Exploring Semantic Search

Semantic search is a cutting-edge search technology that has revolutionized the way people find information online. In the world of enterprise, it has become a vital tool for businesses that need to search through vast amounts of data quickly and accurately. The semantic search engine uses NLP techniques to understand the meaning of the search query and the content being searched. This technology goes beyond traditional keyword-based search engines by using ML algorithms to understand the context of the search query, resulting in more accurate and relevant results.

Understanding semantic search with embedding

A key component of semantic search is the use of embedding, which is the process of representing words or phrases as numerical vectors. These vectors are generated by a neural network that analyzes the context of each word or phrase in a given text corpus. By converting words into vectors, it becomes easier to measure the semantic similarity between words and phrases, which is crucial for accurate search results.

Example

For example, in the realm of medical documentations and papers, embedding can be used to enhance semantic search algorithms by making it easier to identify related concepts and topics.

• For instance, if a researcher is searching for information about cancer treatment, a semantic search engine that uses embedding can identify related terms such as chemotherapy, radiation therapy, and immunotherapy.

• By understanding the semantic relationships between these terms, the search engine can return more accurate and relevant results.

Embedding can also be used to identify related topics and concepts that may not be immediately apparent from the search query.

• For instance, if a researcher is searching for information about breast cancer, a semantic search engine that uses embedding can identify related topics such as mammography screening, breast cancer prevention, and hormonal therapy.

• This broader understanding of the topics and concepts related to breast cancer can help researchers find relevant papers and documents that they may not have discovered through a traditional keyword search.

Utilizing AI for efficient medical research

For example, let’s consider the following scenario. We are a private clinic, and every day, we struggle to find information in the huge amount of available documentation. To produce a diagnosis, doctors need to go through many papers, and this is very time-consuming.

We are looking for an AI research assistant who can help us in the research process. For this purpose, we will use an Azure OpenAI deployment called embedding associated with the text-embedding- ada-002 model.

The idea is as follows:

1. Get the embedding of the available text with the embedding model.

2. Get the embedding of the user query with the embedding model.

3. Compute the distance between the embedded query and the embedded knowledge base.

4. Return the most similar pieces of text and use them as context to the GPT model.

5. Use the GPT model to generate a response.

This is represented here: