What the data for this category looks like
Financial report data for the special equipment category comes primarily from public annual, semi-annual, and quarterly reports of listed companies, plus monthly operational monitoring data released by industry associations. Quarterly reports are published within one month after the end of each quarter. Annual reports are disclosed by the end of April of the following year. Industry monthly data is updated in the middle of each month. Most documents are structured PDF reports, with fields including revenue, backlog orders, capacity utilization rate, unit product cost, and more. Common units are ten thousand yuan, units, and percentage points. Some subcategories include specialized statistics such as equipment delivery volume and operating rate.
Constraints on vector models and indexing
The multi-source data characteristics of special equipment financial reports create multiple constraints for the vector models and indexing workflow. First, financial and industry data contains many specialized fields. Select an embedding model adapted for the industrial manufacturing sector to avoid semantic misunderstanding errors for specialized terms by general-purpose models. Second, batch updates of quarterly financial reports and high-frequency refreshes of monthly industry data require indexes to support incremental construction by time dimension. This reduces resource consumption from full reindexing. Third, associated fields in structured reports require configured association indexing rules to improve recall relevance for same-dimension data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | bge-large-zh-v1.5 or text-embedding-3-large | Both models have strong semantic encoding capabilities for industrial manufacturing specialized terms, and can accurately match specialized fields in special equipment financial reports |
chunk_size | 800–1200 characters | Structured table paragraphs in special equipment financial reports are mostly medium-to-long texts. This segment length preserves semantic associations between fields |
retrieval_top_k | Top 8–10 results | Specialized financial reports have many associated fields. A sufficient number of candidate results must be recalled to ensure core information is not missed |
index_refresh_interval | 1 day | Matches the update frequency of industry monthly data. Daily refresh balances real-time performance and computing resource consumption |
similarity_threshold | 0.72–0.78 | Semantic matching accuracy for specialized terms is high. This range filters low-relevance generalized matching results |
index_incremental_mode | Triggered by timestamp | Financial and industry data updates follow fixed time nodes. Using timestamps accurately identifies new data and avoids full reindexing |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing values.
Three common misconfigurations
- Symptom: No matching data is returned during retrieval after index construction. Cause: No timestamp matching rule for incremental indexing is configured, causing newly added quarterly financial reports or monthly industry data to not be correctly written to the index.
- Symptom: The final split text fragment in the knowledge base cannot be recalled during retrieval. Cause: Chunk size is configured too small, causing semantic associations between specialized fields to be broken during splitting, making vector encoding unable to match core semantics.
- Symptom: Overall relevance of retrieval results is low, with many generalized results. Cause: A general-purpose embedding model is selected, and encoding is not optimized for industrial specialized terms, leading to insufficient semantic matching accuracy for specialized fields.
How to verify proper configuration
- Navigate to the vector configuration page of the knowledge base, check the
embedding_modelvalue, and confirm the selected model is adapted for industrial specialized text scenarios. - Upload a test segment of a special equipment financial report, trigger a manual index update, wait for the preset refresh interval, then perform targeted keyword retrieval to confirm the test data can be normally recalled.
- Adjust the similarity threshold value, observe the number of returned retrieval results, and determine the final threshold based on business requirements for relevance.
- View the index update log to confirm that the incremental update trigger timing matches the release schedule of financial and industry data.
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.