How to Build an AI-Based Semantic Search App

Ever typed something into a search bar and thought, "Why am I getting these results?" Like typing [your bank name] log in and getting their blog page instead? That's because traditional keyword-based search engines are a bit too literal.

Semantic search is the brainier cousin of keyword search. It has unlearned blindly matching words and started understanding what you mean. And, with large language models (LLMs), retrieval-augmented generation (RAG), LangChain semantic search tools, or LlamaIndex, we're seeing massive improvements in how AI retrieves and generates information.

Today, many business leaders seek to build their own semantic search-driven generative AI apps. Let's break them down and see how you can develop one, too.

What Is Semantic Search Based on AI?

It's no news that Google excelled at semantic search a little too long ago. You can type "movie where robots fight in a ring" and get "Real Steel (2011)" instead of some random "Transformers" fanfic. No surprise either that they have managed to improve their relevancy by 50% over seven years. That's how semantic search works, and companies want to get a piece of this power.

Semantic search is the power behind generative AI. Instead of scanning keywords, it reads and understands the content before recommending the best match. It uses techniques like:

• Word embeddings, or mapping words into a multi-dimensional space to capture meaning.
• Context analysis, or figuring out if "jaguar" means a car, an animal, or a sports team based on the surrounding words.
• Neural networks and deep learning, or helping AI think more like humans when processing queries.

So, when you ask ChatGPT "How do I fix a leaky faucet?", you want actual repair steps and not an article debating the best plumbing brands. AI-powered assistants using semantic search get this right.

How to Build an AI Assistant

Article by

Anna Polovnikova

Why Is Semantic Search with AI Important? (Unless You Love Bad Search Results)

Keyword search is clueless because it doesn't grasp intent, but semantic search gets it. It analyzes context, meaning, and relationships between words to return the most relevant information. Here's why it's good and not.

Why AI Semantic Search Rocks

Context is king is the motto of semantic search. Since it's not a mindless keyword match, it delivers:

1. Better accuracy. Users don't have to dig through hundreds of irrelevant results. e-commerce giants like Amazon and search engines like Google, Bing, and OpenAI have all gone all-in on semantic search.
2. Smarter recommendations. Whether you're on a streaming platform, shopping site, or even a healthcare portal, semantic search provides suggestions that make sense. No more nonsense like "If you liked action movies, here's a documentary on knitting".
3. Improved shopping experience. Irrelevant shopping recommendations are one of the worst online experiences ever. That's why keyword-based PPC ads got so annoying at some point. Semantic search fixes this, showing you only relevant results based on intent, not just words.

The Not-So-Perfect Side of AI Semantic Search

It sounds all good, but there are a few things to keep in mind, too. First, semantic search machine learning and AI are quite expensive to implement. Building a truly intelligent search system requires advanced AI models, training data, and constant fine-tuning. The cost to build an AI solution can be pricey, and not every company can afford it.

Next, it's still a work in progress. While it's better than keyword search, semantic search can still misinterpret queries, especially in niche areas or with ambiguous phrases. Try searching for "turkey" in November. The country or the bird?

Finally, AI-powered search engines often analyze user behavior to refine results. That's great for accuracy, but it also raises data privacy questions. How much do you really want AI to know about you?

Anyways, if you're really into building this kind of app, let's pop the hood and break down the tech that makes semantic machine learning happen.

What Technologies Are Used to Power AI-Based Semantic Search?

AI-powered semantic search isn't one-size-fits-all. The gen AI tech stack will vary depending on the type of solution. Different industries use different approaches to optimize search experiences, too. Here are a few examples:

• Personalized recommendations. Netflix or Amazon tailor their results based on user behavior, past choices, and search history.
• Knowledge management/discovery. It's used to make internal documents searchable, speeding up research and decision-making.
• Image search. It allows users to search using images instead of text.
• Voice search/recommendations. Voice recognition enables hands-free, AI-driven searches (e.g., Siri and Alexa).
• Multimodal search. It combines text, images, and video inputs to provide richer, more context-aware search results.

Now that we've set the stage, let's go straight to the tech side of things and overview what technologies are used to power semantic search.

