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qdrant-vector-search

Perform vector similarity and hybrid search with metadata filtering on Qdrant.

4|Updated Apr 19, 2026
Search Tags:#semantic search#RAG#vector search#similarity search#Qdrant
One-click install
npx skills add https://github.com/ragnarokhaa/hermes --skill qdrant-vector-search-ragnarokhaa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: qdrant-vector-search
Source: https://github.com/ragnarokhaa/hermes/tree/main/hermes-cerul-tech-news-package/hermes-cerul-tech-news-package/hermes-agent/optional-skills/mlops/qdrant
Command: npx skills add https://github.com/ragnarokhaa/hermes --skill qdrant-vector-search-ragnarokhaa

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires qdrant-client>=1.12.0, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the need for high-performance vector similarity search, particularly useful for production-scale RAG systems and semantic search applications.

Core Features & Use Cases

  • Vector Similarity Search: Quickly find the most similar vectors to a query, essential for RAG and semantic search systems.
  • Hybrid Search: Combine vector search with metadata filtering, allowing for complex queries that leverage both vector and attribute-based filtering.
  • Distributed Storage: Leverage horizontal scaling and distributed architecture to support large datasets with high throughput.
  • Use Case: Use this Skill in a knowledge base application to quickly find the most relevant articles to a user query, based on vector similarity.

Quick Start

Initialize the Qdrant client and create a collection for document embeddings. Insert document vectors into the collection. Query the collection for similar documents based on vector similarity.

Frequently Asked Questions about qdrant-vector-search

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I implement semantic search for a large dataset?▼

Implement semantic search by initializing a Qdrant client, creating a collection for document embeddings, inserting vectors, and querying the collection based on vector similarity to retrieve relevant results.

Can I combine vector similarity search with metadata filtering?▼

Yes, you can perform hybrid search to combine vector search with metadata filtering. This allows you to execute complex queries that leverage both vector similarity and attribute-based filtering simultaneously.

Does Qdrant support distributed storage for high-throughput RAG systems?▼

Yes, Qdrant supports distributed storage. It leverages horizontal scaling and a distributed architecture to manage large datasets with high throughput, making it suitable for production-scale RAG systems.

What is the best way to find similar vectors for a user query in a knowledge base?▼

Use vector similarity search to quickly find the most relevant articles matching a user query. Insert your document vectors into a collection and query it to retrieve similar documents efficiently.

Do I need a specific client version to use Qdrant for similarity search?▼

Yes, you need the qdrant-client dependency, specifically version 1.12.0 or higher, to enable fast vector search and similarity capabilities for your large datasets.

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