What this category of data looks like
Sources of commercial real estate marketing content include offline investment promotion brochures, shop rental posters, commercial district research reports, merchant settlement agreement abstracts, online shop detail pages, and event promotional materials. Updates are triggered irregularly, tied to new shop openings, commercial district format adjustments, and promotional campaign launches, with no fixed cycle. Document structures are mixed, containing both structured fields and unstructured long text. Structured fields include commercial district name, location, floor area (unit: square meters), business type, rental range (unit: yuan per square meter per day), supporting facilities, target customer groups, and contact information. Unstructured content includes investment policies and event rules.
What constraints these characteristics impose on vector models and indexing
Multi-source, mixed-structure commercial real estate marketing content requires vector models and indexing to support joint retrieval of structured fields and non-text content. This avoids losing field information that occurs when only relying on vector recall. Irregular update cycles and bulk-imported new content require indexing to support incremental updates without full reconstruction. This prevents blocking of online services. Content includes clear numerical and unit fields, so vector encoding must preserve semantic associations to avoid retrieval bias caused by unit confusion. Marketing content contains time-sensitive event information, so indexing must support fast filtering based on time fields. This prevents recalling invalid promotional or investment promotion content.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Covers the common length of commercial real estate marketing content, balances context completeness and retrieval accuracy |
chunk_overlap | 100–150 characters | Reduces context breaks after long text segmentation, ensures semantic coherence for long content such as investment policies and event rules |
vector_db_type | pgvector | Supports combined capabilities of structured field filtering and vector retrieval, adapts to the mixed retrieval needs of commercial real estate data |
embedding_model_path | Determined based on actual testing | Specifies the local vector model path during local deployment, adapts to proprietary semantics in the commercial real estate domain |
top_k | 10–15 entries | Covers sufficient candidate marketing content while controlling redundancy in retrieval results |
similarity_threshold | 0.75–0.85 | Filters low-relevance retrieval results, matches the precise matching needs of commercial real estate marketing content |
The parameter values provided on this page are all conventional recommendations used as a 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 mistakes
- Indexing tasks remain in a running state with no progress updates. Cause: Bulk imported commercial real estate marketing content contains long text exceeding the segmentation limit, causing indexing processing to block.
- The interface returns a 503 status code, prompting that the
text-embeddingmodel under thedefaultgroup is unavailable. Cause: The vector model call link is configured incorrectly, the available embedding service node is not properly bound, or the request retry mechanism is not configured. - Retrieval results include expired promotional event content. Cause: No structured filtering rules based on event end time are configured, only relying on vector similarity recall without filtering invalid content.
How to confirm the configuration is complete
- Check the vector database connection logs to confirm there are no connection timeout or permission errors, and verify that the
vector_db_typeconfiguration matches the actual deployed database type. - Submit a single commercial real estate marketing content entry for indexing, check whether the segmentation results fall within the configuration range of
chunk_sizeandchunk_overlap. - Initiate a retrieval request, verify that filtering retrieval results can be performed based on both vector similarity and structured fields such as commercial district and business type.
- Simulate bulk import of multiple marketing content entries, check whether the indexing task can complete normally without blocking or timeout errors.
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.