Semantic search example. When a user asks a question, the GPT .
Semantic search example Nov 11, 2021 · A timely overview of the landscape and jargon of semantic search and question answering systems. Examples of this are TF-IDF and BM25. Dec 16, 2019 · Ever since Google’s Hummingbird, the term “semantic search” has been thrown around a lot. If your configuration uses a search service managed identity for indexer connections, your search service must be on the Basic tier or higher. This tutorial covers the following tasks: May 12, 2025 · For example, the search engine Baidu uses two types of indexes (a lexical one and a semantic one) from which it pulls search results. Semantic Caching example using LangChain LLM Conversation Memory. Jul 23, 2025 · Semantic Search Architecture Key Concepts in Semantic Search: Natural Language Processing (NLP): Semantic search uses NLP techniques to analyze and understand the meanings behind words and their relationships. ” Learn more. Oct 26, 2025 · Semantic search is an advanced search technology that interprets the meaning behind words, rather than relying on exact keyword matches. May 18, 2025 · Visual Search Example Semantic Caching. While semantic search offers more flexibility and contextual awareness, it Semantic search is an approach to information retrieval that seeks to improve search accuracy by understanding the searcher's intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results. Semantic search is accomplished using AI models to transform the text into vectors, which are arrays of numbers, and are called "embeddings". Jun 27, 2025 · This hands-on tutorial shows you how to build a semantic search application by using Azure Database for PostgreSQL and Azure OpenAI. Learn what semantics has to do with search engines and why your website search function should be semantic. Master intent-based SEO strategies and boost rankings with this guide! May 21, 2025 · For example, semantic search interprets the underlying meaning of your query, ensuring that the results align closely with your expectations. This repository contains Python code samples used in Azure AI Search documentation. This posting will demonstrate the functionality in a short demo. Semantic search plays an important role in various businesses and technical use cases, including search engines and e-commerce platforms like Amazon. Jul 29, 2021 · Search engine technology has evolved, making semantic search essential for SEO. Combining keyword, vector, and semantic search would require a complex custom solution. Apr 24, 2025 · Text submitted is used for both semantic and keyword search, and the image is used for semantic search. The L2 ranker promotes more semantically relevant matches to the top. This section also introduces Learn about semantic search and MongoDB Atlas vector search utilizes this technology to help software developers and businesses. keyword search, what's the g-o? Our guide explains the differences, benefits, and SEO strategies for both approaches. Read on as we fact check if one is better than the other, which you need to build your own chatbot and more. You can find these credentials in the Pinecone web console. Learn about all the ways it works and the tools that support it. Explore the impact of semantic search on user experience and website visibility. Semantic search uses advanced techniques to understand meaning and context, excelling at natural language queries and concept matching. You too can enrich your applications with Qdrant semantic search. At the end you should have a solid view on (1) what semantic search is, (2) when to use semantic search and (3) how to approach implementing a semantic search engine. This app is focused on semantic search over legal documents, but this exact same technique and code can be applied to any content stored locally. Jan 31, 2022 · Building semantic matching at scale Building a fast scalable semantic search system for millions, billions or more documents requires different designs and hardware. Jun 5, 2025 · The more our models can understand the semantics of the language, the more effectively our query will be successfully answered, with the model able to retrieve relevant text from the documents. It uses machine learning to capture the intent and context behind the query, handling language nuances like synonyms, phrasing variations, and word Mar 12, 2024 · However, it seems that the model in this scenario is a little picky to choose Messi as best player 😀. Oct 30, 2025 · Semantic Search helps search engines understand the meaning behind your query, delivering more relevant and personalized results for a better search experience. Semantic image search enables users to search a database of images quickly. The semantic query can be used as a part of a hybrid search where the semantic query is combined with lexical queries. NET Core This ASP. It uses machine learning models to generate more accurate and relevant results compared to Oct 31, 2025 · Semantic search improves data discovery by understanding query intent and context for more relevant results. Get my 7 best tips to future-proof your content. We have learned how to set up the necessary tools and libraries, and how to run some examples of Semantic Kernel prompts. 