What the Data for This Category Looks Like
Data for this category comes primarily from authoritative third-party institutions in the domestic steel industry, public announcements of listed steel enterprises, professional industry journals, and research agency reports. The regular update cadence is monthly standard reports. Temporary analysis documents are added when industry policies adjust, raw material prices fluctuate, or downstream demand changes unexpectedly.
Document structure includes fields such as carbon steel grade, specification parameters, ex-factory price, production capacity scale, cost composition, and breakdown of downstream application areas. Units include yuan/ton, ten thousand tons, millimeters, and others. The word count per document varies widely. Some documents include multiple structured data tables, with mixed text and data presentation.
Constraints on Vector Models and Indexes
The mixed text-structured characteristics, multi-field attributes, and non-fixed update cadence of carbon steel research reports impose multiple constraints on the vector model and indexing workflow.
First, documents contain large numbers of structured data tables. The chunking process must retain the relevance of fields within tables, to avoid semantic fragmentation after splitting. Second, multi-dimensional business fields require the vector model to have cross-field semantic alignment capabilities, to identify associated information such as grades, specifications, and prices across different research reports. Third, the non-fixed update frequency requires the index to support incremental refresh. It must adapt to temporarily added analysis documents without full index reconstruction, while controlling performance overhead during index expansion.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Carbon steel research reports contain mixed text and table content. This range retains business relevance within a single chunk, avoiding semantic fragmentation caused by splitting table fields |
embedding_model | Qwen3-Embedding-8B or open-source multimodal embedding models of similar scale | The structured fields and mixed text features of carbon steel research reports are prominent. Multimodal embedding models can better align the semantic association between tables and text |
milvus_index_type | IVF_SQ8 | The data volume of carbon steel research reports fluctuates with updates. This index type balances recall accuracy and write performance, adapting to non-fixed update cadences |
recall_top_k | Top 10–15 results | A single carbon steel research report contains multi-dimensional business information. A sufficient number of retrieved chunks must cover different downstream business query scenarios |
parse_table_mode | Retain complete table structure | Structured data tables in carbon steel research reports are core business information. After splitting, the relevance of fields must be retained to avoid semantic loss |
vector_db_refresh_strategy | Incremental refresh | Updates to carbon steel research reports are non-fixed. Incremental refresh reduces resource consumption and time overhead from full index reconstruction |
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 case-by-case analysis. It is recommended to conduct testing using applicable samples before finalizing settings.
Three Common Misconfigurations
- Issue: Milvus fails to start when the compose file includes PostgreSQL-related configurations, with an error indicating a database connection failure. Cause: The default database configuration in the official compose file was not replaced. Vector indexes for carbon steel research reports do not require additional PostgreSQL dependencies. Redundant configurations cause startup conflicts.
- Issue: Connection timeouts or authentication failure errors occur when connecting to a Qwen3-Embedding-8B model hosted by a locally deployed VLLM. Cause: The model's API port and access key were not configured correctly. Embedding tasks for carbon steel research reports are sensitive to model response latency. Default timeout settings are insufficient to cover full document vectorization.
- Issue: Unrelated carbon steel grades or specification fields appear in retrieval results, and the number of recalled entries does not match the configured value. Cause:
parse_table_modeandchunk_sizewere not set correctly. Document chunking destroyed the relevance of business fields, and the vector index did not correctly associate text and structured data.
How to Verify Proper Configuration
- Local embedding test tasks can be executed. A single carbon steel research report can be uploaded, and the number of segments output in the console can be checked to match the range specified in the
chunk_sizeconfiguration. - The vector database console can be accessed to confirm that the index type matches the
milvus_index_typeconfiguration, and that the index refresh log indicates normal incremental update triggers. - Queries targeting carbon steel grades or downstream application areas can be submitted, and the business fields included in the retrieved results can be verified to match the original document content.
- The knowledge base storage statistics panel can be viewed to confirm that the storage usage of each storage type can be individually identified.
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.