What This Category of Data Looks Like
Data sources for construction and decoration investment research include industry regulatory documents, building material parameter manuals, bidding announcements, project completion case reports, construction process guides, and market price lists. Update cycles vary significantly. Industry regulations and process guides have long update cycles. Bidding announcements and completion cases are added in real time as projects launch. Building material prices are updated based on market supply and demand fluctuations.
Document structures include both structured fields and unstructured text. Structured fields include building material brand, specification model, unit price, construction period, budget amount, and more. Units include square meters, yuan per square meter, cubic meters, tons, and others. Unstructured text includes process steps, project background descriptions, bid evaluation rules, and more.
How These Characteristics Impose Constraints on Vector Models and Indexing
The multi-dimensional characteristics of construction and decoration investment research data create multiple constraints for the vector models and indexing workflow. First, document length varies widely. Materials range from parameter tables with tens of characters to project case reports with tens of thousands of characters. This requires indexing to support flexible segmentation and encoding of variable-length text. Second, a large number of repeated or highly similar structured parameters exist. It is necessary to distinguish building materials of the same type but different specifications, and projects of the same type but different sections. This requires vector models to adapt to semantic encoding of structured fields. Third, some data has high real-time requirements. This requires support for incremental index updates to avoid redundant overhead from full reconstruction. Finally, investment research scenarios require accurate recall of matching key parameters and business descriptions. This requires recall logic to balance semantic similarity and field relevance.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
EMBEDDING_MODEL | bge-large-zh-v1.5 or text-embedding-ada-002 | Adapts to Chinese terminology in the construction industry, supports mixed encoding of structured parameters and long text |
CHUNK_SIZE | 800–1200 characters | Balances semantic integrity of construction documents, avoids semantic fragmentation from overly small chunks or vector dimension overflow from overly long chunks |
RECALL_TOP_K | Top 8–12 results | Balances information breadth and accuracy for investment research scenarios, avoids excessive redundant results interfering with analysis |
VECTOR_DB_SIMILARITY_THRESHOLD | Calibrated via actual testing | Distinguishes building materials of the same type but different specifications, and projects of the same type but different sections, adapts to matching accuracy requirements of business scenarios |
INCREMENTAL_UPDATE_ENABLED | Enabled | Adapts to high-frequency update requirements for building material prices and bidding announcements, reduces time spent on full index reconstruction |
PARSE_STRUCTURED_FIELD | Enabled | Encodes structured parameter fields separately, improves recall accuracy of key information such as material specifications and budget amounts |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Issue: Search tests return results with large semantic deviation from the query, or key parameters such as building material specifications and project budget are not recalled. Cause: The
PARSE_STRUCTURED_FIELDconfiguration is not enabled, and structured fields are not encoded separately. This causes vectors to fail to accurately match core business parameters of construction documents. - Issue: An error "No available embedding model" is returned when calling the knowledge base. Cause: The
EMBEDDING_MODELparameter is not correctly configured in the configuration file, or the interface for the corresponding model is not added in OneAPI. This interrupts the vector generation workflow. - Issue: Search response speed is slow after indexing hundreds of thousands of pieces of data. Cause: Reasonable
CHUNK_SIZEandRECALL_TOP_Kvalues are not set, or partitioned indexing of the vector database is not enabled. This causes excessive data scanning during single recall.
How to Confirm Proper Configuration
- Check that the
EMBEDDING_MODELparameter in the configuration file matches the model configured in OneAPI, and confirm there are no error messages in embedding model call logs. - Upload a single construction material parameter document and a project case document, and verify via document parsing logs that chunk length falls within the
CHUNK_SIZEconfiguration range. - Initiate a query that includes building material specifications and project budget, and confirm that the similarity scores of recalled results fall within the preset threshold range, with no clearly irrelevant non-matching results.
- Import incrementally updated building material price data, and verify that the index only updates newly added or modified entries, with no excessively long full reconstruction time.
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.