Semantic Search Via Embeddings And ChatGPT 

Introduction

In the digital age, users are inundated with information, making it essential for platforms to provide efficient search capabilities. A cooking recipe website with an extensive collection of 100k recipes sought to improve the discoverability of its content. By harnessing the power of semantic search, the goal was to enhance user experience, making it easier for visitors to find relevant recipes based on their specific queries.

Challenge

The primary challenge faced by the client was the need for better discovery of recipes through an effective search mechanism. Although semantic search technologies could significantly enhance document retrieval and relevance, the complexities of implementing these solutions often deterred many companies. The client recognized that to improve user satisfaction and engagement, they needed a scalable and cost-effective solution that could seamlessly integrate into their existing platform.

Solution

To address the client’s needs, SmartCat developed a comprehensive semantic search solution that leveraged advanced language models and retrieval-augmented generation (RAG) techniques. The solution included:

1. Embedding Client Documents: The recipes were embedded into a semantic search framework, supported by a vector database for efficient storage and retrieval. This setup enables a deeper understanding of the content’s context and meaning.
2. Retrieval-Augmented Generation (RAG): The RAG approach enhances a user’s search by retrieving relevant documents, enriching the query, and generating a contextually informed response with an LLM like GPT-4. This ensured that users received the best matches based on their specific inquiries.
3. Utilization of Large Language Models (LLMs): The solution incorporated LLMs, such as ChatGPT-4o, to assist with both ranking the retrieved documents and generating the final response. These models play a dual role, refining search results and enhancing the generated output for greater relevance and depth.

Results

The implementation of the semantic search solution led to several positive outcomes for the client:

• Improved Discoverability: Users experienced enhanced ability to find relevant recipes, resulting in increased engagement and satisfaction with the website.
• Higher Search Relevance: The RAG approach, combined with LLMs, ensured that users received more accurate and contextually appropriate results, improving overall search experience.
• Cost-Effective Scalability: The solution was designed to be scalable, allowing the client to expand its database further without compromising search performance.

Smart Tip

Investing in semantic search technologies can dramatically enhance user experience by providing more accurate and relevant results, ultimately driving engagement and retention on digital platforms.

Smart Fact

On average, personalized search solutions can increase user engagement rates by over 50%, leading to more meaningful interactions and higher user satisfaction.

Technologies Used

• Semantic Search Framework: Embedding techniques for document processing.
• Retrieval-Augmented Generation (RAG): Methodology for retrieving and generating relevant search results.
• Large Language Models (LLMs): Technologies like ChatGPT for ranking and explaining search results.

About the Client

The client operates a popular cooking recipe website that offers diverse recipes to cater to food enthusiasts. With a rich database of over 10,000 recipes, the site aims to inspire and assist users in their culinary adventures. However, as their recipe collection grew, so did the challenges of ensuring that users could efficiently navigate and discover recipes that matched their needs.