What is a vector database?
Like a traditional structured database, vector databases can store many different types of data, including text, images, or other media. The difference lies in how the data is stored and queried. While data in regular databases is often stored in an indexed tabular format , data objects in vector databases are represented as high-dimensional numeric vectors . The values contained in a vector can be thought of as parameters, each of which describes a property of the original data . In this way, data sets can be parameterized and compared and clustered according to similarity metrics .
Vector databases make it much easier to categorize and query data based on its broad properties . This is particularly beneficial for machine learning and deep learning systems.
How are vector databases used?
Vector databases serve three key functions in AI and ML applications:
- Vector storage
- Vector indexing
- Similarity search based on querying or prompting
In operation, vector databases work by using multiple algorithms to conduct an approximate nearest neighbor (ANN) search. The algorithms are then gathered in a pipeline to quickly and accurately retrieve and deliver data neighboring the vector that is queried.
For example, an ANN search could look for products that are visually similar in an e-commerce catalog. Additional uses include anomaly detection, classification and semantic search. Because a dataset runs through a model just once, results are returned within milliseconds.
Vector storage
Vector databases store the outputs of an embedding model algorithm, the vector embeddings. They also store each vector’s metadata—including title, description and data type—which can be queried by using metadata filters.
By ingesting and storing these embeddings, the database can facilitate fast retrieval of a similarity search, matching the user’s prompt with a similar vector embedding.
Vector indexing
Vectors need to be indexed to accelerate searches within high-dimensional data spaces. Vector databases create indexes on vector embeddings for search functions.
The vector database indexes vectors by using an ML algorithm. Indexing maps the vectors to new data structures that enable faster similarity or distance searches, such as nearest neighbor searches, between vectors.
Vectors can be indexed by using algorithms such as hierarchical navigable small world (HNSW), locality-sensitive hashing (LSH) or product quantization (PQ).
HNSW is popular as it creates a tree-like structure. Each node of the tree shows a set of vectors complete with the hierarchies in each. Similarities between vectors are shown at the edges between the nodes.
LSH indexes content by using an approximate nearest-neighbor search. For extra speed, the index can be optimized by returning an approximate, but nonexhaustive result.
PQ converts each dataset into a short, memory-efficient representation. Only the short representations are stored, rather than all of the vectors.
Similarity search based on querying or prompting
Query vectors are vector representations of search queries. When a user queries or prompts an AI model, the model computes an embedding of the query or prompt. The database then calculates distances between query vectors and vectors stored in the index to return similar results.
Databases can measure the distance between vectors with various algorithms, such as nearest neighbor search. Measurements can also be based on various similarity metrics, such as cosine similarity.
The database returns the most similar vectors or nearest neighbors to the query vector according to the similarity ranking. These calculations support various machine learning tasks, such as recommendation systems, semantic search, image recognition and other natural language processing tasks.
How do vector databases work?
Vector databases offer many advantages in the field of artificial intelligence and machine learning compared to traditional relational databases . However, there are some challenges in storing and managing vector data. The first major challenge of these databases is converting traditional digital data objects into numerical vectors that accurately represent the properties of these data objects. This is where vector embedding models come into play.
Vectors can be understood as coordinate points in a multidimensional space. The high-dimensional space in which the vectors stored in a vector database are located is called vector embedding . In order to get from a digital data object to a corresponding vector embedding , you need a vector embedding model. A vector embedding model is a specialized machine learning model that analyzes data objects and generates an appropriate vector representation based on their meaning and context.
Let's take a vector database that stores and categorizes words as an example. The words "sushi" and "pasta" have similar semantic meanings despite their different spellings. Accordingly, the embedding model would need to generate similar vector embeddings for the two words . To do this, the model could, for example, analyze the textual contexts in which the two words often appear.
Querying data from the vector database is done in a similar way to entering it. The embedding model generates a suitable vector (or coordinate point in high-dimensional space) for the query itself. Mathematical algorithms that specialize in vectors are then used to find the most obvious vectors. This makes it possible to retrieve not only exact hits from the database, but also data objects whose vectors are similar to the query vector . For example, if you enter "food" as a query, entries for "pasta" and "sushi" might come back. If, on the other hand, you enter "Japanese food," the query vector would be much more similar to the "sushi" vector than to the "pasta" vector.
What are the advantages of vector databases?
Vector databases like ChromaDB offer a number of advantages over traditional relational databases that are particularly valuable for AI applications. Below, we'll look at some of them in more detail.
Efficient similarity search
Representing a data object as a point in a high-dimensional space enables the application of algorithms that are specialized in vectors. This allows nearby vectors (or topic-relevant content) to be found quickly and efficiently. This is essential for applications such as image recognition , where similar images must be identified, or for recommendation systems that suggest similar products or content.
Performance and scalability
Vector database systems often use a range of techniques to efficiently speed up query speed and data processing. In addition to efficiently processing high-dimensional vector data, vector databases are often designed to allow many operations to be performed in parallel . Representing complex data as vectors also allows very complex data structures to be handled efficiently. Overall, these techniques help vector databases to contain and process large amounts of data without significant performance loss.
Integration of machine learning models
Because neural networks often use vectors as input and output, many AI models integrate seamlessly with vector databases . This allows direct storage, management, and query of model output and input, simplifying and accelerating the development and deployment process of AI applications.
Where are vector databases used?
One use case of vector databases that is very relevant today is machine learning and generative AI . In machine learning, vector databases are used to perform similarity searches, which is necessary for tasks such as classification, clustering, and recommendation systems . Models can be trained to quickly identify similar data points and make predictions or decisions based on them. For example, a recommendation algorithm can be based on a vector database to suggest products or content to users that are similar to their previous preferences.
In addition, vector databases can be used to speed up the training of new neural networks . Vector databases make it possible to efficiently manage and search very large training data sets , which significantly improves both the accuracy and training time of the model.
One specific application that benefits from this optimization is generative AI models such as OpenAI's GPT. These use vector databases to identify complex patterns in data and create new content. The efficiency gains gained through vector databases are critical to the performance of these systems.
Comparison with traditional databases
Differences in storage and indexing
Unlike traditional databases that store data in tabular form and rely on exact matches for queries, vector databases store data as vector embeddings . They use similarity metrics for query results, which gives them greater flexibility and efficiency, especially when processing unstructured data (so-called similarity searches).
Advantages of vector databases over traditional databases
Vector databases are able to efficiently handle more complex and high-dimensional search functions. They are more flexible, scalable and offer special features that make them particularly suitable for AI and ML applications.
Conclusion vector database
We think: The future of vector databases is closely linked to the further development of AI and ML. New embedding techniques and the development of hybrid databases that combine traditional database functions with vector databases are key trends that will further increase the performance of these technologies.
Refence Website:
https://www.ionos.com/digitalguide/server/know-how/vector-database/
https://www.cloudflare.com/learning/ai/what-is-vector-database/
https://www.ibm.com/topics/vector-database
https://www.arocom.de/en/technical-terms/kunstliche-intelligenz/vector-database
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