Vector Models and Indexing for Iron Ore Financing Daily Reports

Iron ore financing daily report data is sourced from domestic bulk commodity spot trade monitoring data, public delivery data from futures exchanges

What the data for this category looks like

Iron ore financing daily report data is sourced from domestic bulk commodity spot trade monitoring data, public delivery data from futures exchanges, and daily financing transaction ledgers submitted by industry associations. The update schedule is to complete daily data aggregation and release after 16:30 on each trading day. Each document is a structured daily report, including trading date, iron ore variety identifier, total daily financing transaction amount, total futures delivery warehouse receipts, total coastal port inventory, and core trading region transaction proportion value. Units are RMB 100 million, tons, 10,000 tons, and unitless proportion values respectively.

High-frequency daily updated structured features require the index to support incremental synchronization instead of full reconstruction, to avoid repeated computing resource consumption. Mixed multi-field numerical and identifier content requires the vector model to adapt to structured data embedding, and normalize numerical fields with different units to ensure balanced dimension weights of embedded vectors. Filtering requirements by date and variety require the index to support secondary index association, to quickly filter iron ore financing data for a specified trading day. Fixed but multi-field single document structure requires avoiding redundant information interference during embedding, and prioritize generating embedded vectors for core financing indicators.

How to configure the settings

Configuration ItemRecommended SettingRationale
embedding_modelshaw/dmeta-embedding-zhAdapts to semantic embedding of Chinese bulk commodity industry terminology and structured numerical values, and meets the text understanding requirements of financing daily reports
index_refresh_interval3600 secondsMatches the update schedule of iron ore financing daily reports one hour after daily market close, ensuring real-time synchronization between index data and source data
chunk_size800–1200 charactersAdapts to the length of core content in a single daily report, avoiding splitting that disrupts the correlation of financing indicators
filter_field_list["trade_date", "variety"]Matches core query scenarios, supports quick filtering of target data by trading day and variety
vector_dimension768Matches the vector dimension output by the shaw/dmeta-embedding-zh model, avoiding storage and matching anomalies
recall_top_kTop 10 entriesAdapts to the number of core indicators in iron ore financing daily reports, reducing computing overhead caused by redundant recall

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, and it is recommended to test on your own samples before finalizing the settings.

Three common mistakes

  • Phenomenon: Under the hardware configuration of 8 cores, 64GB memory, and RTX2070, knowledge base search response times out and returns status code 504 Gateway Timeout. Cause: Incremental indexing is not configured for high-frequency updated structured data, and full index reconstruction occupies too much GPU video memory and CPU computing resources.
  • Phenomenon: The custom index does not associate the trade_date field, so data cannot be filtered by trading day during query, and returned results include financing daily reports of irrelevant varieties. Cause: The filter field list is not specified in the configuration, and the secondary index association between variety and date is not established.
  • Phenomenon: The embedded vector dimension does not match the vector_dimension configured for the index, returning the error Vector dimension mismatch. Cause: The output dimension of the vector model is not matched, and an embedding model of another dimension is mistakenly used for data processing.

How to confirm the configuration is correct

  • Verify the vector model configuration item, confirm that it matches the model name used when generating embedded vectors, and check that the vector_dimension in the index configuration matches the model output dimension.
  • Submit a test query with trade_date and variety filter conditions, confirm that the returned results only include iron ore financing daily reports for the target trading day.
  • Upload a single newly added iron ore financing daily report, check that the index only synchronizes the new entry and does not trigger a full index reconstruction.
  • View system resource monitoring, confirm that CPU, memory and GPU usage are within the reasonable range of the current hardware configuration.

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.