What This Category of Data Looks Like
Refining industry research report data primarily comes from industry association monthly monitoring reports, annual reports publicly released by refining enterprises, and detailed analysis documents from third-party professional consulting institutions. Update schedules fall into three categories: regular monthly updates, quarterly in-depth reviews, and temporary supplements for emergency events. Document structures include fixed sections: crude oil processing volume, plant operating rate, yields of various refining products, cost accounting, market supply and demand forecasts, policy impact analysis, and more. Most fields come with dedicated units such as tons, yuan per ton, and percentage.
Constraints for Vector Models and Indexing
Refining research reports contain structured process parameters, semi-structured analysis paragraphs, and lengthy market forecast content. Vector models must balance accurate short-field encoding and long-text semantic understanding to support this data. Research reports use multiple update frequencies. Indexing systems must support incremental synchronization to reduce full-reconstruction load. Most fields include fixed units. Vector encoding must preserve unit association data to prevent semantic confusion. Individual reports are lengthy. Segmentation processing must balance context integrity and indexing efficiency.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Matches the length of process parameter descriptions and analysis paragraphs in refining research reports, preserves context associations |
chunk_overlap | 150–200 characters | Prevents segmentation from breaking the logical connection between plant parameters and corresponding analysis, reduces the risk of retrieval gaps |
vector_model | bge-large-zh-1.5 | Adapts to the semantics of Chinese professional research reports, has an embedding dimension of 1024 compatible with most vector storage components, supports calls for version 4.8.7 |
top_k | Top 10–15 results | Covers the multi-dimensional parameters, market data, and analysis content required for professional refining industry retrieval |
similarity_threshold | Calibrated based on actual testing | Filters low-relevance non-professional content, matches the retrieval accuracy requirements of the refining industry |
index_refresh_interval | 1 hour | Adapts to the regular monthly update schedule, balances indexing load and data freshness |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: The interface only displays a single vector model selection option, and it is not possible to configure dedicated vectors for structured fields. Cause: The multi-vector indexing function is not enabled, and vectors are not generated separately for structured parameters and unstructured text in refining research reports.
- Phenomenon: Retrieval results deviate or fail to return valid content when different vector models are used locally and on the server. Cause: Vector embedding dimensions and text preprocessing rules are not unified. Different models have different vector space mapping logic.
- Phenomenon: A single long research report remains in the indexing state indefinitely and cannot complete the indexing process. Cause: A reasonable
chunk_timeoutparameter is not set, leading to timeout failure during long text segmentation processing, or theindex_max_sizelimit is not adjusted, causing indexing blocking.
How to Confirm Proper Configuration
- Upload a single sample refining research report, check whether the segmentation results cover core sections such as process parameters and market analysis, with no obvious truncation or redundant content.
- Initiate a retrieval using refining industry professional keywords, check whether the similarity scores of returned results meet the preset threshold, and include relevant plant parameters or market data.
- Trigger an incremental indexing task, check whether the indexing progress completes within a reasonable time frame, with no persistent hanging status.
- Compare the embedding dimension parameters of the vector model locally and on the server, confirm that the values are consistent and meet the configuration requirements.
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.