What the data for this category looks like
Commercial real estate research report data comes from four main sources: national commercial real estate industry associations, on-site monitoring data from leading real estate consulting firms, commercial segment financial reports published by listed real estate companies, and commercial housing rental filing data released by local housing and urban-rural development departments.
Updates follow a monthly monitoring report and quarterly in-depth research report schedule. Core commercial districts in key cities receive weekly updates.
Typical research report fields include project location coordinates, rental price per unit (yuan per square meter per day), vacancy rate, average daily passenger traffic, business format proportion, surrounding supporting facilities, and more. Some reports include floor plans and competitor comparison tables.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering
The multi-dimensional fields and mixed content structure of commercial real estate research reports require multi-turn dialogue to track limiting conditions such as location and business format mentioned in the previous round. This prevents expanding the retrieval scope to unrelated commercial districts or business formats.
The update frequency of research reports requires prompts to clearly specify retrieval of public data from the last 3 months. This stops the return of outdated rental or vacancy rate information.
The relatively long length and structured content of individual research reports occupy more context windows. Appropriate configuration of context retention length is needed, while avoiding token limit breaches caused by excessive historical information accumulated during multi-turn dialogue.
How to Set Configurations
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Commercial real estate research reports have relatively long individual main text lengths. Sufficient multi-turn dialogue context must be retained to support associated questions across multiple fields. |
maxResponseTokens | 2500–3500 characters | Covers complete research report data fragments required for a single round of questions, avoiding truncation of key information due to insufficient tokens. |
knowledgeBaseRefreshCycle | 7 days | Matches the monthly/quarterly update rhythm of commercial real estate research reports, ensuring retrieval results include the latest commercial district rental and vacancy rate data. |
recallTopK | Top 6–8 entries | Commercial real estate research reports involve multi-dimensional data, so a sufficient number of recalled entries is needed to cover core fields such as location, rental price, and passenger traffic. |
similarityThreshold | 0.72–0.78 | Commercial real estate data fields have a relatively high degree of standardization. A threshold that is too low will introduce irrelevant research reports, while a threshold that is too high will result in insufficient recall. |
parseChunkSize | 1500–2000 characters | Adapts to the mixed content structure of tables and text in research reports, avoiding damage to table integrity and paragraph logic during chunking. |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing values.
Three Common Mistakes
- Phenomenon: When asking about the rental price of a specific commercial district during multi-turn dialogue, the
usage.completion_tokensfield in the session log shows 200, which does not match the configuredmaxResponseTokensvalue. The reply length is only around 200 characters. Cause: The question triggers an implicit length limit on structured industry data from the model interface, or the retrieval result contains unfiltered sensitive content leading to truncation. - Phenomenon: Attempting to upload a new commercial real estate research report in the conversation window triggers a system prompt that the report cannot be saved to the knowledge base. Cause: Uploads during the conversation phase are only for temporary parsing, and are not bound to the knowledge base's write permission. Permanent updates must be completed through the knowledge base management page.
- Phenomenon: When viewing token statistics for a single round of conversation, only the
global.workerPoll.countGptMesfield is visible, and the total token consumption value cannot be retrieved. Cause: This field only counts the number of GPT message calls. The total token consumption for a single round must be viewed through thetotalTokensorusagefield on the conversation details page.
How to Confirm Proper Configuration
- Initiate a multi-turn question targeting a specific commercial real estate project, mention location, business format, and rental data in sequence. Check if each round of reply retains the limiting conditions from the previous round to confirm the
maxContextconfiguration is active. - Trigger a composite question involving rental price, vacancy rate, and passenger traffic data, and check the
maxResponseTokensfield value in the session log to confirm it matches the configured value. - Manually upload a latest commercial real estate research report to the knowledge base, initiate a targeted question, and confirm the returned content includes the latest data to verify the
knowledgeBaseRefreshCycleconfiguration is active. - View the
totalTokensandusagefields on the conversation details page to confirm the single-round token consumption statistics are complete and meet expectations.
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.