What is vector search?
Vector search is a method in artificial intelligence and data retrieval that uses mathematical vectors to represent and efficiently search through complex, unstructured data.
The tech industry is buzzing right now with all the opportunities for change that predictive AI and generative AI bring to how we interact with information. At the center of this transformation is vector search, also known as nearest neighbor search, which enables AI models to efficiently retrieve the most relevant information from vast datasets based on a given query.
Unlike traditional searching models like keyword search that look to find exact matches of information, vector search represents data points as vectors, which have direction and magnitude, in a high-dimensional space. With vector search, the individual dimensions define a specific attribute or feature. The search compares the similarity of the query vector to the possible vector paths that traverse all of the dimensions. Implementing a vector search engine enables more sophisticated and accurate searches through large and complex datasets.
Vector search works by associating similar vector representations and converting queries into the same vector representation. With query and data represented as vectors, finding related data becomes a function of searching for the closest data representations to your query representation, known as nearest neighbors. Unlike traditional search algorithms that use keywords, word frequency, or word similarity, vector search uses the distance representation embedded into the vectorization of the dataset to find similarity and semantic relationships.
Why is vector search important?
Vector search is the latest evolution in how information is categorized and accessed. Like many transformative changes, vector search brings a new way to unlock the full potential of the data we collect.
By organizing data into high-dimensional vector spaces, vector search captures the semantic value of that data, allowing generative AI solutions to extract the contextual relevance and create new relevant content based on that context. You can apply Vector search's contextual relevance to a variety of use cases:
- Similarity retrieval: Provides applications with the ability to have a thesaurus – not just for words – for the entirety of the users' data set. Similarity retrieval enables adaptation to contextual input more directly, allowing users to find variations that suit their requirements quickly.
- Content filtering and recommendation: Vector search provides a more fine-tuned approach to filtering content. Moving beyond the limited scope of keyword association to an approach that considers hundreds or thousands of contextual data points and helps identify additional content with similar attributes.
- Interactive user experience: With vector search, users can more directly interact with large data sets to hone in on relevant information more quickly. Instead of searching product documentation using a specific keyword, users can now interact with the documentation using natural language processing, getting more relevant results for their queries and additional information around those queries they may not know to ask.
- Retrieval-augmented generation: One of the most significant benefits of generative AI is its ability to bridge the gap between predicting and responding to outcomes. Vector search is the foundation for retrieval-augmented generation (RAG) architectures because it can extract semantic value from datasets and continuously enrich them with new context, improving the relevance of AI-generated outputs.
How does vector search work?
Nearest neighbor search is at the core of vector search, with different algorithms available depending on how much compute you want to allocate and how accurate you want your result to be.
K-nearest neighbor (kNN) algorithms provide the most accurate results but also require the highest amount of computational resources and execution time. For most use cases, Approximate Nearest Neighbor (ANN) is ideal, as it provides significantly better execution efficiency in high-dimensional spaces at the cost of perfect accuracy of the results.
This trade-off is especially valuable for large language models and other applications that rely on massive datasets. With larger datasets, the reduction in accuracy becomes less significant because more data yields better results, especially if you introduce algorithms like hierarchical navigable small worlds (HNSW).
What are vector embeddings?
Vector search calculates and uses nearest neighbor algorithms by transforming all data into vector embeddings. In its most basic form, a vector embedding is a mathematical representation of an object as a list of numbers. Once in this numerical representation, the semantic similarity of objects now becomes a function of proximity in a vector space. This numerical translation is known as a vector representation, which defines how objects are positioned and compared within the multidimensional vector space. This representation enables the precise calculation of similarities and differences between various data points, making it the foundation of vector search.
Once represented as a list of numbers, real-world objects can occupy plots on a multidimensional graph. Depending on how close one object is to another, a user can determine how similar a given object is to another.
Once users store objects as vectors, they can store those vectors in a vector database, which is purpose-built to provide efficient storage and retrieval of large datasets of vector embeddings. This ability means that vector search operations are usable at scale.
