using-weaviate

What problem does it solve?

This Skill provides a streamlined way to interact with Weaviate, a powerful vector database, enabling efficient storage, retrieval, and querying of AI-generated embeddings for advanced applications.

Core Features & Use Cases

• Vector Storage & Retrieval: Store and search through high-dimensional vector embeddings for semantic similarity.
• Hybrid Search: Combine vector search with traditional keyword (BM25) search for more relevant results.
• RAG Pipelines: Power Retrieval-Augmented Generation by fetching relevant context from Weaviate to ground LLM responses.
• Use Case: Build a semantic search engine for your company's knowledge base, allowing employees to find information using natural language queries instead of exact keywords.

Quick Start

Connect to your Weaviate instance and create a new collection named 'Documents' with OpenAI vectorization.