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Hello, everyone. My name is Alexandre, or Alex. You can call me whichever you prefer.
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Before we dive in, I want to share a bit about myself. I have a simple blog—actually, I have two domains—and I am migrating content from one to the other. The current one is devcenter.com, and I have had 8,000 views so far. I know it’s a baby blog, but little by little, we are growing.
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I have worked as a software developer with Ruby on Rails for about 14 years. Additionally, I've been working as a technical interviewer for about a year and a half, interviewing candidates who apply for jobs at an American company in a freelance position.
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Today, we are going to talk about implementing semantic search in Rails using vector databases. We are certainly thrilled by the power of large language models nowadays and the impact of AI in our professions and society. Our goal is to implement something that improves the user experience.
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Since we are discussing semantic search and vector databases, we'll start by focusing on semantic search. Let me show you how semantic search differs from traditional lexical search.
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I’m talking about the search feature we have in our applications, where users can type something, and then it brings up results based on that input. Traditional search relies heavily on keywords; for instance, if a user types 'Ruby,' the system searches for that exact keyword in the database.
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However, this approach has a limitation. If someone looks for a Ruby programming book but types only 'Ruby,' they might not find what they’re looking for. The context isn’t considered in traditional search, which often leads to disappointing outcomes. For example, someone might search for 'Ruby book' but still receive irrelevant results, as the system does not recognize the intention behind the search.
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In contrast, with semantic search, we want to take the user's context and intention into account. This allows us to provide more relevant search results. For example, if a user wants to learn programming, they would ideally get results tailored to just that, rather than a set of unrelated results based solely on keywords.
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We also need to keep in mind that traditional keyword-based searches do not consider the user’s intention. Let's say the user decides to switch from searching for Ruby to Rails, thinking the problem lies with Ruby. However, they might still encounter similar issues without context.
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In semantic search, the aim is to deliver results that are not only keyword-based but also take into account the context and the user’s intention. We need large language models to help achieve this. The idea is to recognize how context affects what results should be displayed, improving overall user experience significantly.
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With semantic search, when a user searches for Ruby, for example, they should see relevant books and resources that align with their interest in learning programming, thereby avoiding unrelated results that lead to frustration.
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Now, since traditional searches are often repetitive, results will remain the same for everyone without considering individual contexts. Semantic searches, however, allow for personalized results, which can differ based on each user’s intent and the context of their queries.
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In practical terms, when a user types 'Rails,' traditional search may yield poorly related articles. With semantic processing, the focus would be not only on catching keywords but also understanding what information the user is actually seeking.
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Traditionally, if the search returns irrelevant listings, users have to go through and refine their queries multiple times, which is cumbersome and inefficient. In a semantic search, much of that refining can be alleviated through intelligent algorithms that understand user intent more profoundly.
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In summary, traditional search is primarily keyword-driven, while semantic search focuses on understanding relevant meaning, context, and user intention. This presents a significant advancement in how we handle searches in modern applications.
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By working with vector databases, large quantities of data can be transformed into mathematical representations, allowing searches that prioritize meaning over mere keywords. With this new context-based approach, results become not only more accurate and relevant but also dynamically adaptable to user needs.
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Now, let’s explore how vector databases operate as the foundation for implementing semantic search. Vector databases can efficiently handle the complexity of dimensionally rich information, converting user input into vectors that pinpoint user intents more accurately.
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Vectors are mathematical representations of data points within a multi-dimensional space. This offers a more sophisticated analysis of data compared to traditional flat-data structures, enabling nuanced searches which lead to more tailored results for users.
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Consider visualizing data points in a two-dimensional graph. Each point represents a unique data attribute, showing how they are related through their respective contexts. When these visualizations expand into more dimensions, it reveals an intricate network where similar concepts can be identified and processed together.
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This multi-dimensional characteristic of vectors is essential for representing comprehensive data intuitively. As we utilize this in applications, we would realize how to group and find similar vectors, producing relevant results based on the underlying meanings.
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When we convert input data—regardless of whether it’s text, audio, or images—into vector forms, we can efficiently identify and present similar results across diverse contexts and intentions. This versatility makes vector databases advantageous compared to traditional data storage methods.
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Traditional databases require precise queries and often lead to irrelevant data without dynamic contextual understanding. In contrast, vector databases allow data to be analyzed and related in more meaningful ways, enhancing user-centered search capabilities.
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Now, let’s discuss how to put this knowledge into practice within a Rails application. The implementation requires setting up vector databases that work with OpenAI’s powerful models. By obtaining an API key for OpenAI, we set the stage for our application to harness the power of these advanced algorithms.
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We’ll create a simple Rails application utilizing the PG Vector extension with PostgreSQL to facilitate working with vector embeddings. This involves setting up the necessary migrations to define how our data interacts with vector structures.
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Once the application is correctly set up, we would manage interaction between the OpenAI model and your Rails backend. Through this integration, we enable our application to take user input, generate vectors, and use those vectors to query the vector database, yielding relevant search results.
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Next, by leveraging gems to work with PG Vector and OpenAI, we configure the application to encapsulate API keys securely, while retaining seamless operation in finding and presenting data. This setup empowers developers to create intelligent search experiences in their applications.
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In terms of managing data, we will need to define chunk sizes to ensure that we efficiently load and process data without overwhelming the application. This is important, particularly as unstructured data becomes more prevalent in modern applications.
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Handling the size of data before converting it into vector form allows us to filter through information readily. During the search process, it also helps us to present the most relevant results by focusing on meanings rather than isolated keywords.
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Now we can discuss practical coding, such as creating the necessary functions to transform user inputs into vector embeddings and subsequently retrieve those embeddings for relevant searches. Developers should pay attention to structuring their variable environments carefully to ensure smooth operation with OpenAI APIs.
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When we run our application, the complete integration allows us to take advantage of the context surrounding each search query. In closing this discussion, I hope you see how semantic search using vector databases can redefine search capabilities in Rails applications, creating experiences that engage users more effectively.
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Thank you very much, everyone! If there are any questions or clarifications needed, I’d be happy to assist further.
