What the data for this category looks like
Data for computer equipment financial reports comes primarily from public annual and quarterly financial report notes disclosed by listed companies, plus temporary announcements for equipment purchases. Updates are concentrated at the end of each quarter and year. Temporary purchase announcements are released alongside their disclosure timelines. Most documents use structured tables nested with note explanations, including fields such as original equipment value, accumulated depreciation, book net value, deployed quantity, service life, and single-unit purchase cost. Common units are ten thousand RMB, units, and years. Some disclosures of overseas business equipment data include foreign currency translation explanations.
Constraints on vector models and indexing
Structured tables make up a large share of computer equipment financial reports, and all fields have clear units. This requires the indexing workflow to support mixed indexing of structured fields and unstructured notes, to avoid semantic confusion between units and numerical values. Batch data updated quarterly or annually requires the indexing system to support concurrent batch processing, while limiting per-batch data volume to avoid timeouts. Incremental release of temporary announcements requires indexing to support incremental synchronization, without full reindexing, to cut down on repeated computing resource use. Some cross-currency translation data requires vector models to adapt to multi-unit semantics, ensuring equipment cost data across different currencies can be correctly associated and retrieved.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Structured table rows and note explanations in computer equipment financial reports have moderate length. This range preserves full semantic integrity for a single equipment detail or single note segment |
embedding_model | text-embedding-3-small or bge-large-zh-v1.5 | Supports multi-field semantic encoding, accurately associates equipment values with units, and adapts to the field structure of structured financial reports |
index_batch_size | 20–30 items/batch | Matches the resource limits of an 8c16G host without a GPU, preventing excessive memory usage that causes indexing failures |
similarity_threshold | 0.75–0.85 | Fields such as equipment models and purchase costs in computer equipment financial reports have high semantic similarity differentiation. This range filters out low-relevance search results |
incremental_index_enable | Enabled | Meets incremental update requirements for temporary purchase announcements, reducing resource consumption from full indexing |
parse_structured_table | Enabled | Structured tables make up a large share of computer equipment financial reports. Enabling this option extracts the correspondence between fields and values, improving search accuracy |
The parameter values provided on this page are common 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. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Issue: Knowledge base indexing fails when a PostgreSQL database is deployed via Docker, on an 8c16G host without a GPU. Cause: The index batch size is set too high. Per-batch data volume uses more memory than the host’s available resources, triggering an out-of-memory error or process termination.
- Issue: Vector model call errors occur, with prompts indicating mismatched model return dimensions or call timeouts. Cause: The indexing model is not configured separately. The general call link is used, without adapting to the dedicated interface or rate limits of the indexing model.
- Issue: A large number of irrelevant equipment model data appear in search results, and the number of recalled entries does not meet expectations. Cause: Unreasonable similarity threshold setting, or the structured table parsing switch is not enabled, so field semantic association is not properly distinguished.
How to confirm correct configuration
- Upload a single structured table fragment from a computer equipment financial report, view the parsed field list, and confirm the structured table parsing switch is active, with fields such as original equipment value and service life correctly extracted.
- Run a batch indexing test using the preset per-batch data volume configuration, check the host’s memory usage, and confirm resource usage aligns with expectations.
- Enter a search term such as "server purchase cost", review the ranking of search results and field matching degree, and adjust the similarity threshold to a range that meets business requirements.
- Upload a temporary purchase announcement file, confirm the indexing system automatically completes incremental synchronization without triggering a full index rebuild.
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.