What the data for this category looks like
Property management marketing content data is primarily sourced from internal marketing systems, historical records of owner community groups, and electronic versions of offline promotional materials. It covers community financial service promotions, holiday event invitations, merchant partnership benefit notifications, and similar content. Updates follow two schedules: bulk activity updates and ad-hoc script adjustments. Bulk updates occur mostly at quarterly milestones and major holidays. Ad-hoc adjustments trigger immediately for temporary events. Document structures include fields such as event theme, applicable scope, participation method, and budget explanation. The core structured fields are applicable building numbers, event coverage households, and event budget. Their respective units are building number identifiers, households, and yuan.
What constraints these characteristics impose on vector models and indexing
Structured fields in property management marketing content require vector indexes to support metadata-associated queries. Pure text vector recall alone is insufficient. Document lengths vary widely, ranging from community scripts of a few dozen characters to activity plans thousands of characters long. This demands flexible chunking strategies. Update frequency includes both bulk and ad-hoc patterns. Full index rebuilding consumes excessive hardware resources, so an incremental refresh mechanism is required. Some content includes numeric fields such as amounts and household counts. Vector models must support fusion processing of structured metadata and text vectors to avoid recalling irrelevant content.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Property management marketing content includes structured fields. Segments that are too long will lose associated information such as event scope. Segments that are too short will damage the semantic integrity of marketing scripts |
vector_db_type | pgvector | Adapts to existing PostgreSQL ecosystem deployment environments. Supports structured metadata joint indexing, enabling precise filtering based on applicable buildings |
embedding_model | bge-large-zh-v1.5 (local deployment) | Provides high semantic understanding accuracy for Chinese marketing scripts. Supports local deployment compatible with Win11+1080ti hardware environments |
recall_top_k | Top 8–12 entries | Marketing content matching scenarios mostly focus on precise event matching. Too many recalled entries increase context redundancy. Too few will miss relevant content |
index_refresh_interval | Trigger on demand or every hour | Adapts to the bulk and ad-hoc update schedules of marketing content. Triggering on demand reduces resource consumption from index rebuilding |
metadata_filter_enabled | Enabled | Marketing content includes structured fields such as applicable buildings and covered households. Enabling this allows filtering non-matching content via metadata, improving recall accuracy |
The parameter values provided on this page are general recommendations to serve 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: Vector recall results include marketing content for non-applicable buildings. Cause: The
metadata_filter_enabledconfiguration is not enabled, and associated filtering is not applied to structured fields. - Phenomenon: High memory usage errors occur during local deployment. Cause: The
chunk_sizeandrecall_top_kparameters are not adjusted for 32GB hardware memory. Overly long segments and too many recalled entries consume excessive system resources. - Phenomenon: Knowledge base storage capacity cannot be accurately estimated. Cause: Total vector storage requirements are not accumulated based on post-chunk character counts, and additional storage space for metadata is not reserved.
How to confirm the configuration is correct
- Upload one test marketing content item. Review the segmented results after the vector is stored in the database to confirm the segment length matches the configured
chunk_sizevalue. - Submit a retrieval request that includes the applicable building field. Check whether the recall results automatically filter content outside the matching scope to confirm the metadata filtering configuration is active.
- Manually trigger an index refresh. Review system logs for normal index rebuilding logs. The absence of errors confirms the refresh configuration is working correctly.
- Call the vector query interface. Confirm the number of returned recalled entries matches the configured
recall_top_kvalue to verify the recall parameter configuration is correct.
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.