What Data Looks Like for This Category
Data sources include periodic reports and temporary announcements publicly disclosed by listed entities, as well as publicly available industry operation data. Update cadence: quarterly reports are updated every 3 months, annual reports are updated once per year, and major contract announcements are updated when events occur. Document structure includes structured financial statements such as revenue, cost, and R&D expense breakdowns, unstructured business description text, and attached tables related to equipment delivery and production capacity. Fields include operating revenue, operating costs, R&D expenses, outstanding order amounts, and equipment production capacity-related values, mostly denominated in ten thousand RMB and units/sets.
Constraints for Vector Models and Indexing Workflows
The mixed structured and unstructured document structure requires vector indexes to support both semantic vector retrieval and structured field filtering, to avoid business dimension bias caused by relying solely on semantic matching. Data sources with multiple update cadences (quarterly periodic reports + temporary event announcements) require indexes to support an update strategy combining incremental synchronization and scheduled full refresh, to balance timeliness and resource usage. The multi-field attributes of equipment-related attached tables require vector model inputs to support long text spliced from multiple fields, to avoid loss of key business information. The wide variation in single document length of financial report documents, from dozens of pages of annual reports to single-page temporary announcements, requires the index's segmentation strategy to adapt to text units of different lengths, to avoid truncation of key business descriptions.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | The business description text in automated equipment financial reports is mostly coherent business analysis. This segmentation length preserves complete business logic within a single segment and avoids truncation of key information |
chunk_overlap | 100–150 characters | The business description text in financial reports has strong coherence. This overlap length avoids losing contextual connection after segmentation |
embedding_model | Determined based on actual testing | Adapts to the mixed vectorization needs of structured field spliced text and unstructured business descriptions in automated equipment financial reports |
retrieval_top_k | Top 10–15 results | The retrieval needs for automated equipment financial reports mostly target revenue and order data for specific business segments. This number of recalled results covers core relevant outcomes |
index_refresh_strategy | Incremental synchronization + daily full refresh | Balances the batch update needs of quarterly periodic reports and the real-time requirements of temporary announcements, and avoids resource consumption caused by full index rebuilding |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Automated equipment financial reports include multi-page attached tables, which take a long time to parse. This duration avoids parsing timeout interruptions |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on samples relevant to the specific use case before finalizing settings.
Three Common Misconfigurations
- Only vector data is stored in the vector database, with no original document content. This occurs because original text storage configuration is not enabled, and only vectorized feature data is synchronized. This prevents subsequent retrieval from associating with the original financial report content.
- Index construction may get stuck, or the retrieval interface may return a 500 error after a newly configured embedding model completes indexing. This occurs because the matching between the input length of the embedding model and the vector dimension of the vector database is not verified, or model call timeout parameters are not configured, leading to request timeouts or dimension incompatibility.
- Semantic retrieval may return similarity scores that are consistently higher than reasonable threshold ranges. This occurs because structured fields in financial reports are not separately encoded or filtered, causing redundant numeric fields to inflate the results of semantic similarity calculations.
How to Verify Correct Configuration
- Check the stored content in the vector database, confirm that both original text fields and vector fields exist, and verify whether original text storage is enabled in the configuration.
- Submit a sample automated equipment financial report for index construction, check that the index progress is not stuck, the retrieval interface returns normal result lists, and verify that there are no dimension mismatch errors in the model call logs.
- Conduct retrieval for specific business keywords, adjust the similarity threshold and number of recalled results, confirm that the returned results match the business content of the financial report, and verify that the retrieval logic adapts to the field characteristics of this category.
- Trigger an incremental update task, check whether newly added temporary announcements are synchronized to the index, and verify that the update strategy configuration is effective.
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.