Vector databases can give organizations the ability to handle unstructured data more effectively. With the proliferation of generative AI and large language models (LLMs), the importance of managing unstructured data has never been more pronounced.
Vector database explained
Vector databases specialize in the storage and retrieval of unstructured data through vector embeddings. These databases perform best in similarity searches, using algorithms like the approximate nearest neighbor (ANN) to arrange data points according to spatial relationships. This arrangement means quick identification of the nearest data points within vast datasets, an important function in various applications.
One of the most common uses of vector databases is building recommender systems. With vector embeddings, these systems make recommendations based on the similarity between users’ preferences and historical data. Chatbots are also improved by vector databases. They use vector representations of text to understand and generate human-like responses, making conversations more natural and context-aware.
Vector databases address hallucination issues in large language models. Models, such as GPT-3, generate text based on patterns in the data they were trained on. While this is exceptional, these models may produce plausible-sounding but incorrect information. Vector databases can help validate the generated content by checking its similarity to trustworthy sources, making LLM-generated content more reliable.
Comparison with other vector search technologies
Vector search libraries: These libraries, like FAISS, provide a collection of algorithms specifically designed for vector search but they can lack the basic functionalities of a database system. While they are efficient at searching and retrieving vectors, they are not suitable for managing large datasets or performing complex queries.
Lightweight vector databases: Built on top of vector search libraries, lightweight vector databases, such as Chroma, are relatively easy to deploy but they tend to have limitations in terms of scalability and performance, making them suitable for smaller-scale applications.
Vector search plugins: Some traditional databases, like Elasticsearch and Pgvector, offer vector search functionality through plugins. While this approach can be convenient, it may impact performance and scalability due to the traditional database’s architecture not being optimized for vector operations.
Purpose-built vector databases: The most advanced category, purpose-built vector databases like Milvus, are specifically designed for vector searching. They offer several advantages, including distributed computing, efficient storage, disaster recovery capabilities, and data persistence. These databases are well-suited for handling the demands of modern applications that rely heavily on vector similarity searches.
Evaluating a vector database
When considering the adoption of a vector database for your organization, it’s important to evaluate several key aspects so that it meets an organization’s specific requirements:
Scalability: Evaluate whether the database supports horizontal scalability, which allows the addition of more nodes to the system as your data grows. Also consider load balancing mechanisms and support for multiple replicas to ensure high availability and fault tolerance.
Functionality: Look for database-oriented features like change data capture (CDC), multi-tenancy support, resource groups, and role-based access control (RBAC). These features are important when managing and securing your data. Organizations must also consider the vector-oriented features, particularly the database’s ability to support multiple index types. Having options for both disk-based and GPU-based indexes can significantly impact the efficiency of vector searches.
Performance: Performance evaluation should be the main part of any assessment. Focus on the database’s ability to perform approximate searches efficiently. Measure metrics such as queries per second (QPS), latency, and recall rate. Benchmarking tools like ANN-Benchmark and VectorDBBench can help you conduct a comprehensive evaluation and make informed decisions based on your performance requirements.
