# Qdrant

## Docs

- [Introduction to Qdrant](https://mintlify.wiki/qdrant/qdrant/introduction.md): Qdrant is a high-performance vector search engine and database written in Rust, designed for production-ready semantic search, recommendations, and RAG applications.
- [Quick Start Guide](https://mintlify.wiki/qdrant/qdrant/quickstart.md): Get started with Qdrant in minutes. Learn how to run Qdrant with Docker, create your first collection, insert vectors, and perform similarity search.
- [Installation Guide](https://mintlify.wiki/qdrant/qdrant/installation.md): Complete guide to installing and deploying Qdrant using Docker, Docker Compose, Kubernetes, from source, or with Qdrant Cloud.
- [Collections](https://mintlify.wiki/qdrant/qdrant/concepts/collections.md): Understanding collections in Qdrant - the primary organizational unit for storing and managing vector data
- [Points](https://mintlify.wiki/qdrant/qdrant/concepts/points.md): Understanding points in Qdrant - the fundamental unit of data combining vectors, IDs, and payloads
- [Vectors](https://mintlify.wiki/qdrant/qdrant/concepts/vectors.md): Understanding vector types in Qdrant - dense, sparse, and multi-vectors for semantic search
- [Payloads](https://mintlify.wiki/qdrant/qdrant/concepts/payloads.md): Understanding payloads in Qdrant - JSON metadata for filtering, retrieval, and enriching vector search results
- [Indexing](https://mintlify.wiki/qdrant/qdrant/concepts/indexing.md): Understanding indexing in Qdrant - HNSW vector indexes and payload indexes for fast similarity search and filtering
- [Distance Metrics](https://mintlify.wiki/qdrant/qdrant/concepts/distance-metrics.md): Understanding distance metrics in Qdrant - Cosine, Euclidean, Dot Product, and Manhattan for vector similarity comparison
- [Create Collection](https://mintlify.wiki/qdrant/qdrant/guides/create-collection.md): Learn how to create collections in Qdrant with various vector configurations including dense vectors, sparse vectors, and named vectors.
- [Insert Points](https://mintlify.wiki/qdrant/qdrant/guides/insert-points.md): Learn how to insert and upsert points (vectors with payloads) into Qdrant collections using the REST API and Python client.
- [Search Points](https://mintlify.wiki/qdrant/qdrant/guides/search.md): Learn how to perform vector similarity search in Qdrant using the search API with various parameters and filters.
- [Filtering](https://mintlify.wiki/qdrant/qdrant/guides/filtering.md): Learn how to filter search results in Qdrant using various filter conditions including field matches, ranges, geo filters, and nested conditions.
- [Batch Operations](https://mintlify.wiki/qdrant/qdrant/guides/batch-operations.md): Learn how to perform efficient batch operations in Qdrant including batch insert, batch update, batch delete, and scrolling through large datasets.
- [Update and Delete](https://mintlify.wiki/qdrant/qdrant/guides/update-delete.md): Learn how to update payloads, update vectors, delete points by ID, and delete points by filter in Qdrant.
- [Hybrid Search](https://mintlify.wiki/qdrant/qdrant/advanced/hybrid-search.md): Combine dense and sparse vectors for more powerful search capabilities that leverage both semantic understanding and keyword matching
- [Sparse Vectors](https://mintlify.wiki/qdrant/qdrant/advanced/sparse-vectors.md): Learn how to use sparse vectors for efficient keyword-based search and BM25-like ranking in Qdrant
- [Vector Quantization](https://mintlify.wiki/qdrant/qdrant/advanced/quantization.md): Reduce memory usage by up to 97% with scalar, product, and binary quantization while maintaining search quality
- [Multi-Tenancy](https://mintlify.wiki/qdrant/qdrant/advanced/multitenancy.md): Implement efficient multi-tenant architectures using payload filtering, partitioning strategies, and tenant-specific optimizations
- [Recommendation API](https://mintlify.wiki/qdrant/qdrant/advanced/recommendations.md): Build sophisticated recommendation systems using positive/negative examples, context pairs, and discovery search
- [Docker Deployment](https://mintlify.wiki/qdrant/qdrant/deployment/docker.md): Deploy Qdrant using Docker with volume mounts, environment variables, and Docker Compose
- [Kubernetes Deployment](https://mintlify.wiki/qdrant/qdrant/deployment/kubernetes.md): Deploy Qdrant on Kubernetes with StatefulSets, persistent volumes, and service configuration
- [Distributed Deployment](https://mintlify.wiki/qdrant/qdrant/deployment/distributed.md): Deploy Qdrant in distributed mode with clustering, sharding, replication, and Raft consensus
- [Configuration Reference](https://mintlify.wiki/qdrant/qdrant/deployment/configuration.md): Complete configuration reference for Qdrant including storage, performance, networking, and optimization settings
- [Security Configuration](https://mintlify.wiki/qdrant/qdrant/deployment/security.md): Secure your Qdrant deployment with API key authentication, TLS/SSL encryption, and network security best practices
- [Monitoring](https://mintlify.wiki/qdrant/qdrant/operations/monitoring.md): Monitor Qdrant performance, metrics, and system health with Prometheus integration and telemetry
- [Backup and Restore](https://mintlify.wiki/qdrant/qdrant/operations/backup-restore.md): Backup strategies and restore procedures for Qdrant data protection and disaster recovery
- [Snapshots](https://mintlify.wiki/qdrant/qdrant/operations/snapshots.md): Create, manage, and recover from collection and shard snapshots in Qdrant
- [Scaling](https://mintlify.wiki/qdrant/qdrant/operations/scaling.md): Horizontal scaling, cluster management, shard migration, and zero-downtime updates in Qdrant
- [Performance Tuning](https://mintlify.wiki/qdrant/qdrant/operations/performance-tuning.md): Optimize Qdrant performance with indexing parameters, search threads, memory settings, and CPU budgets
- [API Overview](https://mintlify.wiki/qdrant/qdrant/api/overview.md): Learn about Qdrant's REST and gRPC APIs for interacting with the vector database
- [Authentication](https://mintlify.wiki/qdrant/qdrant/api/authentication.md): Secure your Qdrant instance with API keys, JWT tokens, and TLS client certificates
- [Collections API](https://mintlify.wiki/qdrant/qdrant/api/rest/collections.md): REST API endpoints for managing collections in Qdrant
- [Points API](https://mintlify.wiki/qdrant/qdrant/api/rest/points.md): REST API endpoints for managing points (vectors with payloads) in Qdrant collections
- [Search API](https://mintlify.wiki/qdrant/qdrant/api/rest/search.md): REST API endpoints for vector similarity search, recommendations, and query operations in Qdrant
- [Snapshots API](https://mintlify.wiki/qdrant/qdrant/api/rest/snapshots.md): REST API endpoints for creating, managing, and restoring snapshots in Qdrant
- [Cluster API](https://mintlify.wiki/qdrant/qdrant/api/rest/cluster.md): REST API endpoints for managing Qdrant cluster operations and distributed deployment
- [gRPC API Overview](https://mintlify.wiki/qdrant/qdrant/api/grpc/overview.md): High-performance gRPC interface for Qdrant vector database with efficient binary protocol and bidirectional streaming support
- [Collections Service](https://mintlify.wiki/qdrant/qdrant/api/grpc/collections.md): gRPC API for creating, managing, and configuring Qdrant collections with vector parameters, sharding, and replication
- [Points Service](https://mintlify.wiki/qdrant/qdrant/api/grpc/points.md): gRPC API for inserting, searching, and managing vector points with filtering, recommendations, and batch operations
- [Python Client](https://mintlify.wiki/qdrant/qdrant/api/clients/python.md): Official Python client for Qdrant with full async support, type hints, and high-performance gRPC interface
- [JavaScript/TypeScript Client](https://mintlify.wiki/qdrant/qdrant/api/clients/javascript.md): Official JavaScript and TypeScript client for Qdrant with full type definitions and modern async/await API
- [Rust Client](https://mintlify.wiki/qdrant/qdrant/api/clients/rust.md): Official Rust client for Qdrant with native gRPC support, zero-copy operations, and high performance
- [Go Client](https://mintlify.wiki/qdrant/qdrant/api/clients/go.md): Official Go client for Qdrant with native gRPC support, connection pooling, and idiomatic Go interfaces
- [.NET Client](https://mintlify.wiki/qdrant/qdrant/api/clients/dotnet.md): Official .NET client for Qdrant with full async support, LINQ integration, and strongly-typed C# interfaces
- [Java Client](https://mintlify.wiki/qdrant/qdrant/api/clients/java.md): Official Java client for Qdrant with gRPC support, reactive programming, and comprehensive type safety