What the data for this category looks like
Commercial real estate investment research data mainly comes from property operation management systems, merchant cooperation ledgers, business district passenger flow monitoring platforms, and on-site operation work order systems. Data update cycles vary significantly. Merchant contract and rent pricing data is updated per signing cycle. Energy consumption and passenger flow data is updated daily. Operation work orders are updated when events are triggered.
Documents include structured ledgers with fields such as property number, building area, and rent unit price, semi-structured operation records, and unstructured inspection images. Most fields relate to spatial parameters and performance clauses. Units include square meters, yuan per square meter per month, performance bond ratio, and similar metrics.
What constraints these characteristics impose on model access and configuration
The characteristics of commercial real estate data impose three types of constraints on model access and configuration. First, multi-field structured ledgers contain proprietary professional terms for properties, such as internal floor area and performance bond ratio. Connected embedding models must adapt to Chinese commercial real estate professional semantics to reduce semantic recall bias. Second, data update cycles vary significantly. Rent contract data has few changes, while energy consumption and passenger flow data is updated frequently. Incremental synchronization rules triggered by data type must be configured to avoid wasted computing resources from full updates. Third, the data includes unstructured inspection images and work order text. Both text and multimodal model access links must be configured to cover parsing needs for all data types.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
embedding_model | bge-large-zh-v1.5 or same-scale Chinese professional embedding model | Commercial real estate contains a large number of proprietary terms. This model delivers good semantic alignment for professional text |
max_context_length | 8192 | Commercial real estate contract documents are usually lengthy, requiring adaptation to long-text context processing needs |
retrieval_top_k | Top 8-12 entries | Structured ledger fields have high correlation. Too many recall results introduce redundant information, while too few fail to cover all relevant fields |
sync_interval | Tiered by data type: every 7 days for contract data, every 1 hour for energy consumption data | Different data types have vastly different update frequencies. Tiered synchronization balances real-time performance and resource consumption |
multimodal_enabled | Enabled | Commercial real estate investment research requires combining unstructured data such as inspection images and on-site photos. Multimodal model access links must be enabled |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large property contracts or operation work order documents takes a long time. The default timeout may be insufficient |
The parameter values provided on this page are all common recommended starting points for determining configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: A 404 error is returned when configuring OneAPI to access Baidu embedding-v1 or a locally deployed
bge-large-zh-v1.5. Cause: The calling endpoint of the corresponding model was not correctly configured in the OneAPI model management interface, or the model was not bound to a valid API key group. - Phenomenon: Significant differences in results occur when using workflow templates for merchant type or property format classification. Cause: The large model temperature parameter was not adjusted for commercial real estate professional terms, or a dedicated prompt was not configured to guide the model to recognize proprietary fields such as internal floor area and performance bond ratio.
- Phenomenon: The platform prompts that custom models are not supported when attempting to access a locally deployed private model. Cause: Custom model access permissions were not enabled in system settings, or model API path and key verification configuration was not completed according to platform requirements.
How to confirm configuration is complete
- Run a semantic recall test for structured ledgers. Check whether returned results include proprietary property fields to confirm embedding model adaptability.
- Trigger an incremental synchronization task. Check whether synchronization intervals for different data types match preset rules to confirm synchronization configuration validity.
- Upload a mixed document of property inspection images and work orders. Check whether text and image content can be parsed simultaneously to confirm multimodal model access links are working properly.
- Call the workflow classification template, input test text containing proprietary terms. Check consistency of classification results to confirm large model parameter and prompt configuration rationality.
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.