AI Concepts: Embeddings, Vector Stores, Traditional RAG, and AI Agents

[Audio] Welcome to our presentation on AI concepts, embeddings, vector stores, traditional retrieval-augmented generation, and artificial intelligence agents. Today, we're going to explore the fascinating world of artificial intelligence, concentrating on retrieval-augmented generation, a groundbreaking innovation that's transforming the way we communicate with machines. Let's begin..

[Audio] Despite their impressive capabilities, Large Language Models have significant limitations. They cannot access new information once they've been trained, leaving them with outdated knowledge and unable to provide real-time or up-to-date information. Moreover, they may generate factual mistakes, particularly when addressing less common or specialized topics. The models can also exhibit the hallucination problem, where they confidently produce information that appears plausible but is actually false. Furthermore, they lack the ability to access external information, such as databases or the internet, restricting their capacity to provide precise and accurate details on specific topics..

[Audio] Retrieval-Augmented Generation solves these issues by fetching relevant information from external sources. This allows Large Language Models to rely less on their internal knowledge and instead search databases or documents for real-time, up-to-date information. This reduces hallucinations and improves the accuracy of generated content, ensuring that the model generates more reliable, fact-based answers, even for complex queries..

[Audio] When we retrieve information, we're not looking at the entire dataset, but rather picking out the specific details we need to answer our question. This is the core idea behind information retrieval - finding the relevant bits within a vast collection of data. Whether it's text, images, audio, or video, IR helps us pinpoint what we're searching for. Indexing is the starting point, where we convert external data into numbers, making it easier to search. For instance, if I'm seeking info on the ICC Cricket World Cup 2023, IR systems would scan the database, ranking documents based on how relevant they are..

[Audio] Retrieval-Augmented Generation, or RAG, is a hybrid framework that combines retrieval and generation processes. This allows large language models to access external knowledge bases, enhancing their ability to produce accurate and contextually relevant responses. By leveraging real-time information, RAG integrates dynamic interaction mechanisms that improve the accuracy of generated outputs and align them with user intent..

[Audio] The individual shown in this image is deeply focused on reading a book, indicating a high level of concentration and involvement. This might symbolize the idea of immersion, where someone becomes completely absorbed in a specific task or pursuit. The fact that there is a book present suggests a craving for knowledge and learning, emphasizing the significance of education and self-improvement..

[Audio] The retrieve phase commences by transforming the input query into vector embeddings utilizing an embedding model. This model converts the input into a numerical form suitable for similarity searches. The query is subsequently dispatched to a Vector Database, which comprises embeddings of documents, text data, or any pertinent external information. The database is indexed according to vector similarity, including cosine similarity. Ultimately, a retriever component selects the top N documents or relevant data points based on similarity, ranking them in order of relevance via semantic search or alternative retrieval algorithms..

[Audio] The concept of embeddings is a fundamental idea in artificial intelligence. Each object, such as an email, can be represented as a unique vector, which is a mathematical representation of its properties and characteristics. This enables comparison and manipulation of the vectors to gain insights about the objects themselves. For example, by creating a vector for each email based on its content, sender, recipient, and other relevant features, we can identify patterns, relationships, and anomalies within the dataset. This is the essence of embedding, where complex data is reduced to a lower-dimensional space, making it easier to work with and analyze..

[Audio] Embeddings are a method to convert words or phrases into numerical vectors, allowing us to process text data using mathematical operations, capturing the essence or meaning of the text rather than its literal meaning. This concept is essential in natural language processing, enabling tasks such as sentiment analysis, topic modeling, and information retrieval..

[Audio] Machine reading and understanding texts involves creating word vectors that capture the essence of words. These word vectors are created by using complex algorithms that analyze vast amounts of data. The resulting word vectors are then used to train language models that can recognize patterns and relationships between words. By comparing these word vectors, machines can determine which words are more similar to each other, allowing them to understand the context and meaning of written texts..

[Audio] Embeddings are numerical representations of words or phrases that help AI models understand language. Vector stores are databases of these embeddings, where they are stored for efficient access. Traditional RAG refers to the process of gathering information from various sources, combining it, and generating a response. AI agents are intelligent software programs that can perform tasks and make decisions on behalf of their users, trained using machine learning and capable of adapting to new situations..

