Understanding AI Similarity Search

🌈 Abstract

The article provides an overview of similarity search, also known as semantic search, which is a key discovery in the latest phase of AI. It explains the fundamentals of semantic search, including the mathematics behind it, and how it can be used to deliver highly useful tools to users.

🙋 Q&A

[01] Understanding AI Similarity Search

1. What is similarity search, and how does it differ from keyword search? Similarity search is an approach that compares information based on its meaning rather than keywords. It allows users to search for information using natural language queries, rather than just keywords. This enables the AI to respond with relevant information even if the exact keywords are not present in the query.

2. How does semantic search work? Semantic search converts entire documents and pages of text into a representation of their meaning, using vector embeddings. These vector embeddings capture the semantic relationships between words and concepts, allowing the AI to understand the meaning behind the query and find the most relevant information.

3. What are the key applications of similarity/semantic search? Similarity search can be used in various applications, such as:

• Helping researchers find specific information in large document collections
• Providing developers with precise information when querying API documentation
• Powering recommendation engines to suggest content similar to what the user is interested in

[02] Vectors in Artificial Intelligence

1. How are vectors used in AI? In AI, vectors are used to represent different types of data, such as features of a house in a machine learning model. The same mathematical principles used for vectors in physics can be applied to these data vectors in AI.

2. How are text and documents converted into vectors? Specialized AI models can convert words, phrases, and even entire documents into high-dimensional vector representations that capture the semantic meaning and relationships between the text. This process is known as vector embedding.

3. How can vector embeddings be used for similarity search? Vector embeddings allow the AI to determine the semantic similarity between different pieces of text, such as a user's query and the content of documents. This enables the AI to retrieve the most relevant information, even if the exact keywords are not present.

[03] Storing and Querying Vector Embeddings

1. How are vector embeddings stored and managed? Vector embeddings can be stored in specialized vector databases, which are optimized for efficiently computing the similarity between high-dimensional vectors. These databases provide mechanisms for performing similarity searches on the stored vector data.

2. What are the steps involved in performing similarity search? The key steps in performing similarity search are:

1. Converting source documents into vector embeddings and storing them in the vector database.
2. Converting the user's query into a vector and using the vector database's similarity search functions to find the most relevant documents.
3. Sending the query and the retrieved documents to a large language model (LLM) to verify the relevance of the results.

3. What are the benefits of knowledge retrieval and how can it be implemented as a service? Knowledge retrieval, or Retrieval Augmented Generation (RAG), is a powerful way to improve users' ability to access and get answers to their exact questions. It can be used to find and return entire articles, specific information within documents, or provide recommendations. Many platforms are beginning to offer knowledge retrieval as a service, which simplifies the development process.