What the data for this category looks like
Data sources for iron ore research reports include public reports from industry associations, futures exchange market data, analysis from third-party consulting firms, and internal survey documents from steel mills. Update cycles fall into three categories: high-frequency (weekly/10-day port inventory and price weekly reports), medium-frequency (monthly supply and demand balance sheets), and low-frequency (quarterly/annual in-depth analysis reports). Document structures typically include sections on market overview, supply and demand data, price trends, policy interpretations, and future outlook. Fields include port name, inventory in 10,000 tons, price per wet metric ton, futures contract code, and more. Some research reports include structured tables and chart data.
What constraints these characteristics impose on vector models and indexing
Multi-source heterogeneous data formats require vector indexing systems to support structured table extraction and combined encoding of unstructured text, to avoid losing semantic associations of professional fields. Research reports with different update frequencies require differentiated incremental and full indexing strategies. High-frequency weekly reports need hourly incremental refresh support, while low-frequency in-depth reports can be refreshed weekly, balancing index freshness and computing resource usage. Entity fields with specific units (such as yuan per wet metric ton, 10,000 tons) require the vector model to retain semantic binding, otherwise retrieval cannot distinguish price data for different categories. Segmentation of long-text research reports must retain contextual connections, to avoid splitting critical logical chains and harming recall accuracy.
How to set configurations
| Config Item | Suggested Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Iron ore research reports contain extensive coherent market analysis and structured tables. This segmentation length preserves semantic integrity for individual segments, and avoids splitting table rows or core logic |
chunk_overlap | 100–150 characters | Supply and demand data and market interpretations in research reports often span segment boundaries. This overlap length ensures contextual continuity, and prevents semantic breaks during recall |
vector_model_name | bge-m3 | This model supports semantic encoding of professional entities, accurately identifies iron ore-related terminology and unit information, and adapts to retrieval requirements for this specialized domain |
index_refresh_interval | Differentiated by data type: high-frequency weekly reports set to hourly, in-depth reports set to weekly | Matches the different update cycles of iron ore research reports, balancing index freshness and computing resource consumption |
top_k_retrieval | Top 8–10 results | Iron ore research reports have high professional information density. This recall range covers complete analytical logic, while avoiding irrelevant content |
embedding_batch_size | 32–64 entries | Adapts to the long-text characteristics of individual research reports, prevents server memory overflow during vectorization, and improves processing efficiency |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: Index building takes too long, and the
indexing_timeoutstatus code is displayed in the interface. Cause: Thechunk_sizeandembedding_batch_sizeparameters are not adjusted for the long text and multi-source data of iron ore research reports, leading to excessive per-batch processing load. - Symptom:
ollama qwen2.5andbge-m3have been added to the model channel, but the corresponding options do not appear in the text understanding model dropdown when creating a knowledge base. Cause: The vector model and language model are not bound to their respective configuration items, or the model loading path is not synchronized to the environment variables of the docker-compose service. - Symptom: Unit field information is missing from the vectorized dataset, and retrieval results cannot accurately match target supply and demand data. Cause: Structured field retention configuration is not enabled, and vectorization is performed directly on plain text, leading to loss of semantic information for units such as iron ore prices and inventory.
How to confirm proper configuration
- Check the vector model loading logs to confirm the target model has been successfully loaded, with no
model_load_failederror messages. - Submit a single iron ore research report for testing, and verify that the length of segmented text blocks falls within the preset
chunk_sizerange. - Initiate a retrieval request, and verify that the recall results include core fields from the target research report, such as port inventory and price trend information.
- Check the index refresh logs to confirm that high-frequency research reports complete incremental updates according to the preset
index_refresh_interval.
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.