What the Data for This Category Looks Like
Construction and decoration research report data is sourced from industry association monthly reports, building material market weekly reports, public bidding documents, and housing and urban-rural development department policy documents. Update frequency varies by content type:
- Bidding documents are updated in real time alongside projects
- Building material quotes are updated weekly
- Industry research reports and policy documents are released quarterly or irregularly
Document structure includes project overviews, building material usage and unit price tables, construction process descriptions, cost accounting details, and more. Fields include project number, building material category, unit price (yuan/square meter), construction period (days), bidding amount (ten thousand yuan), and others. Some documents contain mixed structured tables and unstructured text.
How These Data Characteristics Impact Vector Models and Indexing
Multi-source, multi-format data requires adaptation to different parsing rules, which demands strong multi-format encoding capabilities from vector models. Content with uneven update frequencies needs support for incremental indexing to avoid resource waste from full index reconstruction. Documents with mixed structured fields and unstructured text require vector models to encode both numerical fields and text semantics. Fields with specific units (such as yuan/square meter and ten thousand yuan) must retain associated information during indexing to prevent semantic confusion during retrieval.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Vector Model Access Channel | BGE-M3 vector model | Adapts to the structured and unstructured text encoding needs of construction and decoration research reports, supports flexible deployment |
Chunk Length | 800–1200 characters | Construction and decoration research reports contain long-text process explanations and short-text building material unit prices. This range balances semantic completeness and retrieval accuracy |
Number of Retrieved Results | Top 10–15 results | A single research report covers multiple types of building materials and projects. A sufficient number of retrieved documents is needed to cover all scenarios |
Similarity Threshold | 0.72–0.78 | Avoids incorrect association of similar but different categories of building materials, while retaining associated documents under the same project |
Incremental Update Switch | Enabled | Construction and decoration bidding documents are updated in real time alongside projects. Incremental updates reduce resource consumption from index reconstruction |
Maximum Token Count for Vector Model Input | 8192 | Adapts to long-document policy explanation chapters, avoiding truncation of key semantic content |
Index Shard Size | 500 MB | Adapts to monthly building material quote data imported in a single batch, balancing query speed and storage usage |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: The vector model returns a 500 status code, and no matching results appear during retrieval. Cause: The vector model's API key and access address are not configured correctly, preventing a valid connection from being established.
- Symptom: A large number of irrelevant building material category entries appear in retrieval results. Cause: A reasonable similarity threshold is not set, or the chunk length is too short, causing semantic segmentation to destroy field association relationships.
- Symptom: Incremental index update delay exceeds the preset cycle. Cause: The incremental synchronization cycle parameter is not adjusted. The default cycle cannot adapt to the real-time update needs of bidding documents in the construction and decoration industry.
How to Confirm Proper Configuration
- Run a vector model connectivity test, and check if the returned embedding vector dimensions match the configured model parameters.
- Upload a single construction and decoration research report, and verify that all parsed fields are fully extracted, with no missing key information such as project numbers and unit prices.
- Initiate a simulated retrieval, enter a specified keyword, and check if the relevance of the retrieved results meets expectations.
- Trigger an incremental index update, and check if the index update log shows that newly uploaded business documents have been successfully added to the index.
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.