> ## Documentation Index > Fetch the complete documentation index at: https://mintlify.com/qdrant/qdrant/llms.txt > Use this file to discover all available pages before exploring further. # Qdrant Vector Database Documentation > High-performance vector database and search engine for next-generation AI applications

Vector Search Engine for Next-Generation AI

Build powerful semantic search, recommendation systems, and RAG applications with Qdrant — a high-performance vector database designed for production workloads. 

              
                {`from qdrant_client import QdrantClient

                                client = QdrantClient(":memory:")

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

                                # Search with filtering
                                results = client.query_points(
                                  collection_name="documents",
                                  query=[0.2, 0.1, 0.9, ...],
                                  limit=5
                                )`}

Quick start

Get Qdrant running in minutes

Start a Qdrant instance locally using Docker: ```bash theme={null} docker run -p 6333:6333 qdrant/qdrant ``` This starts an insecure deployment for development. For production, see our [security guide](/deployment/security). Install the Qdrant client for your language: ```bash Python theme={null} pip install qdrant-client ``` ```bash JavaScript theme={null} npm install @qdrant/js-client-rest ``` ```bash Rust theme={null} cargo add qdrant-client ``` Connect to Qdrant and create a collection to store vectors: ```python theme={null} from qdrant_client import QdrantClient from qdrant_client.models import Distance, VectorParams client = QdrantClient("http://localhost:6333") client.create_collection( collection_name="my_collection", vectors_config=VectorParams(size=384, distance=Distance.COSINE) ) ``` ```json theme={null} { "result": true, "status": "ok", "time": 0.031 } ``` Add vectors with payloads and perform similarity search: ```python theme={null} from qdrant_client.models import PointStruct # Insert points client.upsert( collection_name="my_collection", points=[ PointStruct(id=1, vector=[0.05, 0.61, 0.76, ...], payload={"city": "Berlin"}), PointStruct(id=2, vector=[0.19, 0.81, 0.75, ...], payload={"city": "London"}), ] ) # Search results = client.query_points( collection_name="my_collection", query=[0.2, 0.1, 0.9, ...], limit=3 ) ``` Learn more in our [quickstart guide](/quickstart).

Explore by topic

Deep dive into Qdrant's capabilities

Understand collections, points, vectors, and payloads Insert, search, filter, and manage your vector data Combine dense and sparse vectors for better results Reduce memory usage by up to 97% with quantization Scale horizontally with sharding and replication Optimize search speed and resource usage

Choose your interface

Access Qdrant through REST, gRPC, or client libraries

HTTP API with OpenAPI specification for easy integration High-performance gRPC interface for production workloads Full-featured client with async support and type hints TypeScript-ready client for Node.js and browsers Native Rust client with zero-copy deserialization Go, .NET, Java, and community-maintained clients

Ready to build with Qdrant?

Start with our quickstart guide or explore the full API reference to integrate vector search into your application.