters irrelevant non-industrial metal research reports while retaining relevant content from different subcategories of the same category |
chunkSize | 1000–1500 characters | Retains the binding relationship between structured fields and corresponding analysis, avoiding semantic fragmentation caused by overly short splits |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing time required for large special research reports, avoiding interruptions to the parsing process due to timeouts |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: After deployment, calling the model interface returns a
401 Unauthorizederror, or the knowledge base configuration page prompts "model unauthorized". Cause: Failure to distinguish between OneAPI's global call configuration and FastGPT's knowledge base-specific model parameters, with duplicate configurations overwriting correct keys and model paths. - Phenomenon: Retrieved knowledge base documents contain specific clause content, but the model output only extracts a brief summary without expanding original details. Cause: The
maxTokenparameter is set too low, or the prompt template is not configured with clear instructions to retain original text fields and clauses. - Phenomenon: Knowledge base retrieval takes more than 30 seconds, causing page loading lag. Cause:
embeddingBatchSizeis set too large, exceeding the processing limit of 8 cores and 64GB of memory, or the vector database's incremental index update mechanism is not enabled.
How to confirm the configuration is complete
- Check the FastGPT model management interface to confirm that the
embeddingModelandLLMModelconfigurations match the preset parameters exactly. - Upload an industrial metal weekly report to test the parsed chunk splitting results, confirming that core fields and context are not truncated.
- Initiate a retrieval test, input a specified industrial metal-related question, and verify that the number of retrieved entries matches the
recallTopKsetting. - Check the system operation logs to confirm there are no records of model call timeouts, parameter parsing errors, or permission exceptions.
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.