What the Data for This Category Looks Like
Commercial real estate investment research data comes from public policy documents, project feasibility study reports, business district operation ledgers, competitor research materials, and rent settlement data tables. Update rhythms vary across sources: public policies update in real time upon release, business district foot traffic and rent ledgers update monthly or per settlement cycle, and project feasibility study reports are one-time archived documents. Document structures include long text reports, structured data tables, and scattered research notes. Structured fields include building area (unit: ㎡), rent unit price (unit: yuan/㎡/day), peak foot traffic (unit: visits per day), and some documents contain nested project-related information.
Constraints on Vector Models and Indexing
The high proportion of long text reports means standard chunking parameters cannot retain complete semantic units. Adjust chunking and overlap length to avoid breaking key project information. Mixed structured data and unstructured text requires vector models to adapt to both numeric fields and natural language semantic encoding. Failure to do so will lose field association information. Data sources with multiple update rhythms require indexes to support incremental updates. Full indexing consumes too many computing resources and cannot adapt to monthly batch update requirements. Vector dimensions of multi-source data must be unified. Otherwise, semantic bias appears in recall results for different document types, affecting the accuracy of investment research judgments.
Configuration Settings
| Configuration Key | Recommended Value | Rationale |
|---|---|---|
PARSE_CHUNK_SIZE | 800–1200 characters | Commercial real estate feasibility study reports are mostly long texts. This range retains complete semantic units such as project location and business planning, and avoids breaking key information |
PARSE_CHUNK_OVERLAP | 100–150 characters | Context association must be retained after chunking long documents, to avoid splitting associated fields such as rent unit price and lease term into different chunks |
VECTOR_MODEL_DIM | 1536 | Standard dimension of general open-source vector models, which meets the vectorization needs of both commercial real estate structured fields and unstructured reports |
RECALL_TOP_K | Top 8–12 entries | Commercial real estate investment research balances breadth and accuracy. Too many recalls introduce irrelevant business district data, too few miss competitor information |
SIMILARITY_THRESHOLD | 0.72–0.78 | Commercial real estate data has high semantic similarity discrimination. This range filters invalid recalls and retains valid project information |
INDEX_INCREMENTAL_ENABLE | Enabled | Commercial real estate data has incremental updates monthly or per settlement cycle. Full indexing consumes too many computing resources |
The parameter values provided on this page are conventional recommendations used to determine the starting point 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: When uploading commercial real estate feasibility study reports larger than 10MB, some chunks show vectorization exceptions, and retrying multiple times restores normalcy. Cause: The chunk overlap parameter is set too small, and edge chunks containing project association information have semantic breaks, making the vector model unable to complete effective encoding.
- Phenomenon: After batch uploading a large number of commercial real estate project data, the server process crashes. After restarting, the knowledge base status shows not ready, and the index cannot be completed automatically. Cause: Incremental indexing configuration is not enabled. The full indexing task is not persistently stored, so the unfinished indexing task cannot be automatically restored after restarting.
- Phenomenon: After updating the platform version to 4.9-4.10, the original commercial real estate knowledge base cannot return vector retrieval results. Cause: The vector model dimension configuration changes after the version upgrade. The vector dimension of the original index does not match the current model, so retrieval cannot match valid results.
How to Confirm Proper Configuration
- Upload a single commercial real estate feasibility study report larger than 10MB, check the chunk details page, and confirm that the character count of each chunk falls within the preset chunking range.
- Initiate a vector retrieval test, enter a query containing commercial real estate structured fields, and verify that the similarity scores of the recall results fall within the preset threshold range.
- Upload an updated commercial real estate rent data table, check the index task status, and confirm that the incremental index task is automatically triggered and completed.
- Export the current vector model configuration information of the knowledge base, and verify that the dimension parameter matches the preset value.
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.