What the data for this category looks like
Residential development industry financial report data comes primarily from publicly disclosed annual reports, quarterly reports, and interim announcements. Updates follow two schedules: regular bulk disclosures and ad-hoc incremental updates. Each individual document includes modules covering full project development cycle cost accounting, land reserves, revenue composition, cash flow management, and more. It contains both structured tabular data and unstructured business description text. Fields include gross floor area included, per-square-meter construction cost, pre-sale revenue collected, ending inventory balance, and similar metrics. Units follow industry standard measurements such as square meters, yuan per square meter, ten thousand yuan, and other industry-accepted units. Document formats vary slightly across different developers.
What constraints these characteristics impose on vector models and indexes
The multi-module structure and long-text nature of residential development financial reports require vector chunking to preserve semantic integrity for both structured tables and unstructured paragraphs. This avoids splitting content that carries critical business relationships. The mixed update schedule of regular and ad-hoc updates requires indexes to support a hybrid strategy of incremental synchronization and full reconstruction. This balances update efficiency and data consistency. The high volume of industry-specific terminology requires vector models to have professional semantic understanding capabilities. This prevents generic models from misinterpreting industry terms. The large size of individual documents requires reasonable control over chunking granularity. This prevents semantic fragmentation of context.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Matches the content length of long business paragraphs and core single tables in residential development financial reports, avoiding semantic fragmentation |
chunk_overlap | 100–150 characters | Balances context coherence and index redundancy, covering cross-paragraph business association logic in financial reports |
embedding_model | dengcao/Qwen3-Embedding-8B:F16 | Supports semantic understanding of residential development industry-specific terminology, and supports local deployment to meet privacy compliance requirements |
index_update_strategy | Incremental updates + full regular synchronization | Aligns with the quarterly incremental updates and annual full updates of financial reports, balancing update efficiency and data consistency |
recall_top_k | Top 8–12 results | Focuses on core business modules of residential development financial reports, avoiding recall of excessive irrelevant content |
similarity_threshold | 0.72–0.78 | Distinguishes semantic similarity between industry-specific terminology and general text, reducing false recall probability |
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 settings.
Three common configuration mistakes
- Issue: pgvector index queries return far fewer results than expected. Cause: Failure to set reasonable chunking parameters for the long-text and multi-module structure of residential development financial reports. This leads to semantic loss after critical business paragraphs are split.
- Issue: The console returns a
token count exceed limiterror. Cause: Failure to count tokens according to the field structure of residential development financial reports. Direct chunking of entire documents causes individual chunks to exceed the model's token limit. - Issue: Index disk usage exceeds the preset threshold. Cause: Failure to limit
chunk_overlapto a reasonable range. This leads to a large volume of duplicate vector data being written to the index.
How to confirm configurations are properly set
- Upload a single residential development financial report sample. Check that chunked text covers core business modules with no obvious semantic fragmentation.
- Run an incremental update test for an interim announcement. Verify that new data can be indexed normally without triggering a full reconstruction process.
- Adjust the
similarity_thresholdvalue range. Compare recall results across different thresholds to confirm that industry-specific terminology can be distinguished from general text. - Check index storage usage. Verify that no meaningless redundant vector data is present.
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.