Understanding Vector Databases: The Future of Handling Unstructured Data

Over 80% of the data is unstructured such as social media posts, Audio, videos, and Images. We cannot fit them easily on any Relational Database. Let’s take an example of a cat Image if we want to put this image into a relational database to search for similar images then what ends up happening is we manually assign some keywords or tags to it, because from the pixel value alone we cannot search for similar images and same holds for unstructured text, video and Audio Data. So to overcome this thing we find a different representation to store the data and this brings us to Vector embeddings and Vector Databases.

1. What are Vector Databases

A Vector Databases indexes and stores Vector embeddings for fast retrieval and similarity search. A vector embedding is just a list of numbers that represents the data differently. One easy possibility we get with vectors is to find a similar vector by calculating the distance and doing a nearest neighbor search so we can easily find similar items. Vectors also need to be indexed, because the indexing process is the second key element of a vector database. Indexing helps in Mapping the vectors to a new data structure that will enable faster search. Java-based vector databases such as Elasticsearch and Vespa can easily integrate with Java applications development services to perform efficient vector operations. These Databases are ideal for use cases like Recommendation systems, Semantic search, and real-time analytics.

1.1 Use Cases

• We can use vector databases to equip Large Language Models (LLMs) with long term memory.
• We can use it for semantic search
• We can also use it for similarity search for images, audio, or video data without the use of any keywords or text to describe the image.
• We can also use a Vector Database as a ranking and recommendation engine. For online e-commerce platforms, it can be used to suggest items which quite similar to the last purchase of the customer.

2. Popular Vector Databases

2.1 Milvus Vector Database

It is an open-source Vector Database that is designed and optimized for managing, indexing, and retrieving large-scale and high-dimension vectors. The main aim to design Milvus vector Database is for Similarity Search, which means finding the same vectors that are closest to a given query vector. This is crucial in applications like recommendation systems, Natural Language Processing(NLP), etc. where data is represented in vector form.

2.1.1 Key Features of Milvus

• Mainly it is designed to handle large-scale vector data, which offers low latency and high performance.
• It supports a variety of algorithms for indexing such as HNSW, IVF, ANNOY which enables fast and accurate similarity search on large datasets.
• It is more Flexible and compatible with different Data Types which include dense vectors, sparse vectors, and binary vectors.
• Milvus Vector Database also works well across various environments which include local setups and hybrid cloud environments.
• With configurable replication and backup strategies it also ensures data durability using persistent storage.
• For security purpose it supports RBAC (role-based access control) and various authentication mechanism.
• To protect sensitive data it provides Data encryption in transit and at rest.

2.1.2 Use Cases

• Recommendation System: By matching user preference with the item or content searched by the user as it is powered by real-time recommendation engines.
• Natural Language Processing: To perform tasks like semantic search or document clustering it can manage large-scale text embeddings.
• Biometrics: It is suitable for Facial recognition and other Biometrics verification systems.

2.2. Pinecone Vector Database

Pinecone is a specialized database designed to handle large amounts of complex data, especially those used in AI and machine learning. It enables storing, indexing, and querying high-dimensional vectors to find similar items or to perform some complex searches. It integrates with various popular ML frameworks and tools. It reduces the complexity of infrastructure management. Pinecone is a powerful tool for managing and searching complex data efficiently, making it essential for modern AI and machine learning applications.

2.2.1 Key Features of Milvus

• Hybrid Search: It supports hybrid search. It combines both vector-based similarity search and traditional keyword-based search which makes it ideal for applications that require both text relevance and semantic understanding.
• Managed Service: Pinecone is a fully managed cloud service which means we don’t have to worry about the infrastructure, scaling, and maintenance. All the backend operations were focused on by the developers while building applications.
• Integration with Machine learning tools: It integrates with various AI/ML frameworks. It contains API’s and SDKs for various programming languages which makes it easily incorporated into any ML application or pipeline.
• Fast Performance: Optimised for quick responses, which is crucial for real-time applications like chatbots and recommendation systems.

2.2.2 Use Cases

• Recommendation Systems: Suggests products or content based on user preferences.
• Text Search: Finds similar documents or text entries.
• Image and Video Search: Identifies and categorizes media based on content.
• Security: It includes features like encryption at rest and in transit, and role-based access control (RBAC).

2.3. Vespa Vector Database

Vespa is an open-source engine created by Yahoo, designed to process and serve large amounts of data quickly. It’s commonly used for search engines and recommendation systems that need to deliver results in real time. It offers both full-text search and vector search capabilities, which makes it suitable for AI-driven use cases that require fast retrieval and processing of high-dimensional data.

2.2.1 Key Features of Vespa

• Real-Time Search: Delivers fast search results and recommendations, even with large datasets.
• Scalability: Can grow to handle very large data and high traffic, perfect for big enterprises.
• Flexible Data Handling: Works with different types of data, making it versatile for various applications.
• Content Serving: Not just a search engine, Vespa also serves content and performs calculations on the fly.
• Open Source: Free to use and customize, allowing developers to adapt it to their needs.

2.1.2 Use Cases

• Recommendation System: By matching user preference with the item or content searched by the user as it is powered by real-time recommendation engines.
• Natural Language Processing: To perform tasks like semantic search or document clustering it can manage large-scale text embeddings.
• Biometrics: It is suitable for Facial recognition and other Biometrics verification systems.

Conclusion

For Modern AI and Machine Learning applications, Vector Databases are becoming a very essential tool. For high-dimensional vector data, offers specialized storage and efficient retrieval capabilities, which enables real-time recommendations, similarity searches, semantic search, and personalization. There are various types of Vector databases present, some of which are discussed above from fully managed services like Pinecone to open-source like Milvus and Vespa. Many organizations select the best option based on their performance, Scalability, and Integration needs. We know in the upcoming time the enhancement in technology will be at its best so vector databases will continue to play a crucial role in providing new insights and delivering enhanced user experience.