Master RAG: Ultimate Retrieval-Augmented Generation Course

This module offers a deep dive into the RAG architecture, providing a step-by-step walkthrough of the RAG pipeline and its integration with LLMs using Langchain. Learn how to create a vector database, generate AI embeddings, and optimize the RAG process for enhanced system performance. With practical examples, you'll discover how to use RAG for information retrieval, including a basic retrieval Q&A example that shows how to collect information and respond to user queries using an Internet Data Source. This course is packed with simple and complex examples to help you advance your career in data science and machine learning. By the end, you'll be able to implement and execute a retrieval QA chain from start to finish and understand the use cases of RAG in developer tools like Hugging Face and Gen AI.

This lecture provides a comprehensive walkthrough of setting up a project on the OpenAI platform, configuring it with an OpenAI API key, and interacting with language models. Learn to use Visual Studio Code to open the starter project, create and activate a virtual environment, and install necessary packages and dependencies. Understand the importance of secret keys in Python projects and how to generate and store them safely. This course also introduces you to Langchain, a vector database for information retrieval in Generative AI. By the end of this lecture, you'll be equipped with the knowledge to run your own RAG pipeline, optimize your use of LLMs, and advance your career in data science and machine learning.

In this lecture, we integrate FAISS and LangChain to create an efficient vector storage system for semantic search. We start by setting up the environment variables and initializing the language model with OpenAI. We then explore how to interact with the Chat Completion API to create a conversational AI. The lecture also covers using the WebBaseLoader class to load content from a webpage and convert it into documents, a crucial step in allowing the language model to access external data. We then create a PromptTemplate, which structures the prompt and combines instructions with input variables. The lecture further explains how to create a Rack pipeline, which includes the retriever and generator components. The retriever creates embeddings from the documents, which are then stored in a Vex2Store for retrieval. The generator then uses these embeddings to generate responses based on the initial training dataset and external data. This lecture is a must for anyone interested in using LangChain, FAISS, and OpenAI to improve semantic search performance, retrieval augmented generation, and natural language processing. You'll also learn how to use embeddings and create text embeddings, which are essential skills in generative AI and efficient similarity search.

In this lecture, we integrate Facebook AI Similarity Search (FAISS) and LangChain to create an efficient semantic search system. We start by focusing on the Retriever component, where we create and load embeddings into a VectorStore using OpenAI embeddings. We then move on to the Generate component, creating a language model that answers user queries based on retrieved context.

Throughout the lecture, we'll use LangChain, a framework for developing applications powered by large language models (LLM), and FAISS, a library for efficient similarity search and clustering of dense vectors. We'll also use the Retrieval Augmented Generation (RAG) technique for content generation.

We'll walk you through creating embeddings, indexing them in a vector database, and using them for semantic search. We'll also show you how to use the LLM to answer questions based on a given user query and context.

By the end of this lecture, you'll clearly understand how to use embeddings, FAISS, and LangChain to create a semantic search system. You'll also learn how to leverage generative AI for various applications, such as creating a custom Q&A chatbot for technical and customer support.

In this lecture, we delve into natural language processing, focusing on Retrieval Augmented Generation (RAG) and its application using LangChain. We explore how RAG addresses the limitations of language models like GPT-3.5 Turbo, which cannot provide real-time updates or access information beyond their training datasets. We'll walk you through using LangChain to create text embeddings, store them in a vector database, and retrieve them for augmented content generation. You'll learn to use vector store for similarity search, enabling more accurate and contextually relevant responses. We'll also cover integrating external data sources into the language model through the retrieval module, enhancing the generation process. By the end of this lecture, you'll be well-versed in creating embeddings, using vector DB, and exploring vector stores using LangChain.

In this lecture, we delve into creating a custom Q&A chatbot using LangChain, focusing on integrating a Retriever and Generator (RAG) pipeline. We'll explore using a vector store and creating text embeddings to enhance the chatbot's knowledge about specific products and services. The lecture will guide you through loading documents, splitting them into manageable chunks, and creating and loading embeddings using ChromaDB, an AI-native open-source vector database. You'll learn how to structure prompts and initialize large language models (LLMs) to guide the chatbot's behavior. We'll also discuss the importance of creating a numerical representation of text (text embeddings) for efficient similarity search and retrieval in the vector store. By the end of this lecture, you'll clearly understand how to use LangChain to bridge the gap between large language models and custom knowledge, enhancing your chatbot's user experience.

