Vector Models and Indexing for Thermal Coal Research Report

What the Data for This Category Looks Like

Data sources for thermal coal research reports include domestic coal industry monitoring platforms, public futures market data, and professional energy institution research reports. Update rhythms fall into three categories: regular market data is updated daily, industry monthly analysis reports are released monthly, and reports related to sudden policy or supply-demand changes are updated on an event-triggered basis. Document structure includes core market indicators, supply-demand balance analysis, policy interpretations, and regional transportation data, with publisher and publish time fields attached. Core indicators include calorific value and flat price, with corresponding units of kilocalories per kilogram and yuan per ton.

Constraints on Vector Models and Indexing

Thermal coal research reports contain structured market indicators and unstructured analysis content, with large differences in update frequencies. This creates multiple constraints for the vector models and indexing link. Structured numerical indicators need to be retrieved in association with unstructured text, requiring support for mixed vectors and metadata filtering. High-frequency updated daily market data and event-triggered reports need incremental index update mechanisms to avoid excessive time consumption from full index reconstruction. Report length varies widely, from hundreds-of-word short comments to ten-thousand word deep reports. Segmentation processing must retain context association to prevent damage to industry analysis logic from improper splitting. Some retrieval needs target clear numerical indicators, so vector models must adapt to numerical semantic understanding, while supporting rapid filtering by publish time and publisher metadata.

How to Set Configurations

Configuration Item	Recommended Value	Rationale
`chunk_size`	800–1200 characters	Covers single-segment industry analysis logic, retains structured indicators and context association, avoids splitting that compromises analysis integrity
`retrieval_top_k`	Top 10–15 results	Adapts to precise retrieval needs in thermal coal research report scenarios. Too many recalls introduce irrelevant content, too few lead to insufficient coverage
`metadata_filter_enable`	Enabled	Supports filtering by publish time, publishing organization, and report type, quickly narrows retrieval scope, improves recall accuracy
`embedding_model`	bge-large-zh-v1.5	Adapts to Chinese industry text and numerical semantic understanding, delivers stable vector representation for energy domain terminology
`index_refresh_interval`	Every hour	Adapts to the update frequency of daily market data, balances index update overhead and data freshness
`PARSE_FILE_TIMEOUT_SECONDS`	600 seconds	Adapts to long document parsing needs, prevents import failure of large deep research reports due to parsing timeout

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, and it is recommended to test on your own samples before finalizing.

Three Common Mistakes

Phenomenon: Some structured indicator fields are empty after importing research reports, and filtering by indicators is not possible during retrieval. Cause: metadata_extract_rules are not configured, and metadata extraction rules are not set for exclusive fields of thermal coal research reports (such as calorific value, flat price), leading to failure to correctly identify fields.
Phenomenon: Retrieval results contain a large amount of duplicate content, and the number of entries does not match the set retrieval_top_k. Cause: The chunk_overlap parameter is not adjusted. Excessive segment overlap leads to multiple recalls of the same content, and result deduplication configuration is not enabled.
Phenomenon: Index construction times out in a local deployment environment, and the log shows 504 Gateway Timeout. Cause: The PARSE_FILE_TIMEOUT_SECONDS parameter is not set reasonably, and segmented parallel processing is not enabled for long documents, leading to single-document parsing time exceeding the threshold.

How to Confirm Proper Configuration

Upload a standard thermal coal short comment and a deep research report, check if the number of segmented chunks meets expectations, with no obvious splitting errors.
Initiate a retrieval request that includes structured indicators, verify that the metadata filtering function works, and narrow the retrieval scope by publish time or organization.
Check the index status of the vector database, confirm that the incremental update task is executing normally according to the configured index_refresh_interval.
Simulate high-frequency retrieval requests, check if response delay meets scenario requirements, and adjust corresponding parameters based on actual performance.

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.