Vector Models and Indexes for Coking Coal Research Report

What the data for this category looks like

Data sources for coking coal research reports primarily include monthly supply and demand reports from coal industry associations, market data from futures exchanges, and specialized analysis documents from securities firms and commodity information institutions. Update cadence falls into two categories: fixed-cycle weekly and monthly reports, and ad-hoc research reports triggered by sudden policy changes or price fluctuations. Document structure typically includes four core sections: core logical derivation, quantitative supply and demand data, downstream industry correlation analysis, and future market outlook. Fields include issuing organization, release date, main producing area output, port closing price, steel mill operating rate, and others. Units are mostly tons, yuan per ton, and percentage.

How These Characteristics Impact Vector Models and Index Workflows

The multi-data source and mixed update cadence of coking coal research reports require indexes to support flexible switching between incremental synchronization and full reconstruction, to avoid semantic deviation between old and new vector databases. A large number of structured numerical fields in documents require vector models to adapt to both professional terminology and quantitative data encoding logic. Failure to meet this requirement may result in terminology recall bias and loss of numerical semantic meaning. The length of individual research reports varies significantly, ranging from hundreds of words of market bulletins to ten-thousand-word in-depth analyses. This demands a chunking strategy that balances semantic integrity and contextual coherence, preventing excessive splitting that breaks industry logical connections. Additionally, some research reports contain cross-language content, such as foreign language citations related to international coal trade. This requires vector models to support multilingual encoding; otherwise, cross-language retrieval recall effectiveness will be compromised.

Configuration Settings

Configuration Item	Recommended Approach	Rationale
`embedding_model`	`Doubao-embedding-v2` or `local` (determined via actual testing)	Adapts to commodity industry professional terminology, delivering more stable encoding results for coal industry-specific terms. The `local` mode may be selected for on-premises deployments.
`chunk_size`	`800–1200 characters`	Coking coal research reports are dense with professional terminology. Overly long chunks break semantic connections, while overly short chunks lose upstream and downstream logical context.
`chunk_overlap`	`100–150 characters`	Compensates for contextual fragmentation caused by chunking, ensuring professional terminology appears intact in adjacent chunks.
`similarity_threshold`	`0.75–0.85`	Coking coal industry terminology has high recognizability. A threshold that is too low introduces irrelevant research reports, while a threshold that is too high misses valid content.
`rerank_top_n`	`Top 10 results`	Research report content is professional and relatively homogeneous. Secondary reranking further filters accurately matched retrieval results.
`reembed_batch_size`	`32–64`	Balances batch re-vectorization build speed and server memory usage, avoiding process blocking.

The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. Testing on in-house samples is recommended before finalizing settings.

Three Common Configuration Mistakes

Symptom: After switching embedding_model, knowledge base index progress stalls. The interface displays "Index Building" for longer than the preset duration without updates, and reversion to the original model configuration on the configuration page is not possible. Cause: The recreate_index operation was not performed first. The new model's vector database format is incompatible with the old model's, so the system locks model switching permissions. Additionally, reembed_batch_size was not configured, leading to excessive memory usage during full vectorization and triggering process blocking.
Symptom: After configuring the API key for Doubao-embedding, test vectorization tasks return a 404 page not found error. Cause: The correct API gateway address was not provided, or an outdated model version was used, causing requests to fail to route correctly.
Symptom: After batch re-vectorization, multilingual retrieval recall rate fails to meet expectations, and irrelevant results may appear. Cause: Language adaptation parameters of the vector model were not adjusted separately for multilingual fields, or a vector model that only supports single language was used, leading to deviations in multilingual semantic encoding.

How to Verify Proper Configuration

Access the knowledge base vector configuration page, confirm that the selected embedding_model and all preset configuration items are fully consistent, with no missing or incorrectly filled parameters.
Upload a single coking coal market bulletin to trigger the index building task. Wait for task completion, then check that the number of chunked text blocks aligns with the chunk_size configuration logic, with no excessive splitting or merging.
Initiate a targeted retrieval, input "coking coal main producing area output", verify that the sorting logic of returned results matches the rerank_top_n configuration requirements, and that results include relevant content for the target field.
Execute a batch re-vectorization task, check that background logs do not contain 404, out-of-memory, or process interruption errors, confirming normal task completion.

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.