What This Category of Data Looks Like
Residential construction marketing content supports finance, insurance, and wealth management scenarios. Sources include marketing materials provided by project parties, bidding documents, construction progress announcements, regional supporting facility research documents, and sorted consultation materials from offline site visits. Data is produced in phases alongside the project lifecycle. Key milestones such as new project preparation, grand opening, and delivery drive batch generation and updates of existing materials. Only minor updates to partial content occur on a daily basis. Document structures vary widely, including short-text selling points, long-form manuals, and structured parameter documents. Structured fields include project name, building type, total construction area, and others, with units in square meters.
What Constraints Do These Characteristics Impose on Vector Models and Indexing
Residential construction marketing content has wide variations in text length. Short-text selling points are only a few hundred characters, while long-form manuals can reach tens of thousands of characters. Vector chunking strategies must adapt to these differences. Avoid over-splitting short texts to prevent loss of semantic meaning. Preserve contextual connections when splitting long texts.
The data includes numerous structured parameter fields. Parameters must be bound to corresponding marketing text. Indexes must support associative retrieval between structured metadata and vector embeddings.
Updates are produced in staged batches during the project lifecycle, with low daily update volume. Use a combination of scheduled full refreshes and incremental updates for index refresh. Real-time synchronization is not required.
Data sources also include professional documents such as bidding materials and progress announcements. Vector models must support construction industry technical terminology to ensure semantic accuracy of embeddings.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Residential construction marketing content includes both long-form manuals and short-text selling points. This range balances contextual completeness for long texts and semantic integrity for short texts, avoiding overly fragmented or overly long splits |
chunkOverlap | 100–150 characters | Preserves overlapping sections when splitting long documents, ensuring semantic coherence between adjacent chunks and adapting to the long-sentence structure of professional construction documents |
vectorModel | General-purpose Chinese vector model fine-tuned for the construction domain; use a general-purpose Chinese vector model if no fine-tuning is available | Residential construction marketing content contains a large number of professional terms. Domain fine-tuned models can improve the semantic accuracy of embeddings, with general-purpose models as an alternative |
recallTopK | Top 8–12 results | User inquiries about residential construction marketing content mostly focus on specific needs such as unit types and supporting facilities. Too many recalled results will introduce irrelevant content, while too few may miss precise matches |
indexRefreshInterval | Every 24 hours | Daily updates to residential construction marketing content are minimal. Full refresh can be manually triggered during staged batch updates. Scheduled refresh balances synchronization timeliness and resource usage |
enableStructuredFilter | Enabled | Residential construction marketing content includes structured parameter fields. Enabling this feature allows filtering of recalled results via metadata, improving retrieval precision |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material formats, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test with your own samples before finalizing settings.
Three Common Configuration Mistakes
- Symptom: Retrieval returns empty results after migrating vector storage from PGSQL to Zilliz. Cause: The metadata binding relationship of the original index was not migrated, or the vector dimension parameter matching the original vector model was not configured.
- Symptom: Core unit type parameters are not recalled when retrieving long-form project marketing manuals. Cause: The chunk overlap length was set too low, cutting off semantic connections between adjacent chunks and dispersing embedding information for professional parameters.
- Symptom: Matching accuracy of retrieval results fails to meet expectations after connecting a private reranking model. Cause: The reranking model was not adapted to professional terminology from the residential construction domain, or the number of returned reranked results was set beyond the actual required range.
How to Confirm Proper Configuration
- Upload a single residential construction marketing document, view the parsed chunk information, and confirm that chunking parameters match the preset configuration.
- Submit a query containing technical terminology from the construction field, verify that retrieval results match the query intent, and that the structured filtering function works correctly.
- Trigger an incremental index update, check that the index update log has no abnormal errors, and that newly submitted marketing content can be properly recalled.
- After connecting to an external index service, confirm that the service connection status is normal, and that the vector dimension parameter matches the currently used vector model.
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.