There’s too much data. Data lakes and data warehouses; vast pastures of pixels and oceans teeming with text. Finding the right data is like searching for a needle in a haystack. Enter vector search, a new data paradigm that powers many advanced AI applications. Vector search engines such as
Qdrant,
Pinecone,
LanceDB, and
Milvus are essential tools in modern MLOps pipelines because they enable semantic understanding at scale.
However, if you’re working with image or video data and you want to incorporate vector search into your workflows, there can be quite a bit of overhead:
- How do you implement cross-modal retrieval like searching for images with text?
- How do you incorporate traditional search filters like confidence thresholds or class labels?
- What about searching over the objects (people, cats, dogs, cars, bikes, …) within your images?
These are just a few of the many challenges you will encounter.
Wait. Stop. Hold your horses. There’s a better way…
FiftyOne is
the computer vision interface for vector search. The FiftyOne toolkit now features native integrations with
Qdrant,
Pinecone,
LanceDB, and
Milvus so you can use your preferred vector search engine to efficiently search your visual data in a single line of code. Whether you’re building intelligent recommendation systems, semantic search tools, or image retrieval engines, understanding vector-based search is essential.
What is vector search?
Let’s start at the beginning, vector search transforms complex, unstructured data, like images, text, or audio, into numerical representations called embedding vectors. Each vector captures the semantic meaning of the data rather than its exact words or pixel values. This allows the system to find items that are similar in meaning, not just identical in wording. For instance, a vector search for “dog” might also retrieve “puppy” or “wolf,” because their vectors exist close together in a high-dimensional space.
How does vector search work?
- Data embedding: Raw data (like an image or sentence) is fed into an embedding model (such as CLIP or BERT). The model outputs a numerical vector that captures semantic meaning.
- Indexing the vectors: These vectors are stored and indexed in a vector search database—a specialized database designed for efficient vector similarity search. This allows rapid retrieval even from billions of data points.
- Image similarity search: When a query is made, it’s also converted into a vector. The system then compares this query vector with the indexed vectors using mathematical distance metrics (like cosine similarity or dot product) to find the closest matches.
In the FiftyOne toolkit, this process is as simple as calling compute_similarity() to create an index and then using sort_by_similarity() to search across your entire vector search database, even with billions of images.
Vector search database
A vector search database is a specialized system built to store and query vectors. Unlike traditional databases, it organizes data by proximity in vector space rather than exact matches.
When integrated with platforms like FiftyOne, these databases make vector-based search as simple as running traditional filters or queries — allowing you to combine vector queries (like “find similar images”) with metadata filters (like “only include images labeled ‘traffic’”).
Vector search example
1. Load your dataset.
For the purposes of illustration, we’ll load a subset of the MS COCO validation split.
2. Generate the similarity index.
In order to search against our media, we need to index the data. In FiftyOne, we can do this via the compute_similarity() function. Specify the model you want to use to generate the embedding vectors, and what vector search engine you want to use on the backend. You can also give the similarity index a name, which is useful if you want to run vector searches against multiple indexes.
3. Search against the index.
Now you can run image searches across your entire dataset with a single line of code using the sort_by_similarity() method. To find the 25 most similar images to the second image in our dataset, we can pass in the ID of the sample, the number of results we want returned, and the name of the index we want to search against:
Vector similarity search explained
Vector similarity search finds items that are “close” to each other in vector space. For example, in an image dataset, two photos of cats will have highly similar vector embeddings, even if the cats are different breeds or colors. You can find similar images to any reference image, or even search for images semantically using a natural language prompt like “kites flying in the sky.”
This is semantic search powered by vector search, enabling natural, human-like understanding of content.
Semantic search vs vector search
Understanding semantic search
Semantic search focuses on understanding the intent behind a user’s query. It moves beyond keywords to interpret meaning, context, and relationships between concepts.
For example, if you search for “fastest land animal,” a semantic search engine knows you’re looking for “cheetah,” even though you didn’t mention it specifically.
Key differences between semantic search and vector search
The key differences between semantic search and vector search lie in how they interpret and retrieve information. Semantic search focuses on understanding the user’s intent through natural language processing (NLP) and contextual analysis, often leveraging language models or knowledge graphs to interpret meaning. It’s primarily used for text-based data, making it ideal for applications like web search, chatbots, and question-answering systems. In contrast, vector search represents meaning through mathematical embeddings, allowing it to compare data points based on their semantic similarity rather than exact matches. This approach works across multiple data types—including text, images, video, and audio—and powers use cases like image retrieval, recommendation engines, and AI assistants.
While different, these technologies often work together. In practice, semantic search vs vector search is less of a competition and more of a collaboration — semantic understanding is often powered by vector embeddings.
Semantic Search Made Simple
Gone is the hassle of handling multimodal data. If you want to semantically search your images using natural language, you can use the exact same syntax! Use a multimodal model like CLIP to create your index embeddings, and then pass in a text query instead of a sample ID:
This can be especially useful in unstructured data exploration, and digging deeper into your data than existing labels would otherwise allow.
This, as well, can be executed entirely in the FiftyOne App.
The future of vector-based search
Vector search is reshaping how we find and understand data. From content recommendation to AI assistants, it allows systems to think semantically, not syntactically.
Expect to see:
- Wider adoption in enterprise AI and data analytics
- Improved multimodal search (text ↔ image ↔ video)
- Integration into everyday applications like e-commerce, media libraries, and data lakes
No matter how much data you manage, millions or billions of records, vector search is the key to making it truly searchable.
By combining the power of semantic understanding and vector similarity search, tools like
FiftyOne simplify the process, allowing you to find and explore data with a single line of code.
The future of search is vectorized, intelligent, and semantic — and it’s already here.
