Overview of Open-Source Vector Databases

So far, we have learned how to generate vector embeddings for various types of data and the benefits they provide. However, where do we store this vectorized data? Vector databases are specialized databases designed to efficiently store, index, search, and retrieve vector data.

You might wonder why traditional databases can’t handle vector data efficiently and why we need a specialized database. Let’s explore.

Why not traditional databases?

Traditional databases, such as relational databases (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., MongoDB, Cassandra), are not well-suited for storing and querying vectorized data for several reasons. These reasons include data structure and indexing techniques, querying capabilities, performance and scalability, and especially the applications and use cases for which vector embeddings are used.

Data structure and indexing

Traditional databases are designed to store structured data in rows and columns, which works well for categorical and numerical data but not for high-dimensional vector data. While vector databases are specifically designed to store high-dimensional vector data, they use data structures such as arrays or specialized formats to store vectors.

Traditional databases use indexing mechanisms like B-trees, hash indexes, and R-trees optimized for range queries and exact match lookups. These methods are not efficient for high-dimensional spaces where similarity search is required. Vector databases employ advanced indexing techniques like HNSW (Hierarchical Navigable Small World graphs), IVF (Inverted File Index), and PQ (Product Quantization) that are optimized for similarity search in high-dimensional spaces.

Querying capabilities

Traditional databases are optimized for exact match queries (e.g., find the record with ID = 123) and range queries (e.g., find all records where age > 30). Vector databases are designed for similarity search, allowing efficient retrieval of vectors close to a given query vector.

Traditional databases lack built-in capabilities for similarity search, where the goal is to find data points similar to a query vector based on distance metrics like cosine similarity or Euclidean distance. Vector databases, on the other hand, are designed to support various distance metrics and can efficiently compute these metrics across millions or billions of vectors.

Performance and scalability

Storing and querying high-dimensional vectors in traditional databases can lead to performance bottlenecks due to inefficient indexing and a lack of optimization for vector operations. Vector databases are optimized for high-performance vector operations, enabling fast similarity searches even with large datasets.

Handling large-scale vector data requires specialized data structures and algorithms to ensure scalability and fast retrieval times, which traditional databases lack. Many vector databases are built with scalability in mind, often supporting distributed storage and computation to handle large-scale data efficiently.

Use cases and applications

Traditional databases excel in applications requiring structured data management, such as transactional systems, reporting, and traditional business applications. Vector databases are specially designed for applications involving unstructured data like text, images, audio, and videos, where data is represented as high-dimensional vectors.

Traditional databases do not well support use cases involving complex, unstructured data or requiring advanced similarity search. Vector databases are specially designed for advanced use cases such as image and video search, recommendation systems, natural language processing, and other AI-driven applications.

Open-source vector databases

Several open-source vector databases have gained popularity due to their robust features and active communities. In this lesson, we’ll discuss some of the most notable ones shown in the following illustration:

Chroma DB

Chroma DB is a lightweight and user-friendly vector database designed for similarity search and storage of embeddings. It aims to simplify managing and querying vector data while offering efficient search capabilities. Chroma makes the development of LLM applications easy by allowing knowledge, facts, and skills to be seamlessly integrated into LLMs.

The following are the prominent features of Chroma DB:

• Easy integration with LLMs: ChromaDB is designed to integrate smoothly with large language models (LLMs). It can generate embeddings (vector representations) from text data with its default embedding model and efficiently store and retrieve these embeddings for use in various applications. It also provides the option to choose a specific embedding model, including multimodal embedding modal, or define custom embedding functions.

• Document and embedding storage: ChromaDB stores both the original documents and their corresponding embeddings. This dual storage system ensures that the text and its vector representation are readily available for efficient query processing and retrieval.

• Fast and accurate nearest neighbor searches: ChromaDB is designed specifically for applications that require fast and accurate nearest neighbor searches. Its optimized algorithms and data structures ensure quick retrieval of similar items from large datasets, making it ideal for recommendation systems, search engines, and real-time data analysis.

FAISS (Facebook AI Similarity Search)

FAISS is a powerful library developed by Facebook AI Research for similarity search and clustering of dense vectors. It is renowned for its high performance and scalability, particularly with large-scale datasets.

FAISS provides a range of similarity search methods designed to meet various trade-offs in usage scenarios. One notable feature of FAISS is its optimization for memory usage and speed, making it efficient for handling large-scale datasets. FAISS incorporates state-of-the-art GPU implementations for its most critical indexing methods, leveraging the computational power of GPUs to accelerate search operations.

The following are the prominent features of FAISS:

• In-memory indexing: FAISS builds indexes in RAM, which allows for extremely fast retrieval times. This is particularly beneficial for real-time applications where low latency is crucial.

• High flexibility: FAISS offers high flexibility with support for various indexing techniques and search parameters. It employs vector compression techniques to manage memory usage effectively and offers the flexibility to trade speed and accuracy. This allows users to customize it to handle different data search precision levels and computational resources.

Qdrant

Qdrant is a highly scalable and distributed vector database optimized for similarity searches in high-dimensional data. It provides real-time search capabilities and supports efficient storage and retrieval of vectors. The diagram below provides an overview of Qdrant’s main components, introducing key terminologies for understanding its functionality:

• Collections: These are named sets of points used for searching. Each collection consists of vectors with associated payloads. All vectors within a collection must share the same dimensionality and metric.

