AI Search

1. What is AI Search?

Unlike traditional keyword-based search engines, AI Search refers to the use of AI techniques to enhance search functionality, making it more intelligent, accurate, and context-aware. AI-powered search systems leverage Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) to understand and provide personalized responses.

2. How AI Search works ?

2.1. Query understanding

When a user enters a query, AI first processes it using NPL to understand intent rather than just matching keywords. E.g. Searching system

• Query: “best laptop for programming under $2000”.

• Process:

– Recognizes “best” as a ranking preference.

– Understands “laptop for programming” refers to developer-friendly specifications (RAM, CPU,…).

– Identifies “under $2000” as a price filter.

• Result: Searching system provides a list of laptops with specs suitable for programmers, not just random laptops under $2000.

2.2. Semantic search

Semantic search enables AI to understand the meaning behind words, even if they aren’t an exact match. E.g. E-commerce search

• Query: “running shoes with good ankle support”.

• Process:

– Recognizes “running shoes” as footwear category.

– Understands “good ankle support” as an important feature.

– AI expands results to include products with “top running shoes”, even if “ankle support” isn’t in the product title.

• Result: AI displays the best-matching running shoes, even if exact keywords don’t match.

2.3. Personalized search

AI tailors results based on user behavior, preferences, and past searches. E.g. YouTube AI Search

• Query: “top AI videos”.

• Process:

– Recognizes that the user frequently watches tech-related content.

– Prioritizes AI videos that the user might like.

• Result: The search returns AI videos relevant to the user, not just trending videos.

2.4. AI-Powered ranking

Search engines use machine learning models to rank search results based on various factors. E.g. Search algorithm

• Query: “how to improve website SEO?”.

• Process: AI evaluates hundreds of ranking factors, such as

– Domain authority, reputation of the website.

– Content relevance.

– User engagement…

• Result: High quality articles on SEO appear at the top of search results.

2.5. AI-Driven recommendations

AI predicts what users might search for next, making the experience faster. E.g. Search auto suggestions

• Query: “best smartphones for”.
• Process: AI analyzes popular searches and personal preferences.
• Result: It returns

– “best smartphones for pictures”.

– “best smartphones for videos”…

3. Popular tools & frameworks for AI-Powered Search

• Search engines (traditional & hybrid search): Elasticsearch, OpenSearch,…
• Vector search & AI Powered semantic search: FAISS, Pinecone, Weaviate,…
• NLP & Query understanding (preprocessing, query expansion, NER): spaCy, NLTK,…
• Machine Learning based ranking & AI Search ranking: XGBoost, LightGBM, TensorFlow Recommenders (TFRS),…

4. Popular AI API services

• Algolia AI Search: Algolia offers AI-powered search capabilities, providing relevant results from your search index based on defined business outcomes.
• Google Cloud AI APIs: Google Cloud provides a suite of AI APIs, including natural language processing and machine learning tools, to enhance search functionalities.
• Azure Cognitive Search: Microsoft’s Azure Cognitive Search is a cloud-based service offering indexing and querying capabilities, enriched with AI features like natural language processing and image recognition.
• OpenAI API: OpenAI provides APIs that can be utilized for various AI applications, including enhancing search functionalities with advanced language models.

5. Architecture

AI Search is a new intelligent layer that can be added on top of your existing system.

5.1. Traditional architecture

5.2. AI Search architecture