Natural Language Processing (NLP) Is Behind Understanding

NLP helps search engines understand human language. Not just words, but context, tone, and intent. It figures out that when you type "cold play tickets", you mean concert tickets, not a guide on how to play in freezing weather.

Used by: Google, Bing, Amazon Alexa, Siri

Word Embeddings Are Behind Meaning

Keyword search sees words as just letters on a page. Semantic search AI sees meaning. That's thanks to word embeddings like:

• Word2Vec
• GloVe
• FastText

These models map words into a giant web of relationships so AI can understand that "smartphone" and "mobile" are related even if they don't share any letters.

Used in: Google Search, Chatbots, Recommendation Engines

Large Language Models (LLMs) Are Behind Responses

A semantic search LLM models like GPT (OpenAI), Gemini (Google), and Claude (Anthropic) bring deep understanding to search. LLM semantic search helps AI analyze entire sentences and answer in a way that feels human.

• You ask: "What's the best way to fix a leaky faucet?"
• Old search: "Here are 10,000 results. Good luck."
• LLM for search: "Here's a step-by-step guide with tools you'll need."

Used in: ChatGPT, Google Bard, Bing AI

Knowledge Graphs Are the Common Sense Database

Imagine AI had a cheat sheet on how the world works. That's a Knowledge Graph—a giant web of connected facts. So, when you search for "Tom Holland movies", a knowledge graph helps AI pull up Spider-Man, Uncharted, and The Crowded Room.

Used by: Google Search (Google Knowledge Graph), Bing, Amazon

Vector Databases Are the Filing Cabinet of AI Search

Semantic AI search doesn't just look at words because it stores concepts as mathematical vectors. It's a high-tech filing cabinet that organizes information based on meaning. Vector databases help AI retrieve the most relevant data, even across millions of documents. Some popular ones include:

• FAISS (Facebook AI)
• Pinecone
• Weaviate

Used in: AI search engines, AI chatbots, recommendation systems

Retrieval-Augmented Generation (RAG) Is AI's Fact-Checker

LLM semantic search is smart, but it can hallucinate (a fancy way of saying they sometimes make stuff up). RAG fixes that by fetching real-world info before answering.

• You ask: "What's the latest iPhone model?"
• Basic AI: "Uh… iPhone 14?"
• AI + RAG: "The latest iPhone is the iPhone 16 Pro Max, released in September 2024."

Used by: OpenAI, semantic search LangChain, LlamaIndex

And that's just scratching the surface. Up next is the semantic search algorithm in AI that makes things happen.

How to Integrate AI into an App: All You Need to Know

Article by

Anna Polovnikova

How Does Semantic Search Work?

Forget the old-school keyword search, here's a step-by-step breakdown of how semantic search AI works.

1. A User Submits a Query

It all starts when you type (or speak) your search. Unlike traditional search, which looks for matching words, semantic AI tries to understand what you're asking for.

2. Understanding Intent and Context

AI analyzes intent. It asks:

• What does the user actually want?
• Are there synonyms or related terms to consider?
• Does this query match a past search pattern?

If you search for "how to boost engagement", AI understands you're probably looking for social media tips, not a gym workout plan.

3. Extracting Intent and Relationships

Now, AI breaks down your query and maps out how words relate to each other. Here, embeddings turn text into structured data points in a way computers can actually use.

4. Retrieving Relevant Data

Once AI understands the intent, it digs into a vector database. There, it finds documents, articles, or products that best match your needs, even if they don't contain the exact words you typed.

5. Ranking Data by Relevance

Not all results are equal! AI scores and ranks everything based on how closely it matches your intent. Ranking factors include:

• similarity score (how close the meaning is);
• freshness (is it up-to-date?);
• authority (is it from a trusted source?).

6. Returning Ranked Results

Once the best matches are found, AI sends them back to the LLM. In turn, it refines and re-ranks them before presenting them to you.

7. Presenting the Final Output

Finally, you see the results—whether that's a list of links, a direct answer, or even AI-generated summaries. For example, instead of 100 links on "best running shoes", AI compiles a list with key takeaways, pros and cons, and even comparisons.

Semantic AI is already changing how we search for and find information, but this is only the beginning. Advances in multimodal AI (understanding text + images + video), better personalization, and real-time learning will make it smarter, faster, and more accurate.

Until then, at least we don't have to suffer through random, keyword-matched nonsense anymore! Next, let's dive into how you can build a better search.

Top Generative AI Development Companies

Article by

Anna Polovnikova

How to Build an AI-Powered Semantic Search App

Want a search engine that actually gets what your users are looking for, not just a fancy string of keywords? Then, it's time to get into generative AI and vector search. Here's how you do it.

Step 1. Get Your Data Right [Build a Knowledge Graph]

You can't find what you need if your data's a mess. A knowledge graph organizes things so they can be searched the smart way.

• Grab data from your docs, APIs, and databases (the more the merrier)
• Clean it up, goodbye duplicates
• Store both structured and unstructured data neatly in your knowledge graph 

Step 2. Slice Your Data into Bite-Sized Pieces [Speed + Accuracy]

Big chunks of data slow down the process and mess with search accuracy. Break it down into smaller, more digestible pieces for faster, better results.

• Chop large text into smaller, meaningful bits
• Keep the juicy details for top-tier search results
• Use tools like RecursiveCharacterTextSplitter for that perfect slice

Step 3. Turn Text Into Numbers [Generate Embeddings]

Text is cute and all, but computers don't really get it. So, we turn it into numbers (embeddings, to be exact).

• Pick your favorite embedding model (OpenAI, Cohere, BERT, you name it)
• Convert all that text into numerical vectors
• Test the performance based on multilingual abilities and accuracy

Step 4. Store and Manage That Data [Time for a Vector Database]

Your data's got to be stored right for lightning-fast searches. Vector databases are indexing data by meaning, not just words.

• Pick a vector database (Pinecone, ChromaDB, Weaviate, or whatever floats your boat)
• Index the embeddings for quick, relevant retrieval
• Make sure it can scale for real-time search!

Step 5. Implement Semantic AI Search

Now the magic happens. With your data prepped and ready, it's time to make it search-friendly for the semantic search model to function properly.

• Convert user queries into embeddings
• Find the closest matches in your vector database
• Rank the results based on similarity scores (who's the best match?)

Step 6. Rank and Personalize [Bring It Home]

All results aren't created equal, and personalization is key. You want the best results up front, every time with semantic machine learning and AI.

• Rank by semantic similarity, no more irrelevant results
• Use user behavior to tweak rankings (they'll love it)
• Personalize based on what they like, what they've clicked, and what they've bought

Step 7. Make It Talk [Use Generative AI for Human-Like Responses]

Forget just showing results, talk. Generative AI can summarize, answer questions, and give a more human-like interaction.

• Plug in an AI model like Gemini, GPT, or Llama for dynamic answers
• Add multimodal features like text, images, or voice search
• Fine-tune it to fit your app and users' needs (because everyone's different)

And bam! You've got yourself a semantic AI search app. Go ahead, take it for a spin!

Ready to Build a Semantic Search App?

At Upsilon, we create AI-powered search apps that actually understand what your users want, not just matching keywords. Whether it's for e-commerce, content search, or something else, we can help you build it right.

Need help getting started? Reach out to us to discuss your ideas or if you want to learn more about our gen AI development services, and we'll guide you through every step of the process. Let's build semantic search technologies together!

FAQ

1. What is semantic search technology?

Semantic search technology is an advanced search method that goes beyond keyword matching by focusing on understanding the meaning and context behind a user's query. It uses natural language processing (NLP) and machine learning to interpret intent and deliver more relevant, accurate search results.

2. What is semantics in AI?

AI semantics refers to the ability of artificial intelligence systems to understand and interpret the meaning of words, phrases, and sentences in context. It moves past simply recognizing individual words and focuses on grasping the relationships between concepts, allowing AI to understand human language in a more nuanced way.

3. What is semantic search in LLM?

Semantic search in AI is a technology that improves search accuracy by analyzing the meaning behind a search request, rather than relying solely on keyword matching. LLM semantic search utilizes large language models to grasp the meaning behind queries, offering more contextually relevant results. Unlike traditional search methods, which rely on keyword matching, semantic search machine learning examines user intent and context.