What this type of data looks like
Iron ore research and investment data mainly comes from industry news platforms, public data from futures exchanges, port spot monitoring systems, and third-party research reports. There are three update frequency categories: spot port quotes are updated daily, prices of main futures contracts are updated in sync with post-market trading sessions, and industry supply and demand research reports are released weekly or biweekly. Most documents use structured tables as their main format, including fields such as date, grade level, origin, loading and unloading port, transaction unit price (yuan/wet ton), total inventory, and month-on-month change. Some research reports also include unstructured supply and demand analysis text.
What constraints these characteristics impose on model access and configuration
The multi-type update schedules, high proportion of structured data, and numerous field categories of iron ore research and investment data create multiple constraints for model access and configuration. A high proportion of structured tables requires configuring output validation rules that support JSON format to ensure consistent formatting of retrieved data. Significant differences in update frequencies across data sources require configuring incremental index trigger conditions to only synchronize newly added or updated data, avoiding invalid index overhead. Categorized attributes of multiple fields such as grade level and origin require configuring field weights for vector retrieval to prioritize matching core transaction fields and improve retrieval accuracy. The presence of regional and category aliases requires enabling entity normalization configuration to unify recognition of different expressions for the same type of naming, reducing matching errors.
How to set the configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
embedding_model | Select a vector model that supports Chinese professional text, such as bge-large-zh-v1.5 | Iron ore research and investment data contains a large number of industry terms and structured fields. This model has strong adaptability to vector representation of professional Chinese text |
chunk_size | 800–1200 characters | After splitting iron ore research report text and spot tables, this length can retain the complete semantics of a single piece of data and avoid field fragmentation |
response_format | Enable JSON Schema validation mode | The high proportion of structured data allows enforcing the model to output results that conform to the preset format, reducing unstructured redundant content |
vector_db_retrieve_top_k | Top 8–12 entries | Balances retrieval coverage and computing efficiency, matching the multi-dimensional data reference required for iron ore research and investment |
similarity_threshold | 0.72–0.78 | Filters low-correlation non-professional text and retains retrieval results strongly related to iron ore grade and price |
embedding_batch_size | 32–64 entries per batch | Adapts to the single-batch data volume and avoids memory overflow errors during the indexing process |
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 case-by-case analysis. It is recommended to test on your own samples before finalizing the settings.
Three common configuration errors
- Phenomenon: Calling the vector model returns a 403 Forbidden error, but the model interface can be accessed normally through curl testing. Cause: The Authorization parameter of the request header is not correctly configured in the FastGPT model configuration, or the cross-domain access permission for the model interface is not enabled.
- Phenomenon: The model generates results that do not conform to the preset structured format, requiring manual adjustment before storage. Cause: The JSON Schema validation configuration of
response_formatis not enabled, and only prompt words are used to constrain the format, which cannot force the model to output a standard structure. - Phenomenon: Incremental indexing tasks frequently time out, and single-batch indexing time exceeds expected values. Cause: The
chunk_sizeparameter is not adjusted based on the length of a single piece of iron ore data, resulting in an excessively large single-batch data volume that exceeds the model's processing limit.
How to confirm the configuration is complete
- Perform a vector generation test for a single data entry, verify the semantic matching degree between the generated vector and the original data, and adjust
similarity_thresholdto a range that meets business requirements. - Initiate a full indexing task, check the indexing logs for errors related to failed model calls, and confirm that the configuration parameters of
embedding_modelmatch the actually deployed model. - Trigger a structured data retrieval test, verify that the returned results conform to the preset JSON Schema format, and confirm that the
response_formatconfiguration has taken effect. - Check the index statistics of the vector database, confirm that the incremental index only synchronizes newly added or updated data, and there is no repeated indexing of old data.
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.