MongoDB is an influential player in the realm of database technology. Its cloud platform, Atlas, has been gaining increased popularity among developers, especially since the company is committed to easing developer friction. Recently, MongoDB released Atlas Vector Search, which according to InfoWorld’s report, will be able to help with a new range of workloads
Atlas Vector Search is designed to make searching across multiple fields of your data faster, more efficient, and intuitive. The company’s press release also stated that this function can improve the accuracy and trust of large language models by incorporating only approved content.
Let’s dive deeper into how Atlas Vector Search functions and what implications it holds for day-to-day database operations.
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Understanding What Vectors Are
Before we can explain how vector search works, it’s important to understand what vectors are. In the context of databases and data, vectors are mathematical representations of documents, objects, or pieces of data. They would look something like this:
[Document 1] -> [0.2,0.5,0.7,0.9,0.1],
[Document 2] -> [0.8,0.6,0.3,0.1,0.9],
[Document 3] -> [0.4,0.7,0.5,0.9,0.8]
These vectors can be described in n-dimensional space, with the values representing the positions on each of the n-dimensions. For instance, for a ‘5-dimensional’ vector like [0.2,0.5,0.7,0.9,0.1], each number indicates the position of the object in that specific dimension.
The idea is that similar data points should have similar vector representations, enabling them to be found more accurately during a search process.
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The Power Behind Vector Search
Now that we understand the basics of vectors, let’s get down to how vector search generally works.
The technique behind vector search is that similar vectors cluster together in the vector space. During a search, the model identifies the object closest to the query vector. The distance between vectors helps determine their similarity. The shorter the distance, the more identical they are. This opens up possibilities like searching for similar images, finding similar documents, and even making advanced recommendations.
For example, imagine we have a movie database and our vectors are based on the movie’s genre, length, release date, and other properties. If we search for movies similar to a specific movie, it will translate this movie into its corresponding vector and find vectors that are closest to it, hence, returning similar movies.
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How Atlas Vector Search Stands Out
‘What is Artificial Intelligence?’ by MongoDB outlines how Atlas Vector Search takes this core capability further. Atlas Vector Search’s distinctive benefits are that it allows higher dimensions and applications can write specific vector encodings straight to the Atlas database. This storage of vector-embedded data using schemas and the open-standard BSON (Binary JSON) format empowers developers to streamline their workflow as they no longer need to manage vector encoding separately.
For an efficient and accurate search, Atlas Vector Search uses an indexing technique introduced by Yu. A. Malkov and D.A. Yashunin called Hierarchical Navigable Small World (HNSW) graphs. HNSW graphs have proven to be a highly efficient method for nearest neighbor search in high-dimensional spaces. They function by creating layers of graphs in which every node points to its nearest neighbor. Nodes on higher layers have fewer connections allowing a faster navigation through the graph.
In the context of Atlas Vector Search, this technique significantly reduces the amount of computation and the time required to find the analogous vectors and return the most relevant results in the search.
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How Atlas Vector Search Can Be Used
Atlas Vector Search finds applications in a variety of scenarios including text search, image search, and personalized recommendations.
In text search, for example, documents are converted into vectors using techniques like Word2Vec or BERT. When a user searches with a phrase, it is converted into a vector and the system then finds the documents whose vector representations are most similar to the query vector.
Similarly, in image search, the color, shape, or other features of an image are encoded into vectors. When an image is used as a query, the system would return images with similar vector encodings, thereby finding images that are visually similar.
Furthermore, in personalized recommendations, vectors can be used to represent user behavior or preferences. When a system needs to make recommendations, it finds vectors that are similar to the user vectors and suggests items that those similar users have interacted with or showed interest in.
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Conclusion
Atlas Vector Search offers a highly efficient method for searching in large, high-dimensional datasets. It employs sophisticated techniques like HNSW graphs and permits specialized vector encoding, resulting in faster, more accurate searches for developers and end users.
We’re already seeing how AI is transforming online Google searches. So as more organizations turn to data-driven solutions, Atlas Vector Search illustrates the kind of innovative, intuitive technologies they should be looking for. It not only has potential to enhance personalized user experiences but also opens the door for more complex applications of vector search in the future.
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