Data Characteristics of This Category
Coal chemical industry investment research data primarily comes from securities firm industry reports, production logs from coal processing links, futures and spot trading quotes, capacity regulation policy documents, and financial report sections of listed companies. Update frequencies vary significantly. Production logs and spot quotes are updated daily or weekly. Securities firm reports are released weekly or monthly. Policy documents are updated dynamically alongside industry regulation. Document formats include long-text reports, structured data tables, and quantified fields with units. For example, coke oven capacity is measured in tons per day, and methanol selling price is measured in yuan per ton. Some documents contain professional process parameters and formulas.
Constraints Imposed on Vector Models and Indexing
The multi-format structure and varying update frequencies of coal chemical investment research data create multiple constraints for the vector models and indexing workflow. Long-text reports require segmentation that matches the model's input length limits, to avoid losing logical connections between process steps after splitting. Structured tables and quantified fields with units require vector models that support joint encoding of numerical features and text semantics, or require separate extraction of quantified fields for feature enhancement. Frequently updated spot and production data require incremental indexing strategies to reduce resource consumption from full reindexing. Documents with dense professional terminology require vector models adapted to industrial domain semantics, to avoid semantic encoding errors.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Coal chemical industry reports often contain continuous process descriptions. This length preserves the logical integrity of single-process segments, and aligns with the input limits of most general-purpose vector models. |
batch_size | 16–32 | Coal chemical data includes many long texts. This range balances vector generation efficiency and memory usage, and prevents request timeouts for single batches. |
vector_store_index_type | HNSW | Coal chemical investment research data requires similarity retrieval with high recall rates. HNSW indexes balance retrieval speed and accuracy for million-scale vector databases. |
similarity_threshold | 0.72–0.85 | Professional terms in the coal chemical industry have high semantic similarity. This range filters irrelevant results while retaining valid recall. |
enable_incremental_index | Enabled | Spot and production data are updated daily. Incremental indexing reduces computational overhead from full reindexing. |
recall_top_k | 10–15 | Investment research analysis requires support from multiple related documents. This quantity covers information needs for most scenarios. |
The parameter values provided on this page are general recommendations to use as a starting point for configuration. Actual values are affected by document format, data volume, and business rules. Specific scenarios require individual analysis. It is recommended to test with your own samples before finalizing settings.
Three Common Misconfiguration Issues
- Phenomenon: The vector service returns a single vector result, or the API request only carries a single text chunk parameter. Cause: Batch processing segmentation parameters are not configured, so the system submits vectorization requests by default using a single chunk, and the batch processing logic is not triggered.
- Phenomenon: Unable to retrieve generated chunked index content via API, returns an empty array or 404 status code. Cause: Persistent storage configuration for chunked indexes is not enabled, or the query parameters do not specify the correct document ID and chunk offset.
- Phenomenon: After adding the Alibaba multimodal-embedding-v1 model, vector generation results have semantic deviations. Cause: The model-adapted input format is not configured, and professional terms and quantified fields in coal chemical data are not preprocessed, leading to poor model encoding performance.
How to Verify Proper Configuration
- Check vector generation logs to confirm the number of text chunks carried in a single request, and verify that it matches the configured batch size.
- Send a retrieval request, and check if the returned chunked index content includes expected document fragments and field information.
- Import a coal chemical industry professional document, verify the semantic consistency of vector generation results, and adjust the similarity threshold to a range that meets business requirements.
- Test the incremental indexing function. After importing new production data, check if the index database only updates newly added chunks and does not trigger full reindexing.
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.