4 days ago · Learn how to change an existing index to use semantic ranker, which helps rescore search results and promote the most semantically relevant matches. semantic search. Semantic Search Example To understand how semantic search works, let’s use an example where a user wants to find the name of the movie featuring Riley’s emotions. Semantic search work is focused on understanding user queries in order to return more relevant and context-aware results. May 9, 2022 · The goal here is to get an intuition for what semantic search is and which challenges you will likely face when implementing a semantic search engine in a practical setting. The system would: Convert this query into a vector, say Q. Feb 24, 2025 · Learn about semantic search, how it works in 2025, and how search engines use intent, context, and entities to improve search results. Dec 11, 2024 · Explore semantic search examples and learn how to optimize your content for better SEO visibility. For example: is the user searching to navigate to a particular page? Fortunately, there’s a library called sentence-transformers that is dedicated to creating embeddings. The agent uses semantic search and the Ontology to perform tasks such as; remembering user preferences, storing personal details and managing schedules to provide a more personalized user experience. Semantic search is often in opposition to lexical search, where keywords are used to identify relevant documents to a given query, though it doesn't have to always be this way! In this Example, learn how to create an AIP Agent that can store 'memories' and then recall and reference the relevant memories when required. Manual Search Azure AI Search supports secondary L2 ranking that rescores initial results using machine reading comprehension models. When a user asks a question, the GPT txtai is an all-in-one open-source AI framework for semantic search, LLM orchestration and language model workflows Feb 13, 2023 · Semantic search helps search engines understand queries. “Meaning” is not the same as “intention. Yet, the concept is often misunderstood. As you type a query into a search bar, it uses semantic search to complete your query and suggest relevant search terms based on context, common searches, and past search history. Reduce the cost and latency of LLMs by caching LLM completions. When a user asks a question, the GPT Mar 27, 2024 · For example, one can return the top 25 results using semantic search and then refine to the top 5 with a Reranker. What is semantic SEO and how can you use it to create epic web content that ranks in the search engines? Step-by-step example. Consider these examples with different context for this type of search technology: Nov 23, 2022 · Semantic search is the task of retrieving documents from a collection of documents (also known as a 'corpus') in response to a query asked in natural language. In the context of dense retrieval, embeddings refer to the numerical representations of text data About Retrieval Augmented Generation Examples - Original, GPT based, Semantic Search based. Semantic search is a collection of query-related capabilities that improve the quality of search results for text-based queries. Dec 27, 2023 · Dense Retrieval Dense retrieval, one of the key types of semantic search, is a method that uses neural network embeddings to represent and retrieve information based on semantic similarity, rather than keyword matching, improving search relevance by understanding query and document meanings. The approach will be step-by-step, ensuring clarity and precision throughout the process. This is possible for many items and can then be used Feb 18, 2025 · In a frenzied world of AI, there’s an ongoing game of this or that: full-text search vs. Semantic search does searches based on semantics. It enables you to build AI agents and applications that combine AI capabilities with your code. Aug 10, 2023 · The given examples collectively showcase the versatility and power of semantic search using embeddings and vector databases across various domains, enabling more accurate, context-aware, and May 5, 2025 · Learn how semantic search improves search engine results. 3 Encoding pipeline: from items to vectors and indices The first step to build a semantic retrieval system is to encode items into small vectors (hundreds of dimensions). Learn what it is, why it matters and how to optimize for it. Definition, Usage and a list of Semantic Examples in literature. Jun 30, 2025 · Discover how semantic search elevates user experience by providing more relevant and personalized search results. However, this is typically used in the context of a search query, not in the context of generating chat completions. If you aren’t familiar with Semantic Search, see the Sentence Transformers > Semantic Search for a broader explanation using dense embedding models. Semantic Search Semantic search seeks to improve search accuracy by understanding the semantic meaning of the search query and the corpus to search over. Standard lexical search does searches based on keywords provided in a query. For more information about semantic search, please refer the workshop content. 