What the Data for This Category Looks Like
Coal chemical financial report data mainly comes from annual, semi-annual, and quarterly reports publicly disclosed by listed companies at home and abroad, as well as monthly operation briefings released by industry associations. The data update rhythm follows fixed quarterly and annual disclosures, with temporary announcements released simultaneously with major business changes. The document structure includes standardized financial statement modules and coal chemical-specific business modules. Fields include but are not limited to coal extraction volume, methanol production capacity, olefin output, comprehensive energy consumption per ton of product, etc. The unit for energy consumption fields is kg standard coal/ton, the unit for capacity and output fields is 10,000 tons/year, and the unit for financial revenue fields is 10,000 RMB. A single annual report can reach tens of thousands of words, containing a large number of industry-specific terms and structured data.
Constraints Imposed on Vector Models and Indexing
The multi-module structure of coal chemical financial reports requires vector models to adapt to both structured financial fields and unstructured business text, and perform semantic alignment for industry-specific terms such as "coal-to-olefins" and "comprehensive energy consumption". The fixed-cycle update feature requires indexes to support incremental updates to avoid resource consumption caused by full reconstruction. The characteristics of long texts and dense professional terms require segment length settings to balance semantic integrity and retrieval accuracy, avoiding semantic breaks across paragraphs. The clear unit requirements for structured fields require retaining semantic associations of fields during vectorization, preventing loss of unit information from affecting subsequent retrieval matching.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Ensures semantic integrity of single-segment business descriptions or financial notes in coal chemical financial reports, avoiding truncation of professional terms and logic by segmentation |
chunk_overlap | 50–100 characters | Retains contextual association between segments, ensuring continuous business logic in financial reports is not split |
recall_top_k | Top 10–15 results | Covers multi-dimensional financial and business data in coal chemical financial reports, avoiding missing key analysis bases due to insufficient recall |
vector_db_batch_size | 200–300 items/batch | Balances vector database insertion efficiency and server memory usage, preventing memory overflow from excessively large single batch data |
index_update_strategy | Incremental update + full verification | Adapts to the quarterly update rhythm of financial reports, reduces resource consumption of full index reconstruction, and regularly verifies data consistency |
similarity_threshold | 0.75–0.85 | Distinguishes semantic similarity of same-type business data in coal chemical financial reports, filtering irrelevant recall results |
The parameter values provided on this page are conventional recommendations used as a starting point 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 Errors
- Phenomenon: Vector database data migration fails after upgrading to a new version, with a prompt indicating data format incompatibility. Cause: No vector format adaptation was performed for structured exclusive fields of coal chemical financial reports (such as comprehensive energy consumption per ton of product), and the field mapping rules of the old vector database are inconsistent with the new version.
- Phenomenon: Calling the voyage index interface returns a 400 status code with no body. Cause: Structured field vectors of coal chemical financial reports were not passed in accordance with interface requirements, the request body lacks industry-specific parameters, or field units were not formatted according to specifications.
- Phenomenon: After local deployment, when creating a knowledge base vector via the interface, server memory continues to occupy a high level, triggering OOM daily. Cause: The
vector_db_batch_sizeparameter was not configured, the single batch vector data for insertion exceeds the server memory limit, and the incremental update strategy was not enabled, loading too much financial report data during full index reconstruction.
How to Confirm Proper Configuration
- Upload a single coal chemical annual report, check whether the length of the segmented results matches the preset
chunk_sizerange, with no obvious semantic breaks or truncated professional terms. - Run an incremental update task, verify that only newly uploaded financial report data is synchronized to the vector database, and old data is not accidentally overwritten or deleted.
- Initiate a retrieval request containing coal chemical-specific terms, confirm that the returned results include corresponding business fields (such as methanol production capacity, energy consumption per ton of product), and the number of results matches the preset
recall_top_krange. - Check server monitoring metrics, confirm that memory usage during vector database insertion does not exceed the preset threshold, with no abnormal continuous growth.
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.