Manages vector embeddings and semantic search operations through the Qdrant REST API.
This skill empowers Claude to interact directly with Qdrant vector databases, enabling high-performance storage, retrieval, and management of vector embeddings for AI-driven applications. It provides a comprehensive suite of tools for building Retrieval-Augmented Generation (RAG) systems, semantic search engines, and recommendation systems. By leveraging direct REST API calls, users can create collections, upsert points with rich metadata, and perform complex similarity searches using various distance metrics like Cosine, Dot Product, and Euclidean distance.
Key Features
01Support for Cosine, Dot Product, Euclidean, and Manhattan distance metrics
02Advanced metadata (payload) filtering for granular search results
03Efficient batch upserting and management of data points
0418 GitHub stars
05High-performance vector embedding storage and retrieval
06Full collection lifecycle management including creation and deletion
Use Cases
01Implementing semantic search engines that understand intent over keywords
02Developing recommendation systems based on vector similarity
03Building Retrieval-Augmented Generation (RAG) pipelines for LLM context