6. May 24, 2023 · “Similarity search” or “semantic search” refers to finding information that has similar features or meaning from a set of data. Contextual Understanding: It can grasp the context of words May 22, 2025 · This shift means you’re writing for people first, search engines second—and Google rewards that. Aug 1, 2025 · Hybrid Vector Index is available with 23ai release 23. Jan 10, 2025 · Semantic search is a data searching technique that uses natural language processing (NLP) and machine learning algorithms to improve the accuracy of search results by considering the searcher's intent and the contextual meaning of the terms used in their query. Thus, a semantic search Mar 27, 2025 · Semantic search is transforming how CEOs optimize content for AI-driven search engines. It also enhances user experience by making searches more intuitive and context-aware. These technologies helps to give more accurate, relevant and personalized results. As described in the library’s documentation, our use case is an example of asymmetric semantic search because we have a short query whose answer we’d like to find in a longer document, like a an issue comment. Semantic search is an approach to information retrieval that seeks to improve search accuracy by understanding the searcher's intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results. Unlike keyword search, which looks for exact word matches, semantic search finds conceptually similar content — even if the exact terms don’t match. Semantic search is widely used in web search engines, such as Google, but it also has applications in areas such as content management Mar 21, 2025 · What Is Semantic Search: Definition, Examples, And Usefulness Take a look at our article and learn about what semantic search is, how it differs from other types of search, and how it can be used. Feb 28, 2024 · A step-by-step guide to building semantic search applications using OpenAI and Pinecone in Python. Jul 13, 2023 · Semantic search is a hot topic these days - companies are raising millions of dollars to build infrastructure and tools. In this walkthrough we will see how to use Pinecone for semantic search. Why Is Oct 21, 2023 · While Semantic Search involves a wide range of complex techniques and considerations, this project focuses specifically on leveraging semantic similarity for building our search engine. Vector search acts as a building block for semantic search, enabling data retrieval based on relevance. What is a Knowledge Graph? In the history of search, there are many different approaches to search, for example, classic keyword search, which matches individual words or phrases based on textual matching or synonyms. Dec 10, 2022 · A knowledge graph, at least according to Stable Diffusion Semantic search and why it matters To give a concrete example of what would be the consequences of a semantic search, consider the Jun 24, 2024 · Learn how to statically and dynamically retrieve data from plugins for Retrieval Augmented Generation (RAG) in Semantic Kernel. Semantic search is a form of retrieval that allows you to find documents that are similar in meaning to a given query, irrespective of the words used in each query. The OpenSearch search pipeline performs the keyword search using textual inputs and a neural search using vector embeddings generated by Amazon Bedrock using Titan Multimodal Embeddings G1 model. May 21, 2025 · The way search engines understand us, the users, has evolved: their interpretation of our searches is now deeply rooted in meaning, not just matching words. Jul 9, 2025 · Explains the concepts behind vector relevance, scoring, including how matches are found in vector space and ranked in search results. Compare Q with the vectors of all Feb 19, 2024 · To put it simply, vector search and semantic search are interconnected but fundamentally different concepts. May 19, 2025 · Learn to build a semantic search system with OpenSearch! We cover querying indexed embeddings, performing k-NN searches, and retrieving similar movies. Jan 10, 2025 · Lexical search offers exact term matching; full-text search allows for fuzzy matching; semantic search understands context and intent. The Legal Semantic Search app demonstrates how to programmatically bootstrap a custom knowledge base based on a Pinecone vector database with arbitrary PDF files included in the codebase. This tutorial covers the following tasks: Dec 10, 2022 · A knowledge graph, at least according to Stable Diffusion Semantic search and why it matters To give a concrete example of what would be the consequences of a semantic search, consider the Sep 29, 2022 · Semantics = theory of meaning, yet most define semantic search with a focus on intent. Jun 25, 2025 · Semantic Search is the process by which search engines go beyond keywords to understand the intent, context, and relationship between words in a query to deliver more accurate and relevant results. Review examples of semantic search. Jun 13, 2024 · Learn how semantic search enhances user experience by understanding search