Dive into the Retrieval Augmented Generation (RAG) world with this comprehensive lecture. We'll walk you through setting up an end-to-end RAG pipeline using LangChain and ChromaDB. You'll learn how to load documents, split them into chunks, and convert these chunks into vector representations or embeddings using OpenAI's language models. We'll then guide you on how to load these embeddings into a vector database (ChromaDB) for context retrieval. The lecture also covers the intricacies of the RAG architecture, including the two stages of indexing documents and retrieval and generation. You'll get hands-on experience with the LangChain tool, learning how to create a chain, specify context, and set up the generated component. We'll also delve into the role of the StringOutputParser and how to use the invoke method. The lecture concludes with a practical example of using the RAG pipeline to simulate a customer support chatbot. This lecture is perfect for anyone interested in AI, natural language processing, and advanced RAG techniques.

In this lecture, we explore creating an interactive example of a basic RAG (Retrieval Augmented Generation) pipeline using Google Colab and ChromaDB. We start by setting up the environment, installing dependencies, and adding your OpenAI API key for interaction with language models. The lecture then guides you through loading and splitting documents into smaller chunks, creating and storing embeddings, and understanding their role in similarity search. We then move on to defining prompts and testing the pipeline with and without RAG, highlighting the difference in the quality of responses. The lecture concludes with a practical example of a chatbot that can answer specific questions about a fictitious washing machine manufacturer's user manual. This lecture is a must for anyone interested in understanding the end-to-end RAG pipeline, LLM (Language Learning Models), and how to augment them using RAG for more context-specific responses.

Dive into the Retrieval Augmented Generation (RAG) world with this comprehensive lecture. Learn how to build an end-to-end RAG pipeline using LangChain and ChromaDB. The lecture begins by explaining the basic RAG workflow, which includes indexing, retrieval, and generation. You'll understand how text is converted into embeddings and stored in a vector database to create a searchable index. The lecture then moves on to the retrieval step, where a user's query is converted into an embedding to search the vector store for the most relevant text data. Finally, the generation step is covered, where the query is augmented with the retrieved documents to generate an answer. The lecture also delves into advanced RAG techniques, including data indexing with chunking optimization and query enhancements. You'll learn about multi-representation indexing, RAG fusion, and decomposition. The lecture also covers using large language models (LLMs) in the retrieval process and optimizing the generation process with techniques like multi-querying. Whether you're a software engineer or an AI enthusiast, this lecture will equip you with the knowledge to build a production-ready RAG pipeline.

Dive into the world of advanced Retrieval-Augmented Generation (RAG) techniques with this comprehensive lecture. Learn how to leverage large language models (LLMs) to improve question answering through query decomposition and rewriting. This lecture will guide you through breaking down user input into a set of sub-questions, rephrasing them to optimize the retrieval process, and using the power of language models to get a final answer.

Discover how to tackle poorly written queries lacking details and context by asking the language models to rewrite the query. Explore using LangChain documentation as a data source and understand how to set up the decomposition process.

The lecture will also cover implementing a function to generate sub-queries and creating a chain to decompose the original query. Learn how to format the answer from the language models into a string and convert this string into an array.

This lecture is necessary for anyone interested in advanced RAG, query rewriting, semantic search, and generative AI. By the end of this lecture, you'll be able to enhance the retrieval process, optimize the use of embeddings, and understand the role of the vector database in the retrieval process.

In this lecture, we delve into the intricacies of Retrieval-Augmented Generation (RAG), a powerful tool for enhancing AI systems. We'll explore how to leverage large language models (LLMs) to improve query translation and optimize the retrieval process. The lecture will guide you through creating LLM prompts, generating sub-questions, and using a vector store to convert blog data into document embeddings. We'll also demonstrate how to use the advanced RAG to generate sequence pairs of questions and answers, providing the LLM with more context for better results. This process involves using utility functions, the LangChain, and retrieving relevant information from a data source. By the end of this lecture, you'll clearly understand how to enhance AI systems with semantic search, query rewriting, and retrieval-augmented generation. This lecture is necessary for anyone interested in generative AI, natural language processing, and advanced query transformations.

