What data for this category looks like
Data sources for commercial real estate intelligent due diligence reports include ownership filing data from real estate registration authorities, on-site surveyed spatial data, public passenger flow and rent data from surrounding business districts, and project completion acceptance archives. Update rhythms vary: ownership and project archives are updated quarterly, while surrounding rent and passenger flow data is updated monthly. Most documents are multi-page PDFs or structured tables, containing fields such as basic project information, ownership certification documents, surveying and mapping parameters, surrounding supporting facility ledgers, and rent quotation details. Standard unit identifiers include square meters, yuan per square meter per day, dates, and document numbers.
Constraints on model integration and configuration
The multi-source, heterogeneous nature of commercial real estate due diligence data requires configuring adaptive parameters for both structured data parsing and unstructured text understanding during model integration. Differences in update rhythms across data sources require configuring trigger interval parameters for scheduled pull tasks, to distinguish between quarterly and monthly synchronization cycles. Individual reports have long document lengths, including long-form engineering descriptions and short-field data, requiring reasonable context window and segment cutting parameters to avoid exceeding model input limits. The presence of multiple field units requires configuring entity recognition rules for the model, to ensure accurate binding of values such as rent and area to their corresponding units.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Single commercial real estate due diligence reports typically span 50 to 200 pages. Split single-segment text lengths fit the base context windows of mainstream large models |
chunkSize | 1000–1500 characters | Commercial real estate documents contain long-form engineering descriptions and short-field data. This range balances information integrity and retrieval accuracy |
vectorStoreBatchSize | 50–100 entries | Balances ingestion efficiency for mixed structured and unstructured data, and avoids single-ingestion timeouts |
apiRequestTimeout | 120 seconds | Some external data sources, such as real estate registration APIs, have relatively high response delays |
structuredParseMode | Auto-detection + manual field specification | Commercial real estate data includes both standardized fields and free text. This mixed mode improves field extraction accuracy |
embeddingModelDimension | 1024 or 1536 | Matches the output dimensions of mainstream vector models, aligned with the semantic complexity of commercial real estate fields |
The parameter values provided on this page are common recommended starting points for configuration. 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 configuration errors
- Symptom: Vector model configuration triggers
ModelNotSupportederror,400 Bad Requesterror, or no matching content in retrieval results after data ingestion. Cause: Failed to select a vector model with a matching dimension based on the field characteristics of commercial real estate data, or failed to configure structured data parsing rules. - Symptom: No streaming output when calling external models, or output results are poorly segmented. Cause: Failed to configure callback parameters for streaming output in the code runtime module, and failed to adapt streaming segmentation rules for long commercial real estate report texts.
- Symptom: Deployed large model GPU utilization is lower than expected, and single CPU core usage remains at 100% for extended periods. Cause: Failed to allocate sufficient GPU video memory quota for the model, or failed to disable non-essential CPU parallel computing threads, resulting in excessive CPU resource usage during the data preprocessing stage.
How to confirm successful configuration
- Upload a structured table from a standard commercial real estate due diligence report, and check that the field extraction results include all preset commercial real estate-specific fields.
- Trigger a scheduled synchronization task, and check that the pull logs for external data sources include commercial real estate data from the corresponding time interval, with no timeout or connection failure records.
- Run a retrieval test, enter a commercial real estate-related query term, and check that the relevance and unit matching of returned results meet preset requirements.
- Enable streaming output testing, and verify that code runtime module output returns segmented results by paragraph, with no lag or truncation.
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.