What Data for This Category Looks Like
Data sources for commercial real estate intelligent due diligence reports include public record information from real estate registration authorities, completion survey reports submitted by project development enterprises, daily operation ledgers provided by property managers, and public data from third-party business district research institutions. Update cycles vary across data types: ownership data updates per the cycle of local registration departments, operation data updates monthly, and surrounding supporting data updates quarterly. A single report typically includes three modules: ownership certification, operation details, and compliance check. The operation details module contains structured tables with fields including total construction area (unit: square meters), rentable area (unit: square meters), average monthly rent (unit: yuan/square meter·month), land use term (unit: years), and others.
Constraints on Multi-turn Dialogue and Prompt Engineering
Dispersed data sources and varying update cycles require multi-turn dialogue to switch contexts by data module. First retrieve ownership data before verifying operation data. Prompts must clarify call priorities for different data sources. Document structures include structured tables and unstructured attachments, requiring multi-turn dialogue to support precise follow-up questions about specific fields. Prompts must define extraction rules for structured fields. Varied field units require unified unit conversion logic during multi-turn dialogue to avoid output confusion. Prompts must preset unit mapping rules. Large differences in data update frequencies require multi-turn dialogue to support dynamic loading of the latest data. Prompts must label data update timestamps to ensure output timeliness.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Commercial real estate due diligence reports include multi-module data, requiring retention of ownership, operation, and compliance context for multi-turn dialogue to avoid loss of critical information |
Recall count | Top 8–12 entries | Commercial real estate due diligence data has many fields. Too many retrieved entries will exceed the context window, while too few will miss critical rent and area data |
Similarity threshold | 0.75–0.85 | Precise matching of specific fields in due diligence reports is required to avoid irrelevant business district data interfering with current queries |
HTTP_REQUEST_TIMEOUT | 600 seconds | Commercial real estate due diligence data may come from multiple external interfaces, and retrieving ownership record data requires a long response time |
Chunk size | 1000–1500 characters | Structured tables and attachment content in commercial real estate due diligence reports are lengthy, and segmented processing improves model understanding accuracy |
Rerank result count | Top 5–7 entries | The most relevant due diligence data modules for the current query must be returned first. For example, return operation data first when querying rent |
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 specific analysis, and it is recommended to test against your own samples before finalizing.
Three Common Mistakes
- Issue: The dialogue logging function cannot associate with specific fields in commercial real estate due diligence reports, such as total construction area or average monthly rent. Cause: Binding rules between fields and corresponding data modules are not clearly defined in prompts, resulting in failed matching of structured data for logging.
- Issue: Intermediate AI-generated output is displayed directly on the interface, without being processed by a text splicing component and output via a specified API. Cause: The output mode of the AI node is not configured to "return only final text", causing intermediate step content to be exposed.
- Issue: After retrieval results from the knowledge base are passed to AI dialogue, the AI cannot recognize structured fields in due diligence reports and outputs incorrect rent or area data. Cause: Retrieval results are not spliced in the structured format of
{field name}:{field value}, preventing the model from accurately extracting corresponding data.
How to Verify Proper Configuration
- Enable the dialogue logging function, initiate a query about ownership information of a specific project, and check whether the logging function can associate with the corresponding fields.
- Run the workflow, and observe whether only the final due diligence report result is displayed on the interface, with no intermediate AI-generated text fragments.
- Import a piece of structured commercial real estate due diligence data into the knowledge base, initiate a query about rent or area, and check whether the AI output correctly references the retrieved fields.
- Run a workflow containing two consecutive AI dialogue nodes, and check whether the second dialogue node correctly obtains the output content of the first node as context.
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.