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qdrant-vector-search

Perform vector similarity search in Qdrant using cosine distance.

Updated Jun 9, 2026
Search Tags:#semantic search#vector search#Qdrant#vector similarity#RAG systems
One-click install
npx skills add https://github.com/zhouboyu-xreal/Hermes-Memory --skill qdrant-vector-search-zhouboyu-xreal
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: qdrant-vector-search
Source: https://github.com/zhouboyu-xreal/Hermes-Memory/tree/main/optional-skills/mlops/qdrant
Command: npx skills add https://github.com/zhouboyu-xreal/Hermes-Memory --skill qdrant-vector-search-zhouboyu-xreal

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill empowers users to leverage Qdrant's high-performance vector search capabilities for efficient data retrieval in RAG systems and semantic search applications.

Core Features & Use Cases

  • Vector Similarity Search: Quickly find similar vectors in a database with precise control over the similarity threshold.
  • Hybrid Search: Combine vector and metadata filtering for nuanced search queries.
  • Scalable Storage: Store and search vectors at scale using Qdrant's distributed architecture.
  • Use Case: When building a recommendation engine that needs to serve personalized content based on user preferences, Qdrant can efficiently search through millions of data points to find the most relevant suggestions.

Quick Start

Run the following command to perform a basic vector search in your Qdrant database:

python search.py -c documents -q "query_vector"

Frequently Asked Questions about qdrant-vector-search

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

FAQPage Schema
How do I perform vector similarity search in a RAG system?▼

You can perform vector similarity search by running a query against a Qdrant collection to find the closest matches. This Skill uses Qdrant to retrieve semantically similar vectors using cosine distance.

Can I filter search results by metadata while doing vector search?▼

Yes, you can combine vector similarity search with metadata filtering to execute hybrid search. This enables nuanced queries by narrowing vector search results based on specific metadata attributes.

Do I need qdrant-client to run vector similarity searches?▼

Yes, the qdrant-client dependency is required to interact with the Qdrant vector search engine. It provides the necessary interface to store vectors and execute similarity searches within your Python application.

What is the best way to handle large-scale semantic search for recommendation engines?▼

Using a high-performance vector search engine like Qdrant is ideal for large-scale semantic search. It leverages a distributed architecture to search through millions of data points and find relevant suggestions.

How do I query a Qdrant collection from the command line?▼

You can query a Qdrant collection from the command line by executing the search script with the collection name and query vector. Run `python search.py -c documents -q "query_vector"` to retrieve matching results.

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