In the field of artificial intelligence, there is a large amount of data that needs to be processed effectively. As we delve deeper into AI applications such as image recognition, voice search, or recommendation engines, the nature of the data becomes more complex. This is where vector databases come into play. Unlike traditional databases, which store scalar values, vector databases are specifically designed to handle multidimensional data points (often called vectors). These vectors represent data in multiple dimensions and can be thought of as arrows pointing to specific directions and sizes in space.
As the digital age propels us into an era dominated by artificial intelligence and machine learning, vector databases have become an indispensable tool for storing, searching, and analyzing high-dimensional data vectors. This article aims to provide a comprehensive introduction to vector databases and introduce the best vector databases available in 2023.
What is a vector database
A vector database is a special type of database that stores information in the form of multidimensional vectors. Depending on the complexity and detail of the data, the number of dimensions of each vector varies widely, from a few to thousands. This data may include text, images, audio, and video, which are converted into vectors using various processes such as machine learning models, word embeddings, or feature extraction techniques.
The main advantage of a vector database is its ability to quickly and accurately locate and retrieve data based on their vector proximity or similarity. This allows searches based on semantic or contextual relevance, rather than relying solely on exact matches or set criteria as in traditional databases.
How vector databases work
Traditional databases store simple data in tabular format, whereas vector databases handle complex data called vectors and use unique search methods.
Regular databases search for exact data matches, while vector databases use a specific similarity measure to find the closest match. Vector databases use a special search technique called Approximate Nearest Neighbor search, which includes methods such as hashing and graph-based search.
To truly understand how a vector database works, and how it differs from traditional relational databases such as SQL, we must first understand the concept of embedding.
Unstructured data such as text, images, and audio lack predefined formats, which creates challenges for traditional databases. In order to leverage this data in artificial intelligence and machine learning applications, we need to convert it into a numerical representation using embeddings.
Embedding is like giving each item (whether it’s a word, image, or something else) a unique high-dimensional numerical representation that captures its meaning or essence. This number helps computers understand and compare these items in a more efficient and meaningful way.
This embedding process is usually implemented using a special neural network designed for the task. For example, word embeddings convert words into vectors so that words with similar meanings are closer in the vector space. This transformation allows the algorithm to understand relationships and similarities between items, and settings can be encoded for different data, such as CLIP.
Essentially, embeddings act as a bridge to convert non-numeric data into a form that machine learning models can use, allowing them to more effectively identify patterns and relationships in the data.
Vector database use cases
Vector database is very efficient in implementing "similarity search", so it can be used in some of the following scenarios:
- Recommendation system: Vector database can be used to store feature vectors of users and items in order to achieve personalized recommendations. By calculating similarity, items similar to the user's historical behavior or interests can be found, thereby providing a better recommendation experience.
- Image search: Images can be represented as high-dimensional vectors, and vector databases can be used to store and retrieve image data. Users can perform image searches by querying similar images, which is useful in areas such as e-commerce, social media, and image library management.
- Natural Language Processing (NLP): In NLP tasks, converting text into embedding vectors is a common approach. Vector databases can be used to store text embedding vectors for tasks such as semantic search, sentiment analysis, and text clustering.
- Speech recognition: Speech features can be represented as high-dimensional vectors, and vector databases can be used to store and retrieve audio data. This is important for applications such as speech recognition, speaker identification, and audio retrieval.
- 3D model and point cloud processing: In computer graphics and computer vision, 3D model and point cloud data are often represented as vectors or embedding vectors. Vector databases can be used to store and retrieve this data, supporting applications such as virtual reality, augmented reality, and 3D modeling.
- Cybersecurity: Vector databases can be used to store network traffic data, malware signature vectors, and network behavior patterns. These databases can help detect anomalous network activity and network intrusions.
- Scientific research: In scientific research, researchers can use vector databases to store and analyze experimental data for data mining, pattern recognition, and comparison of experimental results.
- Internet of Things (IoT): IoT devices generate large amounts of data, including sensor data and device status information. Vector databases can be used to store and retrieve this data to support applications such as smart cities, smart homes, and industrial automation.
- Healthcare: In the medical field, vector databases can be used to store patient medical records, medical images, and genetic sequence data. This helps healthcare professionals with disease diagnosis, drug development and personalized treatment.
5 Common Vector Databases in 2023
This list is in no particular order.
1、Chroma
Chroma is an open source embedding database. Makes it easy to build LLM applications by providing LLM with pluggable knowledge, facts, and skills that can easily manage text documents, convert text to embeddings, and conduct similarity searches.
main feature:
- Feature-rich: querying, filtering, density estimation and many other functions
- LangChain (Python and javascript), LlamaIndex are supported
- The same API running in a Python notebook scales to a production cluster
2、Pinecone
Pinecone is a platform that can host vector databases. In other words, there is a commercial company behind it and a free use plan. Pinecone’s key features include:
- Supports fully managed services
- Highly scalable
- Real-time data ingestion
- Low latency search
- Integrate with LangChain
3、Weaviate
Weaviate is an open source vector database. It scales seamlessly to billions of data objects. Some of Weaviate’s key features are:
- Speed: Weaviate can quickly search for the nearest 10 neighbors from millions of objects in milliseconds.
- Flexibility: With Weaviate, you can vectorize your data when importing or uploading your own, taking advantage of modules integrated with platforms such as OpenAI, Cohere, HuggingFace, and more.
- Rapid deployment: From prototype to mass production, Weaviate emphasizes scalability, replication and security.
- Search Extensions: In addition to fast vector search, Weaviate offers recommendations, summarization, and neural search framework integration.
4、Faiss
Faiss is an open source library for fast searching for similarities and clustering of dense vectors. It contains algorithms capable of searching in vector sets of varying sizes, even those that exceed memory capacity. This Faiss also provides auxiliary code for evaluating and adjusting parameters.
Although it is primarily written in C++, it fully supports Python/NumPy integration. Some of its key algorithms are also available for GPU execution. The main development work of Faiss is undertaken by Meta's basic artificial intelligence research group.
5、Quadrant
Qdrant can be run as an API service and supports searching for the closest high-dimensional vector. With Qdrant, embeddings or neural network encoders can be turned into applications for tasks such as matching, search, recommendations, and more. Here are some of Qdrant’s key features:
- Universal API: Provides OpenAPI v3 specifications and ready-made clients in various languages.
- Speed and Accuracy: Use custom HNSW algorithm for fast and accurate searches.
- Advanced filtering methods: Allows filtering of results based on correlation vector payloads.
- Different data types: Supports string matching, number ranges, geolocation, and more.
- Scalability: Cloud-native design with horizontal scaling capabilities.
- Efficiency: Built-in Rust, optimizes resource usage through dynamic query planning.
Summarize
Continuous developments in the fields of artificial intelligence and machine learning highlight the indispensability of vector databases in today's data-centric world. With their unique ability to store, search, and analyze multidimensional data vectors, these databases play an important role in driving AI-driven applications, from recommendation systems to genomic analysis.
We introduced 5 commonly used vector databases such as Chroma, Pinecone, Weaviate, Faiss, and Qdrant, each of which offers unique features and innovations. As artificial intelligence continues to develop, vector databases will undoubtedly play an increasing role in shaping the future of data retrieval, processing, and analysis, promising more complex, efficient, and personalized solutions in various fields.
https://avoid.overfit.cn/post/289fdcb291024802858148fdc86e7363
Author: Moez Ali