Name: AI Concepts: Embeddings, Vector Stores, Traditional RAG, and AI Agents
Uploaded: 2025-01-02
Duration: 13 min 22 s
Description: 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] 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.. 

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

[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 They cannot access new information once they've been trained, leaving them with outdated knowledge and unable to provide real-time or up-to-date information.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.. 

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Limitations of Large Language Models Before RAG :

Large Language Models (LLMs) have impressive capabilities, but they come with several key limitations: Outdated Knowledge: LLMs can’t access new information after they’re trained. They rely only on the data they were trained with, so they can’t provide real-time or up-to-date information. Factual Mistakes: LLMs can generate fluent text but sometimes give incorrect or misleading answers, especially on less common or specialized topics. Hallucination Problem: LLMs sometimes “hallucinate,” meaning they confidently generate information that sounds reasonable but is false. This happens because they rely only on patterns from their training data rather than real-time information. No Access to External Information: LLMs can’t look up answers from external sources, like the internet or a database, which limits their ability to provide specific or accurate details on certain 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.. 

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Retrieval-Augmented Generation (RAG) helps solve many of these problems by allowing LLMs to fetch relevant information from external sources. Instead of relying solely on their internal knowledge, RAG models can search databases or documents for real-time, up-to-date information, which helps reduce hallucinations and improves the accuracy of generated content. By integrating retrieval, RAG ensures that the model generates more reliable, fact-based answers, even for specialized or 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.. 

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Understanding Information Retrieval Before RAG:

Before diving into Retrieval-Augmented Generation (RAG), it’s crucial to understand Information Retrieval (IR), which plays a foundational role. As the name suggests, information retrieval is about finding and extracting relevant data from large datasets. Think of it like this: when we were kids, we used to answer questions based on a given paragraph. We didn’t use the entire paragraph; instead, we picked the exact information needed to answer the question. This is the essence of IR — retrieving only the relevant information from massive collections, which could be text, images, audio, or even video

 The process of IR generally consists of a few key steps, the first of which is indexing. Indexing involves converting external data sources into numerical representations, making it easier to search through large datasets. For example, if you’re looking for information on the “ICC Cricket World Cup 2023,” IR systems will scan through the database and rank all related documents, prioritizing the ones that contain the most relevant information.

[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.. 

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Hybrid Framework Overview: Retrieval-Augmented Generation (RAG) merges retrieval and generative processes, allowing Large Language Models (LLMs) to access external knowledge bases. This integration enhances the model's ability to produce accurate, contextually relevant responses by leveraging real-time information rather than relying solely on pre-trained data.

 Dynamic Interaction Mechanism: RAG transforms user interactions with LLMs by enabling real-time retrieval of pertinent information. This dynamic approach not only improves the accuracy of generated outputs but also aligns closely with user intent, ensuring that responses are informed by the most current and relevant data available.

[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.. 

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[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.. 

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This phase is responsible for fetching relevant information from an external knowledge base, database, or document repository. The process begins with a query, usually derived from the user’s input or a given prompt.

 Embedding Model: The input query is first converted into vector embeddings using an embedding model. This model maps the input into a numerical form that can be used for similarity searches.

 Vector Database: Once the query is embedded, it is sent to a Vector DB, which contains embeddings of documents, text data, or any relevant external information. This database is indexed based on vector similarity (cosine similarity is often used).

 Retriever & Ranker: A retriever component then selects the top N documents or relevant data points based on similarity. These documents are ranked in order of relevance, typically using semantic search or other retrieval algorithms like sparse or dense retrieval methods.

[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.. 

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Understanding Embeddings and Vector Stores

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[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.. 

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Underneath all machine learning, there’s large arrays of numbers. That’s how neural networks represent inputs and weights. Large language models deal with text, so there needs to be some mapping of language to numbers. We would also expect some notion of similarity — the numerical representation of synonyms should be numbers close in value. Large language models encode semantic meaning through this closeness of values.

[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.. 

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How can machine read and understand texts?

The vectors for the words “King” and “Man” may be similar, as might the vectors for “Queen” and “Woman.” These vectors also have certain properties that can be useful for training language models. For example, you can subtract the vector for “Man” from the vector for “King” and add the vector for “Woman” to get the vector for “Queen.” These properties allow the model to understand the different meanings of words. However, how does an NLP model determine which words are more like each other? This is because NLP models do not use a single number to represent a word. In fact, they often use more than 1,000 numbers to represent a single word. This is why it is called a “word vector.” In mathematical terms, a single number is called a scalar, and a list of numbers is called a vector. A word vector can be thought of as a point in a multi-dimensional space, where each dimension represents a particular aspect or characteristic of the word. The number of dimensions in a word vector is often called “dimensionality.” A high-dimensional word vector would have many dimensions, allowing it to capture a wide range of characteristics and nuances of the word. OpenAI Embeddings models boasts 1536 dimensions, while Cohere Embeddings models can range from 382 to 4096 dimensions. The Elasticsearch dense_vector field type supports up to 4096 dimensions as of the latest release.

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[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.. 

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[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.. 

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With thousands or even millions of embeddings to manage, storing and searching these vectors efficiently becomes crucial. Locality Sensitive Hashing (LSH) : LSH is a technique that partitions vectors into “buckets” based on their similarity. When a search query is performed, it is hashed into one of these buckets, significantly reducing the number of comparisons needed. Instead of comparing the query vector with every stored vector, you only need to compare it with those in the same bucket. This method accelerates search times and enhances efficiency. Vector databases offer two primary benefits: Faster Searches: By employing techniques like LSH, they can rapidly locate relevant vectors. Optimal Storage: They are designed to handle the unique requirements of vector data, ensuring efficient storage and retrieval. As vector databases continue to evolve, they are becoming increasingly essential for applications involving semantic search, recommendation systems, and more.

