Master Semantic Search with Rails and OpenAI

Master Semantic Search with Rails and OpenAI

Table of Contents:

1. Introduction
2. What is Semantic Search?
3. The Need for Semantic Search
4. How Semantic Search Works
5. Building Semantic Search in Ruby
   1. Installing the Required Gems
   2. Breaking Content into Chunks
   3. Generating and Storing Embeddings
   4. Looking Up Chunks
   5. Searching with Nearest Neighbors
6. Improving Search Results
   1. Implementing Caching
7. Conclusion

Introduction

In this article, we will explore the concept of semantic search and how it can be implemented using Ruby. We will discuss the need for semantic search and its advantages over traditional text search methods. Additionally, we will walk through the step-by-step process of building a semantic search engine, covering topics such as breaking content into chunks, generating and storing embeddings, looking up chunks, and performing searches using nearest neighbors. Finally, we will explore techniques to improve search results and conclude with key takeaways.

Building Semantic Search in Ruby

Semantic search is a powerful technique used to enhance search results by generating more contextually Relevant suggestions. Unlike traditional text search methods that rely on exact matches, semantic search understands the meaning behind the search query and returns results that are conceptually similar or related. In this section, we will go through the process of building a semantic search engine using Ruby.

1. Installing the Required Gems

To begin, we need to install the necessary gems for our semantic search engine. We will use gems like Lang chain RB and Neighbor to handle text manipulation and nearest neighbor search. These gems provide useful tools for tokenizing text, generating embeddings, and finding the most similar chunks.

2. Breaking Content into Chunks

Before we can generate embeddings, we need to break our content into Meaningful chunks. This step is important as it allows us to process smaller portions of text efficiently. We can achieve this by using the chunker provided by the Lang chain RB gem. By specifying the chunk size, overlap, and separators, we can split our content into manageable chunks that make Sense semantically.

3. Generating and Storing Embeddings

Once we have our chunks, we can proceed to generate and store embeddings for each chunk. Embeddings are numerical representations of the text that capture its semantic meaning. We can use an AI model like OpenAI's language model to generate these embeddings. By storing the embeddings along with the corresponding chunk, we can efficiently retrieve and compare them during the search process.

4. Looking Up Chunks

To enable efficient retrieval of relevant chunks during the search, we need to implement a Lookup mechanism. We can use the Nearest Neighbor tool provided by the Neighbor gem for this purpose. By computing the Cosine similarity between the query embedding and the embeddings of all the chunks, we can find the most similar chunks. This allows us to narrow down the search results and improve the relevance of the suggestions.

5. Searching with Nearest Neighbors

With our lookup mechanism in place, we can now perform semantic searches using the nearest neighbors algorithm. When a user enters a search query, we generate an embedding for the query and compare it with the embeddings of all the chunks. The most similar chunks are then returned as search results. By leveraging the concept of cosine similarity, we can ensure that the search results are highly relevant and contextually meaningful.

Improving Search Results

While our semantic search engine is up and running, there are several ways we can improve the search results to provide a better user experience.

1. Implementing Caching

To reduce costs and improve performance, we can implement caching for search results. By caching the results of previous searches, we can avoid making unnecessary API calls to generate embeddings. Instead, we can directly fetch the cached results, significantly reducing the response time for subsequent searches. This caching mechanism can be implemented using tools like Redis or Memcached.

Conclusion

In this article, we explored the concept of semantic search and its significance in improving search results. We learned how to build a semantic search engine using Ruby, covering topics such as breaking content into chunks, generating and storing embeddings, looking up chunks, and performing searches using nearest neighbors. We also discussed techniques to improve search results, such as implementing caching. By leveraging semantic search techniques, we can enhance the understanding and retrieval of relevant information, resulting in more accurate and contextually meaningful search results.