Knowledge Base Retrieval and Recall for Coking Coal

What the data for this category looks like

Coking coal-related data sources include publicly available materials from domestic coking coal industry associations, Dalian Commodity Exchange coking coal futures delivery standard documents, daily price ledgers from spot traders, monthly production briefings from mining enterprises, and customs import and export clearance data. Update cadence follows this schedule:

Spot prices are updated daily
Futures delivery standards are revised every 1 to 2 years
Production briefings are updated weekly
Import and export data is updated monthly

Documents are divided into three categories:

Structured table documents, which include fields such as origin, price, and core quality parameters
Unstructured research report documents, which include supply and demand analysis and policy interpretation content
Standardized indicator description documents, which clarify coking coal quality requirements

Fields and their units are as follows:

Price unit: yuan per ton
Caking index: dimensionless value
Sulfur content: milligrams per kilogram
Ash content: grams per kilogram

Constraints on knowledge base retrieval and recall

The multiple data sources and differentiated update cadences of coking coal data require the retrieval system to support filtering recall results by update time. This adaptation supports daily incremental updates of spot price data. Structured table documents contain clear business fields, so retrieval must support field-level precise matching. Examples include conditional retrieval for origin and caking index. Unstructured research report content has a long length. A segmentation strategy must preserve the integrity of professional terminology to avoid splitting core analysis logic. Core quality parameters are standard industry retrieval keywords. The recall model must prioritize matching these professional fields while covering generalized text retrieval needs. The fixed format of standardized indicator documents requires the retrieval system to identify and extract parameter thresholds for result verification.

Configuration Settings

Configuration Item	Recommended Setting	Rationale
`recall count`	`top 20`	Coking coal data includes multiple types such as structured prices, quality parameters, and research reports. A sufficient candidate set covers retrieval needs across different business scenarios
`similarity threshold`	`0.72–0.80`	Semantic similarity of coking coal professional terminology is relatively high. A threshold that is too low introduces irrelevant results, while a threshold that is too high misses relevant industry data
`segment length`	`800–1200 characters`	Unstructured research report content has a long length. This segment length ensures the contextual integrity of professional terminology and avoids semantic fragmentation
`incremental update trigger interval`	`2:00 AM daily`	Spot price data is updated daily. This configuration ensures the timeliness of knowledge base data while avoiding resource consumption from full updates
`rerank return count`	`top 8`	Coking coal investment research requires balancing multi-dimensional data. Returning too many results exceeds the context window, while returning too few misses key analysis content
`field matching weight`	`0.3–0.5`	Precise matching of structured fields must be combined with semantic retrieval. This weight range balances the effects of precise matching and generalized retrieval

The parameter values provided on this page are all conventional recommendations, used as starting points for configuration settings. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to conduct tests on your own samples before finalizing settings.

Three Common Misconfigurations

A large number of non-coking coal coal category data appears in retrieval results. Cause: No field-level filtering rules are configured, and the retrieval scope is not limited to coking coal-specific data.
Knowledge base returns content that does not match the user's query. For example, a request for "coking coal port prices" returns mining production data. Cause: The similarity threshold is set too low, introducing low-relevance semantic matching results, or the segment length is set unreasonably leading to lost context.
After enabling the rerank model, only the single most similar result is returned. Cause: The rerank return count is incorrectly set to top 1, and not adjusted to a reasonable range adapted to coking coal data.

How to Verify Correct Configuration

After uploading coking coal-specific data, check the knowledge base update log to confirm that the incremental update task triggers at the preset interval.
Initiate a retrieval targeting coking coal quality parameters, verify that the retrieval results include relevant content filtered by field level, and confirm that the field matching weight configuration takes effect.
Test the number of recall results before and after enabling the rerank model, and confirm that the rerank return count configuration matches expectations.
Input a generalized coal category query, verify that the retrieval results only return coking coal-related data, and confirm that the filtering rules are configured correctly.

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.