Vector database: Difference between revisions

Complex data, which includes unstructured forms like documents, images, videos, and plain text, is growing rapidly. Traditional databases designed for structured data struggle to effectively store and analyze complex data, often requiring extensive keyword and metadata classification. Machine Learning (ML) techniques can address this issue by transforming complex data into vector embeddings, which describe data objects in numerous dimensions.

Vector databases are designed specifically to handle vector embeddings. These databases index vectors, allowing for easy search and retrieval by comparing values and identifying those most similar to one another. Although challenging to implement, there are various solutions available for vector databases, ranging from plugins and open-source projects to fully-managed services.

A vector database is a type of database that indexes and stores vector embeddings for efficient retrieval and similarity search. In addition to traditional CRUD (create, read, update, and delete) operations and metadata filtering, vector databases enable the organization and comparison of any vector to one another or to the vector of a search query. This capability allows vector databases to excel at similarity search or "vector search," providing more comprehensive search results that would not be possible with traditional search technology.

Vector databases offer several use cases, including:

Unlike lexical search, which relies on exact word or string matches, semantic search uses the meaning and context of a search query or question. Vector databases use Natural Language Processing models to store and index vector embeddings, allowing for more accurate and relevant search results.

Vector databases facilitate search and retrieval of unstructured data like images, audio, video, and JSON, which can be challenging to classify and store in traditional databases.

By finding similar items based on nearest matches, vector databases are suitable for powering ranking and recommendation engines for online retailers and streaming media services.

Vector similarity search can be used to find near-duplicate records for applications such as removing duplicate items from a catalog.

Vector databases can identify anomalies in applications used for threat assessment, fraud detection, and IT operations by finding objects that are distant or dissimilar from expected results.

A vector database must possess certain capabilities to be effective:

Vector databases must utilize algorithms designed to index and retrieve vectors efficiently. These algorithms can be optimized depending on the use case requirements. Common metrics used in vector indexes include Euclidean distance, cosine similarity, and dot products. Approximate Nearest Neighbor (ANN) search can balance precision and performance by approximating and retrieving the most similar vectors.

Single-stage filtering combines the accuracy of pre-filtering with the speed of post-filtering by merging vector and metadata indexes into a single index. This feature is essential for an effective vector database.

To achieve scalable and cost-effective performance, vector databases must support horizontal scaling through data sharding. By dividing vectors into shards and replicas, vector databases can scale across multiple machines, allowing for the efficient searching of large datasets.

Replication is a technique used by vector databases to handle multiple requests efficiently. This approach increases the capacity of the system to process simultaneous or rapidly occurring vector search requests.

Sharding enables a vector database to distribute work across several pods, which can perform vector searches more quickly. Replicas, on the other hand, create copies of the entire set of pods to handle more requests concurrently. In a scenario where multiple search requests are incoming, replicas can help maintain system performance by providing additional processing capacity.

Replicas also improve the availability of a vector database. As machines are prone to failure, it is crucial for the system to recover from these events quickly. By distributing replicas across different [[availability zones]], the database can achieve high availability and resilience against simultaneous failures. Users also have a responsibility to ensure sufficient replica capacity, so the remaining replicas can maintain acceptable latency during a failure event.

Hybrid storage configurations help address the challenges of memory cost and search latency associated with vector searches. These configurations balance the need for speed and accuracy with the requirement for cost-effective infrastructure.

In a hybrid storage system, a compressed vector index is stored in memory (RAM), while the original, full-resolution vector index is stored on disk. The in-memory index is used to locate a small set of candidates, which are then searched within the complete index on disk. This approach enables rapid and accurate search results while reducing infrastructure costs by up to 10 times.

Hybrid storage allows for more vectors to be stored across the same data footprint, thus lowering the operational cost of a vector database. This is achieved by enhancing overall storage capacity without negatively affecting database performance.

APIs play a crucial role in allowing developers to interact with vector databases without the need to build and maintain vector search functionality themselves. By utilizing APIs, developers can focus on optimizing their applications.

Vector databases often offer REST APIs, which add flexibility by allowing the database to be accessed from any environment capable of making HTTPS calls. Developers can also interact with the vector database using clients in various programming languages, such as Python, Java, and Go. These APIs enable actions such as upserting vectors, retrieving query results, or deleting vectors to be performed seamlessly within the context of an application.