What the Data for This Category Looks Like
Data sources for commercial real estate investment research include official real estate registration filing data, commercial project operation ledgers, business district passenger flow monitoring systems, industry association research documents, and publicly disclosed project materials from real estate enterprises. Update cycles vary: quarterly updated project operation data, monthly updated business district supporting facility change data, and annual updated industry trend reports. Most documents are structured tables, graphic-text reports with location annotations, and operation log ledgers. Fields include project location coordinates, rental unit price, rental occupancy rate, floor area, equipment maintenance cycle, and more. Common units are square meters, yuan per square meter per day, calendar days, and similar units.
What Constraints Do These Characteristics Impose on Model Access and Configuration?
The characteristics of commercial real estate investment research data create multiple constraints for model access and configuration:
- A high proportion of structured tables requires the parsing module to support accurate field extraction, to avoid loss of core data from unstructured parsing.
- Large differences in update cycles require the knowledge base synchronization cycle to be flexibly adjusted to fit the update frequencies of different data sources.
- Quantitative fields with specific units require the embedded model to cover the semantics of units commonly used in commercial real estate, to avoid vector matching deviations.
- A high proportion of graphic-text reports requires the accessed model components to support multi-modal information association processing.
How to Set the Configuration
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Commercial real estate reports mostly consist of long structured text. This length preserves core fields such as complete rental calculations and location supporting facilities, and avoids breaking semantic associations through splitting |
similarityThreshold | 0.72–0.85 | Commercial real estate data has strong correlation between units and fields. A threshold that is too low will introduce irrelevant project data, while a threshold that is too high will fail to recall comparable projects of the same category |
rerankTopN | Top 3–5 entries | Investment research reports require comparison of multiple groups of comparable projects. A small number of highly relevant entries can support analysis, while an excessive number will increase context load |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Graphic-text reports for large commercial projects have large file sizes. This timeout range ensures complete data extraction |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Commercial real estate project archives include multiple supporting drawings and operation ledgers. Allowing large file uploads reduces operational costs from split uploads |
embeddingModel | Embedding model that supports multi-modal processing and unit semantics | Commercial real estate data includes graphic-text content and quantitative units. A compatible model improves the accuracy of vector recall |
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. Testing on own samples is recommended before finalizing settings.
Three Common Mistakes
- Phenomenon: Model calls return status code 413, and logs show file parsing failure. Cause: The
UPLOAD_FILE_MAX_SIZEconfiguration is not adjusted. Commercial real estate project archives that exceed the default threshold cannot complete upload and parsing. - Phenomenon: Abnormally high or low token consumption is observed during statistics. Cause: The
chunkSizeconfiguration is not adjusted for the long-text structured data of commercial real estate. Too fine or too coarse splitting leads to token count deviations, and consumption is not counted according to actual segments. - Phenomenon: Locally deployed Ollama or VLLM models fail to recall data normally after access. Cause: The API endpoint and authentication parameters of the local service are not correctly filled in the model access configuration, or the context window length supported by the model is not matched, leading to request rejection or invalid results returned.
How to Confirm the Configuration Is Valid
- A typical commercial real estate project report is uploaded. Parsed structured fields are checked for full extraction, and the
chunkSizeconfiguration is verified against the semantic integrity of split documents. - A similar project recall test is initiated. The relevance of returned results is checked against business requirements, and
similarityThresholdandrerankTopNare adjusted to match expected recall quantity and quality. - Model call logs are reviewed. Token consumption statistics are confirmed to match actual document length, and the impact of the
chunkSizeconfiguration on consumption calculation is verified. - The local deployed model access process is tested. API endpoints and authentication parameters are confirmed to be correctly configured, and the model is verified to return vectors or generated results normally.
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.