What This Type of Data Looks Like
Commercial real estate yield and market trend data primarily comes from commercial building operation management systems, regional business district record transaction databases, and public data from third-party commercial real estate research institutions. Most data documents use structured table formatting, with core fields including project name, affiliated business district, rental unit price, vacancy rate, return on investment, and update date. Units cover multiple categories such as area, monetary value, and percentage. The update rhythm typically synchronizes the previous day’s transaction and operation data every early morning. Benchmark project data for some core business districts is updated on an hourly basis.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompting
Because the data includes multiple fields and mixed units, prompts must explicitly require that corresponding units be marked for each returned data item to avoid confusion between different indicators. Because update rhythms vary between daily and hourly updates, multi-turn dialogue must track the time range specified by the user, ensuring subsequent queries use the most recent data for the corresponding period. Because data is associated with multiple dimensions such as business districts and projects, multi-turn dialogue must retain dimension identifiers from historical context to avoid confusion between different buildings or business districts in later queries. Additionally, batch returns of structured data may consume significant context window space. Prompts must configure reasonable content truncation rules, only retaining core fields of interest to the user.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Commercial real estate data has many fields and long individual project data. Sufficient historical context must be retained to track project and time dimensions across multi-turn queries |
prompt_template | "Please reply to user questions based on the following commercial real estate data. Each returned data item must be marked with its corresponding unit. Only use content from the provided data sources, and no external information may be introduced. Current time: {{current_time}}" | Mandatory constraints for unit marking and data source scope are required. System time variables must also be bound to ensure accurate timestamping |
recall_top_k | 3–5 entries | Commercial real estate data is grouped by business district or project. Only a small number of core project data of interest to the user need to be recalled per query to avoid context overload |
history_save_mode | Full save of conversation context | Multi-turn dialogue must track query conditions such as projects, time, and indicators that the user refines step-by-step. Complete historical records ensure context consistency for subsequent queries |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Commercial real estate data documents may contain a large number of structured entries, which require longer parsing time. Sufficient processing time must be reserved |
similarity_threshold | 0.75–0.85 | Low-correlation commercial real estate data must be filtered to avoid interference from irrelevant projects with response accuracy |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfiguration Issues
- Symptom: Conversation history is not saved, and auto-replied content is empty when the session is reopened. Cause: Full save for
history_save_modeis not enabled, or the configuration is set to only save user questions and ignore AI reply content. - Symptom: Replies from a previous AI conversation in the workflow are carried over to the final result without being filtered. Cause: No context filtering rules are configured in the prompt for the second AI conversation, or only the current round of input content is not specified.
- Symptom: The
{{current_time}}variable called in the prompt does not return the current day’s date, or the format does not meet requirements. Cause: The output format of the system time variable is not correctly bound, the prompt does not explicitly require the use of the current system date, or custom time is not specified.
How to Verify Proper Configuration
- Initiate a test conversation, ask about the rental return rate of a specific commercial real estate project, then close and reopen the session. Confirm that historical conversations and AI replies are still displayed normally.
- Upload a commercial real estate data document containing multiple fields, initiate multi-turn queries to refine search conditions step-by-step. Confirm that subsequent replies always associate previously mentioned projects and time ranges.
- Review the configuration of the AI conversation component in the workflow. Confirm that the input for the second conversation only binds the output variable from the previous conversation, and the prompt includes rules for filtering redundant content.
- Trigger a prompt call, check whether the returned result automatically marks the corresponding unit for all data items, and the timestamp matches the current system date.
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.