local-rag-search

What problem does it solve?

This Skill addresses the need for private and efficient information retrieval by enabling local Retrieval-Augmented Generation (RAG) using vector search capabilities.

Core Features & Use Cases

• Local Embeddings: Generates embeddings locally, ensuring data privacy.
• Document Indexing: Indexes documents for efficient retrieval.
• Semantic Search: Performs searches based on meaning rather than just keywords.
• Privacy-Preserving: Keeps all data and processing within the local environment.
• Use Case: A researcher needs to query a large, sensitive dataset without uploading it to a cloud service. This Skill allows them to perform semantic searches locally, maintaining data confidentiality.

Quick Start

Use the local-rag-search skill to find information about 'vector databases' within the indexed documents.