• Distance metrics: When creating a collection, users select distance metrics to quantify vector similarities. These metrics depend on factors such as the vectors’ origins and the neural network used for encoding queries.

• Points: These are central entities within Qdrant, comprising a vector and, optionally, an ID and payload.

• ID: These are unique identifiers for vectors.

• Vector: These are high-dimensional data representations, such as images, sounds, documents, or videos.

• Payload: It is the additional JSON data associated with a vector.

• Storage: Qdrant supports in-memory storage (storing vectors in RAM for high-speed access) and memmap storage (creating a virtual address space linked to a disk file).

• Clients: These are the supported programming languages for connecting to Qdrant.

The following are the prominent features of Qdrant:

• Multiple language support: Qdrant supports various programming languages, including Python, Rust, Go, and TypeScript. This flexibility allows developers to choose their preferred language and integrate Qdrant into their existing workflows.

• Flexible storage options: Qdrant offers both in-memory and memory-mapped storage options. This flexibility helps balance performance needs with resource availability.

• Rich metadata support: Qdrant can associate rich metadata (e.g., image URI, species) with vectors, enhancing the context and making the data more informative for machine learning models.

Milvus

Milvus is an open-source vector database designed for AI applications. It offers hybrid search capabilities by combining vector similarity search with structured data filtering. Milvus employs a multi-layered architecture tailored to efficiently manage and process vector data, ensuring scalability, tunability, and data isolation, as we can see in the illustration below.

• Access layer: This initial contact point handles external requests via stateless proxies, managing client connections, verification, and load balancing. These proxies facilitate Milvus’s comprehensive API suite, routing responses back to users after processing requests.

• Coordinator service: As the central command hub, this service coordinates load balancing and data management across four coordinators. These coordinators oversee tasks related to data, queries, and indexing.

  • The root coordinator manages data tasks and global timestamps.

  • The query coordinator oversees query nodes for search operations.

  • The data coordinator handles data nodes and metadata.

  • The index coordinator maintains index nodes and metadata.

• Worker nodes: These scalable pods execute tasks directed by coordinators, dynamically adapting to changing demands in data, queries, and indexing. They play a crucial role in supporting Milvus’s scalability and tunability.

• Object storage layer: This is essential for data persistence; this layer encompasses:

  • Metastore: Utilizing etcd for metadata snapshots and system health checks.

  • Log broker: Facilitating streaming data persistence and recovery, employing Pulsar or RocksDB.

  • Object storage: Storing log snapshots, index files, and query results, compatible with services like AWS S3, Azure Blob Storage, and MinIO.

The following are the prominent features of Milvus:

• Support for distributed deployment: Milvus supports distributed deployment, allowing it to scale horizontally across multiple nodes. This enables the handling of large datasets and high query loads by distributing data and computational tasks. It ensures fault tolerance and high availability, making it ideal for big data applications.

• Variety of index types: Milvus supports a wide range of index types, including IVF, HNSW, and PQ. This flexibility allows users to choose the best indexing strategy based on their specific data characteristics and query requirements, optimizing performance and accuracy for diverse use cases.

• Rich ecosystem integration: Milvus integrates well with other data ecosystems and tools, facilitating seamless data management and retrieval. It supports various data sources and can be integrated into existing data pipelines.

Key considerations for choosing a vector database

With several options available, choosing the right vector database for our project can be daunting. Exploring the below key considerations can help you make an informed decision.

• Use case compatibility: The first step in selecting a vector database is identifying your project’s specific use case and requirements. Consider the types of data you’ll be working with, the volume of data, and the types of queries you’ll need to perform. For example, if your project involves natural language processing tasks, you may prioritize databases with strong support for text-based queries.

• Scalability and performance: Scalability and performance are critical factors, especially for projects dealing with large datasets or high query volumes. Look for databases that offer efficient indexing and search algorithms, as well as support for distributed architectures. Additionally, consider the database’s performance benchmarks and its ability to handle real-time queries.

• Deployment options: Consider your deployment preferences when choosing a vector database. Some databases might offer cloud-based solutions with managed services, while others might be better suited for self-hosted deployments. Evaluate the scalability, reliability, and cost implications of each deployment option to find the best fit for your project.

• Integration and ecosystem: Integration with existing tools and frameworks is essential for seamless development and deployment. Look for vector databases that offer robust APIs, client libraries, and support for popular programming languages. Additionally, consider the availability of documentation, community support, and third-party integrations within the database ecosystem.

• Features and functionality: Evaluate the features and functionality offered by each vector database to ensure they align with your project requirements. Look for capabilities such as support for different distance metrics, efficient indexing and retrieval mechanisms, and built-in support for metadata management. Additionally, consider any specialized features or optimizations tailored to specific use cases.

In this lesson, we learned about the importance of vector databases and their purposes. Choosing the right vector database is a crucial decision that can significantly impact the success of a project. By considering factors such as use case compatibility, scalability, deployment options, integration, and features, we can make an informed decision that meets our project’s needs. We should evaluate multiple options, experiment with different databases, and leverage community resources to ensure we find the best fit for our project. Remember that the choice of vector database is not permanent and can be revisited as our project evolves and our requirements change. In the next lesson, we’ll walk you through Chroma DB, a simple database we’ll use in our dummy projects in this course.