intent and context to deliver the most relevant results. When you enable semantic search on your service, it performs two primary functions: Improves Search Results: It adds a secondary ranking over an initial search result set by using advanced algorithms that consider the context and meaning of the query, resulting in Apr 30, 2024 · The same can be done in ecommerce, media, and other online services. Gain a better understanding of user search intent. Fine-tuning text embeddings involves adapting an embedding model for a particular domain. This couldn’t be further from the truth - for most use cases, you’ll be fine with just a few lines of Python code and no external dependencies. Semantic search is a collection of query capabilities that improve the quality of search results using text-based queries. Apr 24, 2025 · Discover the semantic search definition, how it works, examples, & why semantic search technology is important for your business. Oct 17, 2023 · Learn what semantic search is and how it works in NLP to improve the accuracy and relevance of search results. Background Jul 1, 2025 · This code sample provides the syntax for semantic ranking. Apr 10, 2024 · Lexical search matches specific words and phrases, offering speed and accuracy for structured queries. As well as being helpful in In this LLM University chapter, you’ll learn how to use embeddings and similarity in order to build a semantic search model. Conclusion In this article, we have explored how to use Semantic Kernel with OpenAI and Azure OpenAI in C#. If a new query is similar enough to a previously cached query, the cached query is returned. The “meaning” here refers to understanding the searcher’s intent to retrieve the correct data; it’s different from Lexical Search (Google, Bing Explore semantic search and how it works. This code repository is for Semantic and Vector Search with Amazon OpenSearch Service Workshop. This is different from traditional search as it only focuses on matching keywords but semantic search tries to understand the meaning behind words, enabling more accurate and relevant search results. A search for " running shoes,” for example, on a large e-commerce website, can illustrate how a semantic search engine operates. Create custom Python functions that AI can call automatically Jul 15, 2023 · Semantic Search with FAISS HuggingFace get_neareast_example and Cosine Similarity Search About This Project In this project, our objective is to conduct a similarity search utilizing the Hugging May 6, 2025 · In this sample, we will explore how to use the file-search tool of an OpenAIAssistantAgent to complete comprehension tasks. This is the source code for the article: Quickstart: Semantic ranking (Python). From the basics to the advanced, here’s how semantic search works: Semantic Search Technology Explained Semantic search is a data-searching technique that strives to understand the meaning and intent behind what you type or speak into May 9, 2022 · The goal here is to get an intuition for what semantic search is and which challenges you will likely face when implementing a semantic search engine in a practical setting. Mar 11, 2025 · Semantic Search is an information retrieval technique (NLP) based upon the contextual meaning and intent behind a user's query. Apr 3, 2024 · Answer: Azure Cognitive Search supports semantic search capabilities which can be enabled by setting the queryType parameter to ‘semantic’. May 19, 2025 · Contains information on how to use a Semantic Kernel Vector store connector to access and manipulate data in Azure AI Search. Semantic search can also perform well given synonyms, abbreviations, and misspellings, unlike keyword search engines that can only find documents based on lexical matches. Semantic search comes truly into its element where a straight keyword match clears the user’s real intent. Mar 11, 2025 · What is Semantic Search? Semantic Search is an information retrieval technique, in which natural language processing (NLP) and machine learning (ML) are leveraged to comprehend the contextual meaning and intent behind the search query of a user, rather than merely matching keywords. This project uses dotenv to easily load values from Jun 13, 2024 · Why is semantic search technology superior to traditional SEO-based search, and how can it help sites provide excellent, Google-like user experiences? Nov 29, 2014 · Discover how Google's Semantic Search enhances relevancy in search results, making connections between related terms for better accuracy. When a query is issued, the search engine employs NLP to transform the words into numerical representations, known as embeddings, that capture the words and the context and nuances surrounding them. 4 days ago · This tutorial guides you through the end-to-end process of creating and using text embeddings for semantic search and retrieval-augmented generation (RAG). LLM queries are compared using vector similarity. Unless noted otherwise, all samples run on the shared (free) pricing tier of a search service. It supports features like fuzzy matching, word stemming, and wildcard searches. Using semantic search we can: Improve Search Results