Citation Sources and Traceability for Industrial Metals Financial Report Analysis

Industrial metals financial reports and industry data primarily come from official trading platforms including the London Metal Exchange and the

What data for this category looks like

Industrial metals financial reports and industry data primarily come from official trading platforms including the London Metal Exchange and the Shanghai Futures Exchange, as well as industry institutions such as the China Nonferrous Metals Industry Association. It also includes quarterly and annual public financial reports of domestic listed copper and aluminum smelting enterprises.

Data update schedules differ:

  • Monthly spot prices and inventory data are released by the 5th day of the following month
  • Quarterly financial reports are disclosed within 45 working days after the end of the quarter
  • Annual financial reports must be published by the end of March of the following year

Document structures include core financial statements and detailed category data tables. Fields cover production volume, inventory volume, import and export volume of products such as cathode copper and aluminum ingots. Pricing units include tons and kilograms, with two pricing systems: RMB and USD.

Constraints on citation sources and traceability

Dispersed multi-source data requires clear labeling of data source entities and release times during traceability, to avoid confusion between statistical standards of different institutions.

Differentiated update frequencies mean a single query may match both historical financial reports and real-time spot data. Timestamp filtering is required to ensure recalled data timeliness matches business requirements.

The multi-unit and multi-pricing system of fields requires retaining original unit identifiers during traceability. Without this, data interpretation errors may occur.

Long documents and dense detailed fields require recalled fragments to be accurately positioned to specific data units. This avoids generalized recall causing contextual redundancy. Original professional expressions of fields must also be fully retained.

How to set the configuration

Configuration ItemRecommended ValueRationale
recall_top_kTop 8-12 entriesThe volume of industrial metals financial reports and industry data is large. Excessive recall will exceed the context capacity limit of the large model, while insufficient recall may miss key data units
vector_similarity_threshold0.75-0.85Financial reports and industry data have strong professionalism. A higher similarity threshold can filter irrelevant content and ensure recalled fragments highly match query requirements
source_tag_fieldData source name + data release timeThis can clearly distinguish different sources such as London Metal Exchange inventory data and Shanghai Futures Exchange financial reports, meeting precise traceability labeling requirements
parse_chunk_size800-1200 charactersIndustrial metals financial report appendices have dense fields. A moderate segment length retains complete data units and avoids losing field association relationships after splitting
rag_enable_source_citeEnabledForcibly attach data source identifiers to responses, meeting traceability compliance requirements for industrial metal scenarios
function_call_max_rows50Limit the number of database fragments returned by a single Function CALL to avoid excessive content occupying the large model context

The parameter values provided on this page are common starting points 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.

Three common configuration mistakes

  • Phenomenon: After calling MySQL to retrieve industrial metal inventory data, the response does not display the original database fragment, and only vaguely mentions the data source. Cause: The rag_enable_source_cite configuration is not enabled, and original fragments of database query results are not cached to the conversation context.
  • Phenomenon: The number of recalled entries from the locally deployed FastGPT knowledge base is fixed and cannot be adjusted. Cause: The recall_top_k parameter in the deployment configuration file is not modified, and the default value is still used.
  • Phenomenon: The finish_reason field is not included in the conversation context of subsequent components. Cause: This field is not added to the transfer parameter list of the conversation context, so subsequent components cannot obtain the field value.

How to confirm correct configuration

  • Initiate a query containing industrial metal financial report keywords, and check if the response ends with labels for data source names and data release times.
  • View the knowledge base recall log to confirm the number of recalled fragments matches the recall_top_k configuration value.
  • Call Function CALL to retrieve industrial metal data from the MySQL database, and check if original database field content is displayed in the response.
  • Modify the vector_similarity_threshold value, and verify if recall result relevance changes as expected.

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.