What data for this category looks like
Data comes primarily from periodic reports of publicly traded iron ore-related enterprises, supply and demand and price reports published by authoritative industry institutions, and public port throughput and inventory data. Update cadence: Enterprise financial reports are released quarterly and annually. Industry data is updated monthly. Document structure includes financial statement sections, business operation data, and industry analysis chapters. Fields cover report period, mining volume, trade volume, average selling price, net profit, and more. Common units are ten thousand tons, yuan per ton, and ten thousand yuan.
Constraints on vector models and indexing
Iron ore financial report data includes structured business metrics and unstructured analysis text. Multi-source data updates at different cadences, creating multiple constraints for the vector model and indexing workflow. Long financial report paragraphs require appropriate segmentation strategies to avoid semantic fragmentation. Unit information for structured fields must be retained during preprocessing to ensure accurate vector semantics. Incremental updates must adapt to the different update frequencies of quarterly and monthly data, to avoid resource consumption from full index rebuilding. In multi-source data mixing scenarios, metadata fields must be used to distinguish data sources and report periods, to support precise retrieval filtering.
How to set configuration values
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Iron ore financial reports contain long business descriptions and transcribed structured table text. This range preserves complete semantic units and avoids segmenting critical indicators |
chunk_overlap | 100–200 characters | Key metrics in financial reports, such as quarterly revenue changes, often span segments. Overlap preserves contextual connections |
embedding_model_path | Determined via actual testing | Different localized models have varying semantic extraction effects for structured text. Validation must be performed using business data |
vector_db_batch_size | 32–64 entries | The volume of iron ore financial report data fluctuates with update cycles. This batch size balances insertion efficiency and memory usage |
filter_metadata_fields | ["report_period", "data_source", "unit"] | Financial report analysis requires precise data filtering by report period and data source. These fields must be included in the vector metadata index |
recall_top_k | Top 8–12 entries | Key business metrics in iron ore financial reports are distributed across multiple areas. An appropriate number of recalls covers multi-dimensional analysis needs |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test using your own samples before finalizing values.
Three common configuration errors
- Issue: Calling the vector model API returns
503 Service Unavailable, and logs show no availabletext-embeddingmodels in the currentdefaultgroup. Cause: No correct group identifier is specified in the vector model configuration, or the corresponding embedding model is not deployed in the group. - Issue: In a Docker deployment environment, indexing tasks remain in a running state with no progress. Cause: The incremental index switch is not configured, full index processing of multi-source mixed iron ore financial report data takes too long, or the
PARSE_FILE_TIMEOUT_SECONDStimeout parameter is not set, causing task blocking. - Issue: Retrieval results do not distinguish iron ore data from different report periods, and the
unitfield is empty. Cause: Unit fields are not retained during preprocessing, orunitis not included in the metadata index configuration, resulting in missing vector-associated metadata.
How to verify correct configuration
- Upload a single iron ore financial report sample, check if the number of segmented text blocks matches expectations, to confirm the segmentation configuration is active.
- View the vector database insertion logs, confirm that the fields specified in
filter_metadata_fieldshave been correctly written to vector metadata. - Submit a retrieval request, verify that retrieval results can be filtered using the
report_periodordata_sourcefields, to confirm metadata filtering is active. - Simulate monthly incremental data upload, check if indexing tasks only process new data without performing a full rebuild, to confirm the incremental update configuration is active.
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.