vector-search
Implement semantic vector search for construction data. Build AI-powered search using embeddings and vector databases (Qdrant, ChromaDB) for intelligent querying of specifications, standards, and project documents.
npx skills add majiayu000/claude-skill-registry --skill vector-search-datadrivenconstructi-ddc-skills-for-ai-ag-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Vector Search for Construction ## Overview Based on DDC methodology (Chapter 4.4), this skill implements semantic vector search for construction data. Move beyond keyword matching - find documents and data by meaning, not just words. **Book Reference:** "Современные технологии работы с данными" / "Modern Data Technologies" > "Векторные базы данных позволяют находить семантически похожие документы, даже если они используют разную терминологию." > — DDC Book, Chapter 4.4 ## Quick Start ```python from sentence_transformers import SentenceTransformer from qdrant_client import QdrantClient from qdrant_client.models import VectorParams, Distance, PointStruct # Initialize embedding model model = SentenceTransformer('all-MiniLM-L6-v2') # Create Qdrant client (in-memory for demo) client = QdrantClient(":memory:") # Create collection client.create_collection( collection_name="construction_docs", vectors_config=VectorParams(size=384, distance=Distance.COSINE) ) # Sample construction documents documents = [ "Concrete mix design for C30 grade with water-cement ratio 0.45", "Steel reinforcement specifications for structural columns", "Waterproofing membrane installation for basement walls", "F
- Overview
- Quick Start
- Vector Database Setup
- Qdrant Setup
- ChromaDB Alternative
- Construction-Specific Applications
- Specification Search
- Standards and Codes Search
- Work Item Search (OpenConstructionEstimate)
- RAG for Construction
- Retrieval Augmented Generation
- Document Indexing Pipeline
- Quick Reference
- Embedding Models for Construction
What does the vector-search skill do?
Implement semantic vector search for construction data. Build AI-powered search using embeddings and vector databases (Qdrant, ChromaDB) for intelligent querying of specifications, standards, and project documents.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill vector-search-datadrivenconstructi-ddc-skills-for-ai-ag-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.