[Audio] With thousands or even millions of embeddings to manage, storing and searching these vectors efficiently becomes crucial. Locality sensitive hashing partitions vectors into "buckets" based on their similarity. When a search query is performed, it's hashed into one of these buckets, significantly reducing the number of comparisons needed. Vector databases offer two primary benefits: faster searches and optimal storage. They're designed to handle the unique requirements of vector data, ensuring efficient storage and retrieval. As vector databases continue to evolve, they're becoming increasingly essential for applications involving semantic search, recommendation systems, and more..

[Audio] The retrieved information is used to add more context to the query or prompt, helping the model understand the task better. The top N documents from the retrieval stage are passed back to the model as retrieved context, which is then appended or augmented with the original user query to provide more details and make the response more relevant and accurate. The goal is to combine the external knowledge base with the model's trained knowledge to handle specific or unseen questions better..

[Audio] The final stage of our hybrid framework generates the actual output by combining the original prompt or query with the augmented data retrieved during the previous phase. This combined input is then passed to large language models like GPT, BERT, or other transformer-based models. They process the input and generate a response that is more accurate and context-aware due to the additional information they receive from the retrieval phase. The output is finally formatted into a response that is typically displayed in the user interface, providing users with enriched information that addresses some limitations of traditional language models..

[Audio] The workflow starts by extracting relevant information from unstructured data sources such as text documents, emails, and social media posts. This extracted information is then converted into numerical vectors using methods like word embeddings. These vectors are stored in a vector store, allowing for quick searching and retrieval. The resulting vectors are used to train a traditional RAG model, which learns to recognize patterns and connections within the data. Once trained, the RAG model is deployed to generate insights and predictions based on new, unseen data..

[Audio] RAG systems have difficulty producing accurate content that matches users' expectations. They may fail to grasp the context and intent behind queries, resulting in irrelevant or inaccurate outcomes. Furthermore, combining data from multiple sources and generating practical insights can be difficult. Moreover, customization and scalability issues can impede their performance..

[Audio] Here is the rewritten text in English: The sample code for Retrieval-Augmented Generation using KDB.ai vectors in a Google Colab Notebook is provided below. This code illustrates how to implement a traditional RAG model utilizing KDB.ai vectors. The code snippet showcases the implementation of the RAG algorithm, encompassing the computation of similarity scores between input queries and stored vectors. This code serves as a foundation for constructing a practical RAG application..

Summary. A diagram of a tree structure Description automatically generated.

AI Agents. None.

[Audio] Agentic RAG is a more advanced form of Retrieval-Augmented Generation, where an agent takes charge of determining the most relevant resources or databases for a user's query. This allows it to handle complex, multi-tasking scenarios, making it more adaptable and able to make decisions based on the situation. Unlike traditional RAG, this system can work independently, making decisions and taking actions accordingly..

[Audio] The flowchart illustrates the transformation of text into vectors, which begins with tokenization, where words are divided into smaller units called tokens. These tokens are subsequently processed by various algorithms to produce numerical representations, referred to as embeddings. The resulting vector space has numerous applications, including natural language processing, information retrieval, and machine learning models..

[Audio] In this example use case, agentic RAG creates a multi-agent workflow where multiple agents work together to achieve a common goal. One agent collects data, another processes it, and another makes decisions. The LangChain graph facilitates seamless communication and coordination among these agents, enabling more complex workflows and increasing overall efficiency..

[Audio] ReAct is a tool that allows us to build reasoning and action agents from scratch. This hands-on guide demonstrates how we can create these agents using Gemini. With ReAct, we have more control over the behavior and decision-making processes of our AI agents. We can use this tool to explore new possibilities for AI applications and develop innovative solutions..

ReAct Workflow. A diagram of a process Description automatically generated.

[Audio] In designing LLM-based agents, key principles include understanding the problem domain, defining agent goals and constraints, identifying relevant data sources, and selecting suitable LLM architectures. These principles enable the creation of effective AI agents that can learn from large datasets and adapt to changing environments. By applying these principles, developers can build AI agents that are capable of making informed decisions and taking actions that align with their goals.

[Audio] Selecting the right RAG system depends on the business's specific requirements. For straightforward, low-risk tasks, traditional RAG is a reliable and cost-effective choice. However, for complex, data-driven tasks, agentic RAG's autonomy and adaptability offer significant advantages, making it ideal for industries with high-stakes applications..