Vector search: Empowering devs to build GenAI apps

In the age of AI, Apache Cassandra® has emerged as a powerful and scalable distributed database solution. With its ability to handle massive amounts of data and provide high availability, Cassandra has become a go-to choice for many AI applications, including Uber, Netflix, and Priceline. However, with the introduction of generative AI and large language models (LLMs), new query capabilities are needed. 

Enter vector search, a revolutionary new feature that empowers Cassandra with enhanced search and retrieval functionalities for generative AI applications.

What is vector search?

Vector search is a cutting-edge approach to searching and retrieving data that leverages the power of vector similarity calculations. Unlike traditional keyword-based search, which matches documents based on the occurrence of specific terms, vector search focuses on the semantic meaning and similarity of data points. By representing data as vectors in a high-dimensional space, vector search enables more accurate and intuitive search results. 

For example, vector search easily identifies the semantics in these examples that term-based search would struggle with:

• False positive: “Man bites dog” and “dog bites man” include the same words but have opposite semantics.

• False negative: “Tourism numbers are collapsing” and “Travel industry fears Covid-19 crisis will cause more companies to enter bankruptcy” have very similar meanings but different word choices and specificity.

• False negative: “I need a new phone” and “My old device is broken” have related meanings but no common words.

Why vector search matters

Integrating vector search with Cassandra isn’t just a technical upgrade—it’s a game-changer for developers building AI-driven applications. Cassandra has long been a powerhouse for handling massive datasets with speed and scalability, making it a favorite for companies like Uber, Netflix, and Priceline. 

But as generative AI and large language models (LLMs) reshape how we interact with data, traditional search methods fall short. Vector search steps in to bridge that gap, bringing semantic understanding and flexibility to Cassandra’s already impressive toolkit.

So, why should you care? For starters, vector search lets Cassandra tackle unstructured data like text, images, audio, and video with the same efficiency it applies to structured data. Before this, you were limited to querying floats, integers, or exact strings. Now, you can run similarity-based searches that dig into the meaning behind your data, not just the keywords. Think of it as upgrading from a basic flashlight to a high-powered spotlight—you see more, and you see it better.

The benefits don’t stop there. Vector search boosts accuracy by focusing on semantic relationships, uncovering patterns that keyword searches miss. It’s fast, too. Cassandra handles similarity calculations right in the database, cutting out the need to shuffle data to external systems. 

Plus, it scales effortlessly. As your data grows, Cassandra’s distributed architecture keeps vector search humming along, no sweat. Whether you’re building recommendation engines, fraud detection systems, or natural language processing tools, this integration opens doors to applications that weren’t possible before.

In short, vector search makes Cassandra more than just a reliable database—it turns it into a one-stop shop for high-scale, AI-ready solutions. That’s why it matters. It’s not about keeping up with the latest buzzwords. It’s about giving developers the power to build smarter, faster, and more relevant applications.

Integrating vector search with Cassandra

The integration of vector search with Cassandra (for details, see CEP-30) offers several benefits. It opens up exciting possibilities for applications that require similarity-based queries—and not just for text. Applications as diverse as recommendation systems, fraud detection, image recognition, and natural language processing can all benefit from vector search.

Here are some key advantages of incorporating vector search into Cassandra:

Unstructured data queries

Prior to vector search, Cassandra was limited to searching structured data (floats, integers, or full strings). Vector search now opens the possibilities to query unstructured data, including text, audio, pictures, and videos. This makes Cassandra the one-stop-shop for high-scale database applications. 

Enhanced search accuracy

Vector search allows for similarity-based queries, enabling more accurate and relevant search results. By considering the semantic meaning of data points, it can uncover hidden relationships and patterns that traditional keyword searches might miss.

Efficient query processing 

With vector search, Cassandra can perform similarity calculations and ranking directly within the database. This eliminates the need to transfer large amounts of data to external systems, reducing latency and improving overall query performance. Furthermore, you can combine vector search with other Cassandra indexes for even more powerful queries to find exactly the data you need.

Scalability and distributed processing

Cassandra's distributed architecture aligns perfectly with vector search requirements. As data volumes grow, vector search can leverage Cassandra's scalability and distributed processing capabilities to handle large-scale similarity queries efficiently.

