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Table of Contents
Vector search: what is it?
Using machine learning (ML), vector search converts unstructured data—such as text and images—into a numerical representation by capturing its context and meaning. Vector search, which is commonly used for semantic search, uses approximate nearest neighbor (ANN) techniques to locate related data. Vector search operates more quickly and produces more relevant results than typical keyword search.
Use vector search to enhance your search experiences.
What makes vector search crucial?
How many times have you searched for something but couldn’t find the name? You might have a description or know what it does. But you’re left searching if you don’t have the keywords.
You can search by what you mean thanks to vector search, which gets around this restriction. Using a similarity search, it may provide answers to queries rapidly. This is due to the fact that vector embedding may capture unstructured data, including audio, video, and image, in addition to text. By using a hybrid search strategy and combining it with conventional scoring, you may maximize relevance and improve the search experience by combining vector search with filtering and aggregations.
What is the operation of a vector search engine?
The closest neighbors to a given (vectorized) query are found via vector search engines, sometimes referred to as vector databases, semantic search, or cosine search.
Vector search engines use distances in the embedding space to express similarity, whereas traditional search depends on keyword mentions, lexical similarity, and the frequency of word occurrences. Finding relevant information turns into looking for your query’s closest neighbors.
Embedding a vector
Vector embeddings are high dimensional (dense) vectors that contain numerical representations of data and associated context. For more accurate and pertinent findings, models that produce embeddings can be trained on millions of samples. In certain situations, you may be able to leverage numerical data that you have gathered or created to represent important aspects of your documents as embeddings. You just need to be able to search effectively.
Score for similarity
The fundamental tenet of a vector search engine is that similar documents and data will have similar vectors. You can identify similar documents as your query’s closest neighbors by using vector embeddings to index both queries and documents.
The ANN algorithm
Conventional nearest neighbor techniques, such as the k-nearest neighbor algorithm (kNN), consume a lot of computer resources and have lengthy execution durations. In order to operate effectively at scale in high dimensional embedding spaces, ANN forgoes perfect precision.
Use cases for vector search
In addition to powering the upcoming generation of search experiences, vector search creates a plethora of new opportunities.
ML-powered power search
Semantic search
Semantic or similarity search is powered by vector search. Vector search determines what users intend without having a precise keyword match since the meaning and context are recorded in the embedding. It functions with music, graphics, and textual data (documents). Finding products that are linked to or similar to their quest is simple and quick.
Suggestions
Similar documents and their vectors in the embedding space are recognized by the model that creates the embedding. For instance, an app might suggest films or goods that people who bought the same thing as you did also enjoyed. However, ensure that the embeddings are obtained using likability or popularity as the target metric.
Recommendations can accomplish several objectives by combining vector distances with other measures. Sort product recommendations, for instance, according to revenue potential and satisfaction ratings.
Answering questions
Full text responses to queries can be obtained by combining contemporary natural language processing (NLP) with the conversion of documents to text embeddings. This method allows your staff to respond more rapidly and saves users from having to read through long instructions.
The closest match can be provided as a “answer” by a “question answering” transformer model using the text embedding representation for both your current query and the knowledge base of documents.
How to use Elastic for question answering
- You may use vector search for more.
- Don’t limit yourself to semantic search alone!
- Examine unstructured data
- Look through any unorganized information. Text, image, audio, and sensor measurements can all be included.
How to use a similarity search for images
- Apply a metadata filter.
- Use metadata to filter vector search results. Applying a filter in accordance with approximate nearest neighbor (ANN) search preserves recall without compromising speed.
- Discover how to filter queries.
Discover how to filter queries
- Reorder the search results
- Similarity ratings derived from vector similarities can be re-ranked using additional data. This includes both new characteristics created by using machine learning models and static fields that are already present in your vector search database.
Discover how to rank in Elastic
- Scores that are hybrid
- Use hybrid scoring, which combines vector similarities with BM25F scores, to further optimize. When using BM25F, this enables you to rank photos based on vector similarity, which helps improve text rankings.
- Utilize Elastic’s hybrid scoring.
How to begin
Elastic makes vector search and NLP simple.
To use NLP models and implement vector search, you don’t need to move mountains. A framework for creating AI search apps that work with large language models (LLMs) and generative AI is provided by the Elasticsearch Relevance EngineTM (ESRE).
Building creative search applications, creating embeddings, storing and searching vectors, and implementing semantic search with Elastic’s Learned Sparse Encoder are all possible with ESRE. Try this self-paced, hands-on vector search learning course or read more about using Elasticsearch as your vector database.
Big language models
Using your own data (not just publicly trained data), provide LLMs with business-specific knowledge. Utilize Elasticsearch to gain access to Generative AI using plugins and APIs that are linked with your preferred LLM.
Embedding text and more
Discover how to use Elastic to give your data feelings and other classifications. With more metadata, use named entity recognition (NER) to enhance search results.
In the Elastic Stack, contemporary NLP