Semantic Search and Vector Databases
Semantic Search #
Semantic search is a powerful technique to improve the user experience and the quality of search results by understanding the meaning and intent behind queries. It relies on vector representations of data, which can be stored and queried using vector databases. Unlike traditional keyword-based search engines that focus on finding documents containing specific words, phrases, or sequences, semantic search aims to comprehend the underlying meaning behind those words and their connections. As a result, this type of search engine delivers more targeted, intuitive, and intelligent outcomes.
While still relatively young compared to conventional keyword-based search engines, semantic search has shown great potential across diverse sectors including healthcare, education, research, and business analytics. Its capacity to derive insights from unstructured data makes it increasingly essential for organizations handling large volumes of digital assets.
One of the key components of semantic search is the use of vector representations of words and documents. #
Vectors are numerical arrays that capture the semantic features of words and documents, such as their topics, sentiments, or relationships. Vectors can be generated using various techniques, such as word embeddings, document embeddings, or neural networks. Vectors can also be compared using similarity measures, such as cosine similarity, to find the most similar words or documents to a given query.
Imagine you are looking for a product online, but you don’t know the exact name or keyword to search for. You only have a vague idea of what you want, or maybe you have an image of something similar. How can you find the most relevant results without wasting time and effort?
Vectors can be compared using similarity measures to find the most similar data to your query. For example, if you are looking for a product, semantic search can compare the vector of your query with the vectors of the products in the database, and rank them according to their similarity.
Vector Databases #
At the heart of semantic search are vector databases, designed to store, process, and retrieve data represented as vectors - numerical arrays encoding the meaning of various data types like text, images, or sounds. These vectors are positioned in a high-dimensional space, where their proximity and orientation to one another represent the semantic relationships between data points.
Vector databases enable fast and scalable semantic search by using efficient indexing and retrieval algorithms, such as approximate nearest neighbor (ANN) search. It can handle large-scale and high-dimensional data, text, images, audio, or video, and support various types of queries, such as similarity search, ranking, clustering, or top-k.
As we continue to generate more complex data, the role of these technologies will grow #
Tools and Examples of Vector Databases #
Elasticsearch with Vector Scoring: Elasticsearch, a popular open-source search engine, supports semantic search through its vector scoring feature. It allows storing vector data and performing similarity searches using cosine similarity or dot product calculations, enabling users to find closely related content based on context rather than mere keyword matching.
Faiss by Facebook AI: Developed by Facebook AI, Faiss is a library for efficient similarity search and clustering of dense vectors. It excels in handling very large datasets, which is typical in AI and machine learning applications.
Milvus: An open-source vector database, Milvus is optimized for handling large-scale similarity searches. It supports multiple similarity metrics and can handle mixed data types, blending traditional tabular data with vector data.
An interesting application - Retrieval-Augmented Generation (RAG) #
Retrieval-Augmented Generation is an approach in AI, particularly in natural language processing (NLP), where the generation of text is augmented by retrieving relevant information from a large vector database. This approach combines the strengths of both neural language models and traditional information retrieval systems to provide more accurate, relevant, and context-aware responses.
The applications of semantic search and vector databases are vast and diverse, we will discuss RAG in detail in the next article.
A very simple RAG implementation in python can be found here:
https://github.com/marciokugler/llms-demo-chat