Can I filter search results by metadata?

Absolutely, the skill includes support for advanced payload filtering using 'must', 'should', and 'must_not' logic to refine your vector search results.

What is the Qdrant skill for Claude Code?

It is a specialized capability that allows Claude to manage and query Qdrant vector databases via its REST API using curl commands for vector-based operations.

Which distance metrics are supported?

The skill supports Cosine similarity, Dot product, Euclidean distance, and Manhattan distance metrics for comparing vectors.

Can I use this for RAG applications?

Yes, it is specifically designed to facilitate Retrieval-Augmented Generation by storing and retrieving relevant context through vector similarity searches.

Does it support Qdrant Cloud?

Yes, it is compatible with both Qdrant Cloud clusters and self-hosted instances (e.g., running via Docker) using the appropriate URL and API key.

Qdrant Vector Database

Name: Qdrant Vector Database
Author: vm0-ai

byvm0-ai

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数据科学与机器学习

Manages vector embeddings and semantic search operations through the Qdrant REST API.

关于

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.

主要功能

Support for Cosine, Dot Product, Euclidean, and Manhattan distance metrics
Advanced metadata (payload) filtering for granular search results
Efficient batch upserting and management of data points
18 GitHub stars
High-performance vector embedding storage and retrieval
Full collection lifecycle management including creation and deletion

使用场景

Implementing semantic search engines that understand intent over keywords
Developing recommendation systems based on vector similarity
Building Retrieval-Augmented Generation (RAG) pipelines for LLM context

关于

主要功能

Support for Cosine, Dot Product, Euclidean, and Manhattan distance metrics
Advanced metadata (payload) filtering for granular search results
Efficient batch upserting and management of data points
18 GitHub stars
High-performance vector embedding storage and retrieval
Full collection lifecycle management including creation and deletion

使用场景

Implementing semantic search engines that understand intent over keywords
Developing recommendation systems based on vector similarity
Building Retrieval-Augmented Generation (RAG) pipelines for LLM context