by adding a ranking over initial search results using advanced algorithms that consider the context and Jul 20, 2023 · Build Semantic search applications using Open Source Vector database ChromaDB Learn to use ChromaDB for a semantic search application Introduction Generative AI has taken big strides in the past … This page shows an example demonstrating how to perform semantic search manually, but also how to integrate a SparseEncoder model with popular vector databases/search systems. This approach considers context and the relationships between words to improve search engine rankings and deliver more valuable content to readers. Semantics is one of the important branches of linguistics that deals with interpretation and meaning of the words, sentence structure and symbols, while determining the reading comprehension of the readers how they understand others and their interpretations. Module 1 - Search basics: You will learn fundamentals of text search and semantic search. Aug 31, 2024 · Semantic search vs. Powered by the latest Transformer language models, semantic search allows you to access the best matches from your document collection within seconds, and on the basis of meaning rather than keyword matches. Traditional search engines were like librarians who only scanned book titles for the exact words you said. It combines full text and semantic search in one index. To do that, they use semantic search to: Identify and disqualify low-quality content. It focuses on understanding search intent and providing comprehensive, high-quality information that addresses the user’s needs. Jun 12, 2024 · Enhanced personalization and context awareness The evolution of semantic search will bring unprecedented levels of personalization and context awareness to search experiences. Jun 12, 2024 · Semantic search attempts to apply user intent and the meaning (or semantics) of words and phrases to find the right content. Explore how semantic search transforms digital experiences by understanding context and user intent—not just keywords. This repository contains C# code samples used in Azure AI Search "Day One" quickstarts and tutorials. What are semantic terms in SEO? Semantic terms in SEO are words and phrases that are closely related to your main keyword, either in meaning, intent, or context. For example, the query below finds documents with the title field matching "mountain lake", and combines them with results from a semantic search on the field title_semantic, that is a semantic_text field. Unlike traditional search methods that focus on literal terms, meaning-based search uses machine learning and artificial intelligence to understand context, user intent, and relationships between concepts. Sep 11, 2024 · Semantic search is a central concept in natural language AI systems. This is done using a vector database, where text is stored as embeddings (numerical representations of meaning). Apr 24, 2024 · Learn what semantic search is, how it works, why it can impact your business, and where product discovery tools, like Bloomreach Discovery, can help. Nov 14, 2025 · Semantic Kernel is an open-source SDK by Microsoft that integrates Large Language Models (LLMs) with conventional programming. It demonstrates an approach for adding a semantic configuration to a search index and query parameters to a query. Chapter 3 extends the context of the chat application by using Azure Cognitive Search for data indexing and retrieval. Nov 12, 2022 · Keyword search Before semantic search, systems would typically build a keyword-based index to help find data. While semantic search offers more flexibility and contextual awareness, it Apr 9, 2025 · Read on to understand what is semantic search and what this search engine offers compared to keyword search and similarity search engines. Interested in Semantic Search Examples? Check out the dedicated article the Speak Ai team put together on Semantic Search Examples to learn more. Semantic search is the method GPTs use to find relevant information across uploaded files. Apr 14, 2025 · What Is Semantic SEO? Semantic SEO is the strategy of creating content for topics instead of just keywords. We’ll use a JSON file with essential details for indexing, including the title, description, genre, and an ID for accurate indexation. For example, imagine a search engine that, beyond recognizing your query, understands your long-term projects and goals. NET Core MVC sample Jul 19, 2021 · How Google Uses Semantic Search Google's bottom line is to give users the best search experience possible. For example, your recipe dataset might not contain labels like gluten-free, vegan, dairy-free, fruit-free, or dessert, but these . In order to run this example, you have to supply the Pinecone credentials needed to interact with the Pinecone API. It’s a hybrid approach that allows users to benefit from exact match keyword searches. Jun 13, 2023 · Examples of Semantics The following examples demonstrate how we can use semantics alongside pragmatics (reading words in context) to develop meaning of communication: Slang: Slang is a form of creative and informal language that includes words, phrases, and acronyms used in a particular context. Mar 3, 2025 · In this example, we’re building a semantic search for books. Aug 15, 2023 · Semantic Search is a search with meaning. Sep 19, 2023 · Example of similarity search: Suppose a user submits the query “How does photosynthesis work?”