What the Data for This Category Looks Like
Commercial real estate research report data primarily comes from public industry consulting reports, internal operational data from real estate enterprises, and public statistical materials from local housing and urban-rural development departments. Update cycles are primarily quarterly. Benchmark project research reports for some key cities are updated monthly. Most documents use long text format. They include fields such as project location parameters, rental unit price, vacancy rate, competitor business format layout, and regional policy interpretation. Most fields have clear units, such as square meters, yuan/square meter/month, and percentage.
Constraints for Vector Models and Indexing
The long text structure of commercial real estate research reports requires vector models to support long context segmentation. This prevents key information from being truncated. Fields with clear units require vector models to preserve unit-linked features. This avoids vector shifts for similar data with different units. The primarily quarterly update cycle requires indexes to support incremental refresh. This reduces resource costs from full index rebuilding. Variations in fields across multi-source data require index configurations to support field mapping rules. This standardizes retrieval standards.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
embedding_model | text-embedding-ada-002 | Adapts to mixed structured numerical and text content in commercial real estate research reports, with stable semantic encoding consistency |
chunk_size | 800–1200 characters | Matches the length range of core information per segment in research reports, avoiding semantic breaks from long text truncation or overly fragmented segmentation |
chunk_overlap | 50–100 characters | Retains semantic continuity content between segments, preventing key information from being lost at segment boundaries |
recall_top_k | 10–15 results | Covers multi-dimensional project, policy, and competitor information in research reports, balancing retrieval accuracy and response speed |
index_refresh_interval | 7 days or 30 days | Adapts to the primarily quarterly update cycle of research reports. High-frequency regional data can be configured for 7-day refresh to reduce resource consumption |
vector_similarity_threshold | 0.72–0.8 | Adapts to the strong correlation characteristics of commercial real estate data, filtering irrelevant matches while retaining valid retrieval results |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Individual retrieval requests time out, with latency exceeding normal ranges. Cause: No reasonable
chunk_sizeconfigured. Overly long segments increase vector encoding computation load, or an excessively highrecall_top_kvalue exceeds the vector database's processing capacity. - Symptom: The console reports "no retrievable content" after index completion. Cause: Core research report fields were not added to the parsing whitelist, or
embedding_modelwas not configured correctly, leading to failed vector encoding and no valid vectors stored in the database. - Symptom: Retrieval results include large amounts of non-commercial real estate content. Cause:
vector_similarity_thresholdis set too low, or non-target category fields were not filtered, leading to semantic matching deviations.
How to Verify Proper Configuration
- Run a vector encoding test for a single research report. Confirm that the vector dimension generated in the vector database matches the output dimension of the selected
embedding_model. - Trigger an incremental index refresh. Confirm that the number of documents refreshed in the index log matches the actual number of updated research reports.
- Input a retrieval query containing rental and location keywords. Verify that returned result fields include the search keywords and align with commercial real estate scenarios.
- Confirm that the vector database connection is normal. The number of stored vectors must match the number of indexed documents.
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.