The difference between traditional vs vector search
Unlike traditional search that compares keywords or indexes, vector search provides the ability to compare the entirety of the query against the entirety of the data searched and represent how closely each result is to the given query with context. This distinction highlights the superiority of how a vector search engine works in managing complex search queries, transcending the limitations of keyword-based traditional search systems. But what does this mean, and what is its impact?
Traditional keyword search thrives when exact results are requested. If you know the specific details of what you’re looking for, it’s easy to find it. Take, for example, all the information for a given account, such as name, phone number, or address. Traditional search works well if you have one of these pieces of information readily available.
But what if you knew of an upcoming service outage in a specific area and wanted to make a broader query, such as all accounts on a given street? That type of search is more difficult with a traditional search because there isn’t an exact match. This is where similarity search becomes crucial, as vector search excels in identifying and retrieving information that is not just identical but semantically similar to the query.
With vector search, this type of query shifts to searching based on the street and finding all the data representations nearest to that query's vector embedding, yielding highly accurate results quickly and efficiently. This ability to elastically compare and search large datasets with speed and accuracy is what makes vector search so powerful.
How to implement vector search
Implementing vector search involves four key steps: generating vector embeddings, storing embeddings in a vector database, selecting a similarity metric, and performing nearest neighbor search to find the best results. Here’s a quick guide on how to get started implementing vector search :
1) Generating vector embeddings
Vector search starts by converting your data, such as text, images, or other media, into vector embeddings. Users create these embeddings using pre-trained machine learning models or transformers. The model you choose to generate embeddings should align with your data type and use case. For text, options include models from OpenAI, Hugging Face, or Sentence Transformers, while for images, models like CLIP or ResNet are a better option. Once generated, these vectors serve as the foundation for semantic search.
2) Store embeddings in a vector database
For vector search to perform efficiently at scale, users must store embeddings must be stored in a database optimized for high-dimensional indexing. One such option is Astra DB, a fully managed, cloud-native database built on Apache Cassandra that natively supports vector search.
With Astra DB, you can store your embeddings alongside any associated metadata within a single, scalable system. Its schema-flexible design allows you to combine both structured and vector data in the same database. Astra DB also manages indexing automatically, enabling you to run vector queries with minimal configuration.
3) Choose a similarity metric
After generating and storing your embeddings, the next step is to decide how to compare them. Similarity metrics quantify how close or relevant one vector is to another. The right metric depends on your use case and the characteristics of your data. Some common options include:
- Cosine similarity: Measures the angle between two vectors. It’s ideal for text embeddings, where direction captures semantic relationships.
- Euclidean distance: Calculates the straight-line distance between points. Euclidean distance makes it useful for spatial data or image embeddings.
- Dot product: Emphasizes magnitude and alignment. It’s often a component of recommendation engines where higher scores correlate with greater relevance.
Astra DB allows you to specify the similarity metric when configuring your vector search index so that you can tailor your search to the needs of your application.
4) Perform nearest neighbor search
The final step in implementing vector search is to retrieve the most relevant results using vector search itself. Instead of filtering by exact keywords or attributes, vector search finds the closest matches based on proximity in vector space. This process enables highly relevant data retrieval, even for large and complex datasets.
To perform this efficiently, Astra DB uses Approximate Nearest Neighbor (ANN) techniques, which help optimize search operations without compromising quality. ANN methods, such as Hierarchical Navigable Small World (HNSW) graphs, are particularly effective at narrowing down the search space, enabling fast lookups and quick response times, even with high-dimensional data. HNSW and similar techniques reduce the need for exhaustive search through every possible vector, delivering high-quality results faster and with less computational load.
By following these steps – generating vector embeddings, storing the embeddings, choosing a similarity metric, and performing a nearest neighbor search – you can implement a vector search solution that provides fast, accurate, and scalable information retrieval.
