What Data Looks Like for This Category
Four primary sources supply commercial property due diligence data: real estate registration department filing documents, property self-owned lease ledgers, business district passenger flow monitoring systems, and merchant operation reports. Ownership-related data updates quarterly. Lease and operation data updates monthly. Business district monitoring data updates weekly. A single due diligence report typically includes four modules: ownership certificate attachments, lease detail lists, monthly operation statistics, and surrounding business distribution tables. Common fields include building area, actual rent, lease term, and number of settled merchants. Corresponding units are square meters, yuan per square meter·month, calendar days, and households respectively.
Constraints Imposed by These Characteristics on Model Access and Configuration
The multi-source and scattered nature of commercial property due diligence data requires model access configuration to support unified cross-data source calls, and avoid repeated configuration of API keys for different systems. The differing update rhythms of each module require timed synchronization tasks to use trigger intervals differentiated by data type. This avoids wasting resources from frequent pulling of low-update-frequency ownership data. The multi-module structure of a single report requires dividing context shards by module when configuring parsing rules. This prevents long texts from exceeding model context limits. The specific requirements for fields and units require configuring model output verification rules. These rules identify and retain fields with clear units such as building area and rent, and avoid unit loss after parsing. Commercial property due diligence data involves sensitive real estate-related information. This requires configuring API key permission isolation rules to restrict keys to only call models for specified business scenarios.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | The total text length of a single commercial property due diligence report after splicing multiple modules usually falls within this range, which avoids truncation of core ownership and lease information |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Commercial property due diligence files often contain high-definition scanned documents and complex tables. This duration covers the complete parsing process for multi-format files |
rag_top_k | Top 8–12 entries | Commercial property due diligence requires associating multi-dimensional data including lease, operation, and business district information. This recall range balances information completeness and result redundancy |
similarity_threshold | 0.75–0.85 | Accurate matching of business fields with clear units is required. This threshold filters low-relevance non-property data and avoids field parsing errors |
UPLOAD_FILE_MAX_SIZE | 500–1000 MB | Commercial property due diligence reports often include multi-page ownership scanned documents. This configuration supports complete upload of large-volume attachments |
api_key_permission_scope | Only allowed to call models related to commercial property due diligence | Commercial property data involves sensitive real estate information. This configuration limits the key calling scope and reduces the risk of data leakage |
The parameter values provided on this page are all common starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: Port occupation error is returned when calling a locally deployed model, or the model fails to start normally. Cause: The correct local listening port is not specified in the model configuration, or the port is already occupied by other programs.
- Phenomenon: A custom model cannot be selected for calling, and the target model entry does not appear in the model list. Cause: The API configuration of the custom model is not added in the FastGPT model management module, or the calling permission of the API key is not enabled.
- Phenomenon: A prompt indicating no available channels is displayed when calling a model using the default group. Cause: The API channel corresponding to the model is not bound to the default group, or the access permission of the channel is not correctly configured.
How to Confirm Configuration is Complete
- Enter the FastGPT model management page, view the configured model list, and confirm that the target model has been added and its status is normal.
- Initiate a small-batch test call, enter sample text for commercial property due diligence, and check whether the model can correctly identify fields with units such as building area and rent.
- Upload a small commercial property due diligence file, and check whether the analysis result is split by module without obvious truncation or unit loss.
- Check the API key permission configuration, and confirm that the correct model calling permission has been assigned to the corresponding group.
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.