# Qdrant Vector Search

High-performance vector similarity search engine for RAG and semantic search.

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill qdrant-vector-search -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill qdrant-vector-search -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill qdrant-vector-search -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill qdrant-vector-search -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## Description

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Qdrant - Vector Similarity Search Engine

High-performance vector database written in Rust for production RAG and semantic search.

## When to use Qdrant

**Use Qdrant when:**
- Building production RAG systems requiring low latency
- Need hybrid search (vectors + metadata filtering)
- Require horizontal scaling with sharding/replication
- Want on-premise deployment with full data control
- Need multi-vector storage per record (dense + sparse)
- Building real-time recommendation systems

**Key features:**
- **Rust-powered**: Memory-safe, high performance
- **Rich filtering**: Filter by any payload field during search
- **Multiple vectors**: Dense, sparse, multi-dense per point
- **Quantization**: Scalar, product, binary for memory efficiency
- **Distributed**: Raft consensus, sharding, replication
- **REST + gRPC**: Both APIs with full feature parity

**Use alternatives instead:**
- **Chroma**: Simpler setup, embedded use cases
- **FAISS**: Maximum raw speed, research/batch processing
- **Pinecone**: Fully managed, zero ops preferred
- **Weaviate**: GraphQL preference, built-in vectorizers

## Quick start

### Installation

```bash
# Python client
pip install qdrant-client

# Docker (recommended for development)
docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant

# Docker with persistent storage
docker run -p 6333:6333 -p 6334:6334 \
    -v $(pwd)/qdrant_storage:/qdrant/storage \
    qdrant/qdrant
```

### Basic usage

```python
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct

# Connect to Qdrant
client = QdrantClient(host="localhost", port=6333)

# Create collection
client.create_collection(
    collection_name="documents",
    vectors_config=VectorParams(size=384, distance=Distance.COSINE)
)

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/15-rag/qdrant/SKILL.md