What the Data for This Category Looks Like
Commercial property due diligence report data mainly comes from property ownership registration documents, existing lease contracts, fire safety acceptance certificates, property fee collection ledgers, surrounding business district passenger flow monitoring data, and similar sources. Data update rhythms vary. Static materials such as ownership documents and fire reports have very low update frequencies. Lease contracts and property fee ledgers are updated quarterly or annually. Business district passenger flow data is updated monthly. Document structures include structured tables (such as rent calculation sheets, area statistics sheets) and unstructured paragraphs (such as lease clause explanations, property status descriptions). Fields include building area (square meters), rent unit price (yuan/square meter/day), property type, ownership certificate number, and similar items. Some fields have clear unit constraints.
What Constraints These Characteristics Impose on the Knowledge Base Retrieval and Recall Link
The multi-source nature and differentiated update rhythms of commercial property due diligence data require the retrieval and recall link to support flexible switching between incremental indexing and full indexing. Structured fields and unit constraints require the retrieval system to support precise field matching and unit alignment verification, to avoid mixing irrelevant results. A high proportion of long unstructured document paragraphs requires the segmentation strategy to balance context integrity and retrieval accuracy. When the data volume is large, it is necessary to balance recall efficiency and result relevance, to avoid loading too much redundant data in a single retrieval.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
RECALL_TOP_K | 10-15 items | Commercial property due diligence data includes multi-dimensional fields, and at least 10 candidate results are required to match business query needs |
SIMILARITY_THRESHOLD | 0.72-0.85 | Structured field matching requires high precision, and this interval can filter low-relevance non-target entries |
PARSE_CHUNK_SIZE | 800-1200 characters | Commercial property documents mostly contain long lease clauses, and this segmentation length can retain complete context while avoiding overly fragmented segments |
PARSE_CHUNK_OVERLAP | 100-150 characters | Lease contract clauses have cross-segment associations, and overlapping segments can retain context coherence |
VECTOR_DB_BATCH_SIZE | 500-800 items/batch | Adapts to batch indexing of commercial property knowledge bases with hundreds of thousands of entries, avoiding a single write exceeding database load |
SYNC_INCREMENTAL_INTERVAL | 1 hour | Frequently updated lease contracts and property fee data can be synchronized at this interval; low-frequency static materials can be adjusted to daily synchronization |
The parameter values provided on this page are conventional recommendations used as a starting point 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 Mistakes
- Phenomenon: After the knowledge base completes indexing hundreds of thousands of data entries, retrieval tests return far fewer results than expected. Cause: The
PARSE_CHUNK_SIZEparameter was not adjusted for commercial property long documents, resulting in valid content not generating valid vectors or being truncated. - Phenomenon: In the locally deployed V4.8.20-FIX2 version, PGSQL vector database retrieval triggers a timeout error. Cause:
VECTOR_DB_BATCH_SIZEis set beyond the database connection pool limit, and batch write operations block retrieval requests. - Phenomenon: When mapping knowledge base QA locally, modifications to a single data entry cannot be synchronized to the vector database. Cause: The
update_collection_iteminterface was not called correctly, and the unique identifier field of the data was not specified.
How to Verify Correct Configuration
- Perform a full indexing test, check that there are no timeout or field parsing errors in the vector database write logs, and verify that the number of completed index entries matches the total number of source data entries.
- Input query terms containing specific business fields, and check whether retrieval results prioritize returning entries that match the corresponding fields.
- Simulate a single data entry modification operation, call the corresponding interface, and verify whether the update time and content of the data entry in the knowledge base are synchronized.
- Adjust the test range of retrieval parameters, compare the relevance of recall results under different configurations, and confirm that the configuration combination meets business 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.