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[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.. 

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In this phase, the retrieved information is used to provide additional context to the query or prompt, enhancing the model’s understanding of the task.

 Retrieved Context: The top N documents fetched in the retrieval stage are passed back to the model as retrieved context. This information is appended or "augmented" with the original user query to provide additional details and improve the relevance and accuracy of the response.

 The goal here is to leverage both the external knowledge base and 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.. 

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The final stage is responsible for generating the actual output, which combines the original prompt/query with the augmented data from the retrieval phase.

 LLMs (Large Language Models): The augmented prompt, along with the retrieved context, is passed to the LLMs (e.g., GPT, BERT, or any transformer-based model). The LLMs processes the input and generates a response that is more context-aware and accurate, thanks to the extra information it received from the retrieval phase.

 Formatted Response: The output is returned as a formatted response, usually displayed in the user interface. The user query is enriched with additional, often domain-specific or up-to-date, information, addressing some of the inherent limitations of standard 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.. 

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[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.. 

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Limitations of Traditional RAG Approaches

Accuracy and Relevance: Ensuring that the retrieved and generated content is accurate, relevant, and directly addresses the user’s query.

 Contextual Understanding: RAG systems must understand the context and intent behind each query, especially when handling ambiguous or multi-part questions. But sometimes, they lag in this area! Synthesis and Reasoning: Beyond retrieving relevant information, the system must synthesize data from multiple sources and generate coherent, actionable insights. Customization: Adhering to specific internal style guides or user-defined preferences adds another layer of complexity. Scalability:  Managing a large volume of diverse queries across different domains or topics can strain the system’s ability to provide high-quality responses.

[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.. 

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Google Colab Notebook: RAG using KDB.ai vector

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[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.. 

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Agentic RAG refers to a more intelligent and dynamic Retrieval-Augmented Generation system where an “agent” plays a key role in orchestrating processes. The agent intelligently determines which resources or databases are most relevant for a user’s query, making it capable of handling more complex, multi-tasking scenarios. It is an evolution from traditional RAG systems, offering greater adaptability and decision-making by incorporating additional logic or heuristics into the retrieval and response generation pipeline. Agentic: The system works on its own, making decisions and taking actions depending on the situation. RAG (Retrieval-Augmented Generation): It mixes information from a knowledge base with the AI’s ability to create responses.

[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.. 

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[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.. 

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[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.. 

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[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. 

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[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.. 

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Choosing Between Traditional and Agentic RAG

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. For complex, data-driven tasks: Agentic RAG’s autonomy and adaptability offer significant advantages, making it ideal for industries with high-stakes applications. Traditional RAG is often preferred in settings where tasks are simple and predictable. Customer Support: Traditional RAG offers consistent, contextual answers ideal for large-scale customer service. FAQ and Educational Tools: Perfect for static information retrieval where users expect quick, accurate responses. Agentic RAG, on the other hand, thrives in complex environments that require adaptability. Scientific Research: Enables researchers to analyze vast datasets, synthesize information, and draw insights quickly. Smart Cities: Manages urban data, like traffic and resource allocation, to improve efficiency and real-time responsiveness.

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


Welcome to our presentation on (A-I ) 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.

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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.

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.

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 I-C-C Cricket World Cup 2023, IR systems would scan the database, ranking documents based on how relevant they are.

Retrieval Augmented Generation, or R-A-G--, 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, R-A-G integrates dynamic interaction mechanisms that improve the accuracy of generated outputs and align them with user intent.

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.

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.

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.

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.

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.

 The vectors for the words “King” and “Man” may be similar, as might the vectors for “Queen” and “Woman.” These vectors also have certain properties that can be useful for training language models. For example, you can subtract the vector for “Man” from the vector for “King” and add the vector for “Woman” to get the vector for “Queen.” These properties allow the model to understand the different meanings of words. However, how does an NLP model determine which words are more like each other? This is because NLP models do not use a single number to represent a word. In fact, they often use more than 1,000 numbers to represent a single word. This is why it is called a “word vector.” In mathematical terms, a single number is called a scalar, and a list of numbers is called a vector. A word vector can be thought of as a point in a multi-dimensional space, where each dimension represents a particular aspect or characteristic of the word. The number of dimensions in a word vector is often called “dimensionality.” A high-dimensional word vector would have many dimensions, allowing it to capture a wide range of characteristics and nuances of the word. OpenAI Embeddings models boasts 1536 dimensions, while Cohere Embeddings models can range from 382 to 4096 dimensions. The Elasticsearch dense_vector field type supports up to 4096 dimensions as of the latest release.


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Embeddings are numerical representations of words or phrases that help (A-I ) models understand language. Vector stores are databases of these embeddings, where they are stored for efficient access. Traditional R-A-G refers to the process of gathering information from various sources, combining it, and generating a response. (A-I ) 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.

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.

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.

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 G-P-T--, 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.

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 R-A-G model, which learns to recognize patterns and connections within the data. Once trained, the R-A-G model is deployed to generate insights and predictions based on new, unseen data.

R-A-G 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.

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 R-A-G model utilizing KDB.ai vectors. The code snippet showcases the implementation of the R-A-G algorithm, encompassing the computation of similarity scores between input queries and stored vectors. This code serves as a foundation for constructing a practical R-A-G application.

 Google Colab Notebook: RAG using KDB.ai vector

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Agentic R-A-G 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 R-A-G--, this system can work independently, making decisions and taking actions accordingly.

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.

In this example use case, agentic R-A-G 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.

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 (A-I ) agents. We can use this tool to explore new possibilities for (A-I ) applications and develop innovative solutions.

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

Selecting the right R-A-G system depends on the business's specific requirements. For straightforward, low risk tasks, traditional R-A-G 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.