A high-performance skill for building production-ready vector search systems using the modern Weaviate v4 Python client.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install weaviate
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install weaviate using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Weaviate?
This skill provides developers with the essential patterns and configurations needed to implement Weaviate v4, the latest standard for vector databases. It emphasizes the critical shift from v3 to v4, focusing on context-managed connections, collection-based architectures, and efficient module integration. By utilizing Openclaw Skills, developers can rapidly deploy scalable search solutions that leverage both semantic and keyword-based retrieval methods. The skill ensures that every implementation follows modern best practices for vectorization, filtering, and high-performance querying while avoiding deprecated syntax.
Weaviate Use Cases
- Building Retrieval-Augmented Generation (RAG) pipelines for AI applications.
- Implementing hybrid search that combines BM25 keyword search with semantic vectors.
- Managing large-scale batch data ingestion with robust error handling.
- Designing multi-vector schemas for complex data objects requiring multiple embeddings.
- Configuring automated vectorizers and rerankers using third-party AI models.
How Weaviate Works
- Initialize the client using modern v4 context managers to ensure safe connection handling and resource cleanup.
- Define collections and properties using the new configuration classes like Configure and Property instead of legacy schemas.
- Configure specific vectorizer and generative modules, such as OpenAI or Cohere, within the collection definition.
- Execute batch imports using dynamic batching context managers to optimize data throughput and minimize failures.
- Perform complex queries using hybrid search with tunable alpha parameters to balance keyword and semantic results.
- Verify vector generation and metadata using the updated result access patterns in the v4 client.
Weaviate Setup
First, ensure you have the latest client installed via pip:
pip install "weaviate-client>=4.0"
When using Openclaw Skills to connect to your instance, ensure your environment variables are set and use the modern connection pattern:
import weaviate
import os
# Always use a context manager for v4 connections
with weaviate.connect_to_local(
headers={"X-OpenAI-Api-Key": os.environ["OPENAI_API_KEY"]}
) as client:
# Your logic here
pass
Weaviate Data Schema & Taxonomy
The skill organizes data around the concept of Collections which replace the legacy Class structure. Data is managed through the following taxonomy:
| Component | Description |
|---|---|
| Collection | The container for objects (e.g., Article, Product). |
| Property | Data fields defining the object schema and data types. |
| Vector Config | Rules for embedding, supporting Single or Named vectors. |
| Batch | Context-managed logic for bulk data operations and error tracking. |
| Filters | Property-based conditions applied before vector search operations. |
Weaviate Advanced Features
- Named vectors support allowing multiple distinct embedding models for a single data object.
- Dynamic batching with automated error management and retry logic for high-scale ingestion.
- Fine-tuned hybrid search control with precision alpha balancing between semantic and lexical results.
- Pre-filtering capabilities that reduce search space before vector calculations to optimize performance.
- Seamless module configuration for integrated RAG, generative search, and reranking workflows.
SKILL.md
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