Vector Search

What This Skill Does

The Vector Search skill enables OpenClaw to perform semantic retrieval over unstructured construction documentation. Unlike traditional keyword search that relies on exact matching of terms, this skill leverages deep learning embeddings (Sentence-Transformers) and specialized vector databases like Qdrant or ChromaDB. It effectively converts complex technical specifications, building codes, and project standards into multi-dimensional vectors, allowing the agent to find conceptually relevant information even when phrasing varies significantly. Based on DDC methodology (Chapter 4.4), this tool is essential for managing the vast, highly technical documentation landscape of modern construction projects.

Installation

To integrate this skill into your environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/datadrivenconstruction/vector-search Ensure you have a running instance of a vector database (Qdrant or ChromaDB) accessible to your agent, and the necessary Python environment with sentence-transformers installed.

Use Cases

• Compliance Checking: Instantly find relevant safety standards or material requirements within thousands of pages of building codes.
• Knowledge Retrieval: Query unstructured project notes or RFIs to retrieve technical answers regarding specific material grades or installation procedures.
• Data Synthesis: Aggregate disparate technical specs into cohesive summaries for project managers.
• Cross-referencing: Align on-site observations with design-intent documentation by searching for conceptual matches between reports and blueprints.

Example Prompts

1. "Search the project documentation for the structural requirement regarding C30 concrete curing times in basement walls."
2. "Find any safety standards in the library that relate to fire-rated doors in primary escape routes and summarize the installation requirements."
3. "Retrieve the relevant specifications for steel reinforcement in structural columns that matches the latest design revision."

Tips & Limitations

• Embedding Quality: The search accuracy is directly tied to the model choice. 'all-MiniLM-L6-v2' is fast, but for highly domain-specific technical jargon, you may need to fine-tune your embedding model.
• Database Management: Ensure your vector database is periodically indexed or synchronized with your document storage. Stale vector indices lead to hallucinated or missing data.
• Context Windows: While vector search retrieves accurate context, always ensure the retrieved content fits within the LLM's context window for accurate synthesis.
• Semantic Shift: Be aware that synonyms in construction (e.g., 'moisture protection' vs 'waterproofing') are handled well, but highly specific alphanumeric codes might benefit from a hybrid search approach (keyword + vector).