. create-mvc-app C# Tutorial: Create a search app in ASP. May 1, 2024 · Semantic search represents a significant leap over traditional keyword-based search methods. Get answers to many questions you might have and discover how semantic search might make your business better. In all, this tutorial creates a minimal implementation for using Semantic Kernel as a foundation for enabling enterprise data ingestion, long-term memory, plug-ins, and more. Dec 1, 2020 · A semantic search system is composed of two parts: an encoding pipeline that builds indices, and a search pipeline that lets the user use these indices to search for items. Jan 4, 2024 · In Azure AI Search, semantic ranking improves our searches by using language understanding to rerank search results. What is semantic search and how it helps SEO efforts? Examples Semantic AI Search Semantic Search Semantic Search with pgvector and Supabase Edge Functions Semantic search interprets the meaning behind user queries rather than exact keywords. Understand some use cases for semantic search. Jul 23, 2025 · We can use semantic search paired with powerful tools like Elasticsearch and Natural Language Processing (NLP) to find exactly what we need. This helps search systems comprehend user queries in a more human-like way. Jun 10, 2025 · Stop focusing on keywords alone and learn how semantic search affects visibility in search results. Unlike traditional search, which takes into account only keywords, semantic search also considers their meaning in the search context. Jul 27, 2024 · Semantic search enhances traditional search by interpreting the context and meaning behind user queries. Unless noted otherwise, all samples run on the shared (free) pricing tier of an Azure AI Search service. Semantic search Semantic search is a way to search for text based on the inherent meaning or context, rather than relying solely on keywords or other traditional search methods. Unlike traditional search methods that primarily rely on keyword matching, semantic search evaluates the relationships between words and phrases, allowing for a more nuanced txtai is an all-in-one open-source AI framework for semantic search, LLM orchestration and language model workflows Mar 27, 2025 · Semantic search is transforming how CEOs optimize content for AI-driven search engines. It goes beyond keyword matching by using information that might not be present immediately in the text (the keywords themselves) but is closely tied to what the searcher wants. Whether you're searching for products, medical advice, or educational resources, semantic search adapts to your needs and provides tailored results. See an example of semantic search and how to implement it with Python, BERT, and Elasticsearch. Instead Tagged with ai, semanticsearch, openai, python. Jul 21, 2023 · Definition and Overview of Semantic Search Semantic search refers to an advanced search technique that aims to improve search accuracy by understanding the intent and contextual meaning behind a user's query. Sep 29, 2023 · When implementing semantic search with Azure Cognitive Search, consider the following: The choice of the cognitive skills to apply based on the nature of the content. Discover 11 practical use cases that support a better user experience. Learn how it works, why it matters, and where it’s used today. The engine understands that "running shoes" are related to Jun 17, 2025 · In this post, we’ll explore what semantic search is, how it differs from traditional keyword search, and how modern AI models like BERT, Siamese networks, and sentence transformers are making Jul 23, 2025 · Semantic search relies on a variety of advanced technologies that help search engines understand the context, intent and meaning behind queries. In this tutorial, we describe how you can use Qdrant to navigate a codebase, to help you find relevant code snippets. Real-life tips for marketers and content creators inside! Jan 24, 2025 · Learn how semantic search delivers relevant results by understanding user intent, context, and relationships between words in the comprehensive guide. Oct 11, 2024 · What is Semantic Search in AI? Learn how it enhances user experience and influences the future of digital search. Mar 16, 2024 · The magic behind Semantic Search The operational backbone of semantic search lies in creating and analyzing data embeddings and vectors. Mar 26, 2024 · Discover how semantic search transforms SEO practices with real-life examples. I think due to this, most semantic search tutorials I see assume you need lots of tools like vector databases and LangChain. Semantic search, on the other hand, is like a librarian who listens to your question, understands what you’re really asking, and then finds the book that best answers it even if the exact words don’t match. lsqyzcszdoagksmwpwuqezhkqbcfllqaexhkvmbtvfciwwfptyoxfbzktkoohupvtntxjhjvbucdsmxz