Introduction:

In the era of AI-driven applications and unstructured data management, vector databases have become essential tools for enabling semantic search and similarity matching. Qdrant is one such cutting-edge, open-source vector database designed to simplify working with high-dimensional data embeddings. Whether you're building a recommendation system, integrating semantic search, or powering an AI chatbot, Qdrant makes it seamless to store, query, and manage vector embeddings at scale.

At FutureSmart, we pride ourselves on staying adaptable and embracing new technologies. If a solution has great potential and delivers value, we’re always eager to explore and adopt it. After testing Qdrant ourselves and seeing its impressive results, we’ve prepared this comprehensive guide to walk you through its installation and setup on Docker and locally. While the blog primarily focuses on on-premise deployment, it’s worth noting that Qdrant also offers a robust cloud platform that simplifies scaling and management for production use cases.

What is a Vector Database

Let us first understand what a Vector database is, and what advantages it has over traditional dbs. In simple terms, a vector database is a specialized database designed to store and work with vector embedding. Vector embeddings are numerical representations of data that capture its meaning, features, or relationships. These embeddings are often generated by AI models and are used to process unstructured data like text, images, audio, or videos.

Unlike traditional databases, which rely on exact matches (like finding a name or ID), vector databases focus on finding similarity between data points, even when the input isn’t identical. Unlike traditional relational databases, vector databases enable semantic search, allowing advanced similarity retrieval and unstructured data management.

The above image is an example of how we can still find relevant or similar objects even if they are structurally completely different. If it were JSON objects instead of plain text, it would still be able to to do it. That’s the power of similarity search or vector search. You can try out this similarity checker yourself at AI Demos Playground.

What is Qdrant

Among the top open-source vector databases, Qdrant stands out as a powerful, Rust-based vector database and similarity search engine. It supports seamless integration with LangChain for building sophisticated AI solutions. It offers robust performance, a user-friendly API, and support for Python. With its use of the indexing, Qdrant delivers both speed and precision, making it a competitive choice for modern applications.

Key Features of Qdrant:

1. HNSW Indexing: Uses the Hierarchical Navigable Small World (HNSW) algorithm for fast and accurate similarity searches.
2. Distance Metrics: Supports Cosine Similarity, Dot Product, and Euclidean Distance for flexible vector search.
3. Free Vector Database: Qdrant is open-source and perfect for creating a local vector database.
4. Multi-Language APIs: Offers APIs for Python, JavaScript/TypeScript, Rust, and Go, ensuring smooth integration with various tech stacks.
5. Recommendation API: Includes a built-in API for creating efficient recommendation systems.
6. Scalability and Production-Ready: Designed for real-world applications, it scales to handle millions or billions of vectors seamlessly.
7. Hybrid Compatibility: Works well with databases like PostgreSQL for blending relational and vector data.

Setting Up the Environment:

We'll cover two approaches to use Qdrant: