Generative AI is at the top of every single sector today. It possesses the extraordinary capabilities to respond to the user’s query instantly. However, AI systems are dependent on huge amounts of unstructured data, such as images, texts, and videos, that machines need to understand. By learning from vast amounts of data, it can generate human-like output.
But the strong pillar behind the capabilities of generative AI is a vector database. They play a significant role in storing and retrieving similar information or a vector representation of data to deploy generative AI models. This blog walks through the context of vector databases for AI applications.
Understanding Vector Database
A vector database stores, manages, and retrieves information as numerical representations or arrays of numbers called vectors. Rather than storing simple text, it converts the content into arrays of numbers. Let’s take an example: In the traditional database, the words " doctor and physician are in different text representations. The system understands only perfect matches. A vector database records the words as the same numerical patterns because they have the same meaning.
Such numerical patterns, known as vectors, are high-dimensional (mainly 384, 768, or 1536 dimensions) with every dimension showing a distinct meaning. These vectors are organized in the database in such a way that they can be measured with respect to their similarity.
How Does Vector Database Work?
Here's how vector databases work in simple steps:
1] Vector databases first convert data such as text, images, videos, and more into high-dimensional vectors using AI models
2] When a query is searched, it is also turned into a vector.
3] These vectors are indexed so similar meanings are grouped.
4] When a query comes, the database compares vector distances and comes up with semantically similar results, not exact keyword matches.
5] They are designed to scale across distributed systems and can handle massive volumes of data and high query rates.
Reasons Why Vector Databases are Important for AI
AI applications mainly rely on situational awareness, trends, and working with large amounts of data of changing information. These capabilities are enabled by vector databases that help systems operate based on meaning rather than keywords.
1] Search accuracy: In semantic search, the relevance of a query is brought to the front because of user intent, despite the exact terms.
2] Adaptive learning systems: Vector databases help AI models adapt by updating embeddings continuously as user behavior, data, or environments evolve.
3] RAG pipelines with context-awareness: It guarantees the most relevant and accurate context to LLM models, leading to better responses.
4] Innovative suggestions: Vector search enables relevant, quick, and scalable suggestions for content personalization.
Real-World Examples
Here are the examples of organizations that leverage vector databases to power AI apps.
Shopify
The eCommerce platform uses vector-based search to suggest similar products based on product semantics, product attributes , and user search, browsing, and purchase behavior.
Anthropic
Claude stores conversations, documents, and user interactions in a vector store. Alongside, uses this indexing method to provide context-relevant, more useful responses.
Netflix
Uses the embeddings of the vectors that users have watched to recommend their content by examining watching habits, preferences, and similarity of content, not just to the same genre.
Other examples:
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- AI chatbots and virtual assistants
- Recommendation and personalization engines
- Fraud detection and anomaly detection
- Image and video similarity search
Wrapping it Up!
Vector databases stand as a backbone of modern AI systems/ They allow machines to know data by the meaning and not the precise matches of the data, thus opening faster searches, deeper contexts, and smarter decision making. Alongside, it serves as a glue between unstructured data and actionable intelligence, whether it is to power semantic search and RAG pipelines, or to run recommendations and anomaly detection.
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FAQs
1. Which dBs is best for AI?
Answer: Following dBs are best for AI:
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- Vector database
- NoSQL database
- Relational database
2. What are vector databases helpful for AI?
Answer: They are designed to cluster similar items that allow powerful capabilities like semantic search.
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