What the data for this category looks like
Commercial real estate marketing data comes from internal project investment promotion documents, property operation and maintenance systems, offline event materials, and business district operation reports. Update cycles vary: updates happen in real time when investment promotions adjust or new brands settle in, event materials update according to event schedules, and operation reports update monthly. Most document types are PDFs and Excel files with structured fields, plus location maps with specific coordinates. Fields include shop number, rental unit price (yuan/square meter·day), business category, full name of settled brands, event execution cycle, and more. Some documents include both text descriptions and visual annotation content.
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
The data includes structured exclusive business fields such as rental prices and shop numbers, plus a large number of location documents with coordinates. Retrieval must cover both semantic matching and structured field matching. Document lengths vary widely, from single-page event notices to dozens of pages of investment promotion plans. Systems must adapt text segmentation rules to different lengths to avoid breaking the contextual association of structured information. Update frequencies differ: operation reports update monthly, and investment promotion adjustments take effect in real time. Systems must support incremental synchronization, not full retransmission, to ensure the timeliness of recalled content. Some documents include non-text location annotations. Systems must support associated retrieval of structured metadata and visual content to improve matching accuracy.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_CHUNK_SIZE | 800–1200 characters | Commercial real estate documents include both long paragraphs of investment promotion plans and short event notices. This range balances segmentation integrity and recall accuracy |
RECALL_TOP_K | Top 8–12 results | Marketing content needs to cover multi-dimensional information such as shops, events, and locations. Too many results will interfere with context, while too few will miss valid content |
SIMILARITY_THRESHOLD | 0.72–0.80 | Terms such as rental prices and business categories in commercial real estate have high recognition rates. A threshold that is too low will recall irrelevant competing information, while a threshold that is too high will miss matching results for similar business categories |
UPLOAD_FILE_MAX_SIZE | 500 MB | The total size of large investment promotion brochure collections usually does not exceed this value, to avoid upload timeouts |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing multi-page PDF documents takes a long time. This duration covers the complete parsing process |
ENABLE_STRUCTURED_PARSE | Enabled | Commercial real estate data includes structured fields such as shop numbers and rental prices. Enabling this function retains metadata for precise retrieval |
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 specific analysis. It is recommended to test on local samples before finalizing configuration.
Three Common Misconfigurations
- The phenomenon is that results returned by tool calls are unrelated to knowledge base search results. The cause is that the structured parsing function is not enabled. Only pure text semantic matching is used, which cannot accurately match exclusive business fields such as shop numbers and rental unit prices.
- The phenomenon is that files remain in the loading state indefinitely after upload in an offline environment, and return to normal after connecting to the internet. The cause is that the system needs to access specified external resources to complete pre-verification for document parsing. This process cannot be completed in an offline environment.
- The phenomenon is that semantic retrieval fails to recall target content, but full-text retrieval works normally. The cause is that segmentation parameters are set unreasonably, destroying the contextual association of structured information in long documents, or the similarity threshold is set too high, causing valid matches to fail to meet standards.
How to Confirm Configuration is Correct
- Upload a commercial real estate document containing structured fields. Check if parsed segments retain key information such as shop numbers and rental unit prices, to confirm that the
ENABLE_STRUCTURED_PARSEconfiguration is enabled. - Initiate a semantic retrieval test. Enter a query containing business category and rental range, and verify that the number of recalled results falls within the preset
RECALL_TOP_Krange. - Adjust the similarity threshold and initiate multiple retrievals. Compare changes in the relevance of recalled results to confirm that the threshold setting matches business requirements.
- Simulate an offline environment to upload a document. Check if a loading timeout prompt is triggered, to confirm that external resource access dependency logic meets 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.