Broad applicability

Vector search provides the flexibility to compute similarity across various types of data, including text, numerical values, images, and embeddings. This versatility enables developers to build advanced applications that span multiple domains and data types, all within the Cassandra ecosystem.

Vector search use cases

It seems that not a day goes by when a new, innovative application of generative AI is invented. Almost all generative AI use cases are enhanced by vector search because it allows developers to create more relevant prompts. Use cases of vector search for generative AI include:

Question answering 

Converting documents to text embeddings can be combined with modern natural language processing (NLP) to deliver full text answers to questions. This approach spares users from studying lengthy manuals and empowers your teams to provide answers more quickly. A "question answering" generative AI model can take the text-embedding representation for both the knowledge base of documents and your current question to deliver the closest match as an "answer." 

Semantic search

Vector search powers semantic or similarity search. Because the meaning and context is captured in the embedding, vector search finds what users mean, without requiring an exact keyword match. It works with textual data (documents), images, and audio, to, for example, easily and quickly help users find products that are similar or related to their query.

Semantic caching 

As your generative application grows in popularity and encounters higher traffic levels, the expenses related to LLM API calls can become substantial. Additionally, LLM services might exhibit slow response times, especially when dealing with a significant number of requests. Caching LLM responses can significantly increase response times, and lower the cost of using generative AI. However, to match the input of an LLM to previous input requires performing a semantic match rather than an exact match. Vector search provides users with that ability. 

Transformers and vector search

Transformers power generative AI by understanding language context through tokens, using self-attention to generate coherent responses. But they hit a wall with token limits—hardware memory caps them at a few thousand tokens per pass. 

Vector search fixes this by pulling only the most relevant data, maximizing the token window’s value. For a Q&A chatbot, it grabs precise answers instead of dumping an entire repository. For chat history, it keeps the right context without overloading the limit. Plus, it cuts costs by optimizing prompts and caching responses. That’s how it boosts efficiency in AI apps.

 

Get started!

Cassandra and Astra DB developers can take a great leap forward with vector search. Today, Cassandra is the number one database for querying both structured and unstructured data. 

Understanding the strengths and limitations of transformers and using advanced data management technologies like DataStax can greatly enhance the effectiveness and cost-efficiency of generative AI applications. 

Sign up now to get started, or watch our webinar on vector search to learn more!

 

FAQs: Cassandra vector search

1. What is Cassandra vector search, and how does it work?

Cassandra vector search enables similarity-based querying by storing and retrieving high-dimensional vector embeddings. It works by comparing query vectors to stored vector data using distance metrics like cosine similarity or Euclidean distance, making it useful for AI-powered applications like semantic search, fraud detection, and recommendation engines.

2. What is a vector column, and how do I store vector data in Cassandra?

A vector column is a special data type in Cassandra that stores numerical arrays representing data embeddings. To store vector data, you define a vector data type column in your table schema and use storage-attached indexing (SAI) for fast retrieval. This allows efficient searches across high-dimensional data such as text embeddings, image recognition models, and machine learning outputs.

3. How does vector search help with machine learning and AI applications?

Vector search is essential for AI tasks because it allows for semantic meaning retrieval rather than simple keyword matching. It is widely used in retrieval-augmented generation (RAG), recommendation systems, anomaly detection, and large language models (LLMs). By storing vector embeddings from a machine learning model, Cassandra enables efficient approximate nearest neighbor (ANN) search for finding similar data points.

4. How can I optimize vector search performance in Cassandra?

To improve vector search speed, ensure that you:

• Use storage-attached indexing (SAI) to efficiently index and retrieve vector data.

• Optimize vector index parameters for better search performance.

• Leverage globally distributed indexing to speed up searches across large datasets.

• Reduce dimensionality of high-dimensional data when possible to improve query speed.

5. How do I create a table for vector search in Cassandra?

To enable vector search in Cassandra, you need to define a table with a vector column that stores numerical embeddings. This column allows for similarity-based searches using distance metrics like cosine similarity or Euclidean distance. After defining the table, you should apply storage-attached indexing (SAI) to optimize retrieval speed. Proper indexing ensures that vector search queries run efficiently, even on large datasets used in AI, recommendation engines, and anomaly detection.