In this lecture, we explore the advanced use of Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) to enhance query retrieval. We'll walk you through the process of using RAG to individually answer each sub-question in a query, leveraging the AI's language model to refine the context for each response. You'll learn how to define an empty array for RAG results, iterate over sub-questions, and retrieve relevant documents based on each sub-question. We'll also cover how to use pre-written prompts to instruct the language model and how to create an 'AnswerChain' to invoke the model with the question and context. The lecture will guide you through appending the results to an array and returning the RAG results and sub-questions for further optimization. This lecture is a must for anyone looking to enhance their understanding of advanced RAG, query translation, and the use of embeddings in semantic search. By the end, you'll be well-equipped to leverage LLMs and RAG for advanced query transformations, augmentation, and document retrieval in your AI use cases.

In this lecture, we delve into the world of AI and natural language processing, focusing on the advanced use of Retrieval-Augmented Generation (RAG) and query translation. We'll explore leveraging large language models (LLMs) and vector databases to enhance retrieval and optimize results. The lecture covers the step-by-step process of decomposing user queries, generating pairs of questions and answers, and using these pairs to create a context for the final prompt. We'll also discuss how to define prompts and chains and how to use the langchain utility function to format QA pairs. The lecture concludes with a practical demonstration of how to generate an optimized answer using the RAG and how to troubleshoot common issues. This lecture is necessary for anyone interested in advanced query transformations, semantic search, and generative AI. You'll also learn how these techniques can be applied in various use cases, including market research, brand positioning, and digital marketing.

Step up your AI and data science skills with this comprehensive lecture on advanced RAG (Retrieval-Augmented Generation) techniques. This module dives deep into the world of RAG, focusing on query transformation and RAG fusion to optimize your RAG models for real-world applications. Learn how to use the power of language models (LLM) to write better queries and enhance your retrieval strategies. The lecture also covers the use of LangChain for reciprocal rank fusion (RRF) and the application of RAG techniques like re-ranking with RAG Fusion. You'll also explore CRAG for corrective RAG and refinements, as well as the concept of multi-query. By the end of this lecture, you'll be well-versed in advanced RAG retrieval strategies, ready to generate multiple, optimized responses using generative AI.

In this lecture, we delve into the complexities of Retrieval-Augmented Generation (RAG) and how to enhance its efficiency using advanced RAG techniques. We explore the challenges of handling multiple queries and the issues of redundancy and document overlap. The lecture introduces the concept of multi-query RAG fusion. This improved solution leverages the power of AI and language models (LLMs) to generate multiple versions of a user query and retrieve relevant documents from a vector database.

We walk you through the step-by-step process of setting up a multi-query retriever using LangChain, from defining a multi-query prompt to generating an answer based on the new context. The lecture also covers indexing, loading, and storing documents in a vector store.

We further discuss the use of reciprocal rank fusion (RRF) and how it enhances the quality of the generated response. By the end of this lecture, you will have a comprehensive understanding of advanced RAG retrieval strategies, the role of generative AI in information retrieval, and how to use LangChain for efficient retrieval and response generation.

In this lecture, we delve into the world of advanced RAG (Retrieval-Augmented Generation) techniques, focusing on using LangChain and Multi-Query to enhance information retrieval. We will walk you through transforming user queries into multiple queries using AI and language models (LLMs). This process results in multiple sets of retrieved documents, each serving as a unique retriever until the results are consolidated.

We will also explore the use of RAG Fusion and Reciprocal Rank Fusion (RRF) to increase the likelihood of generating accurate and enhanced responses. The lecture will guide you through using the context and the original query to generate the final response using the LangChain tool.

You will learn how to improve retrieval robustness to variability in user input using techniques such as paraphrasing, semantic matching, and natural language understanding. We will also discuss the benefits of query rewriting and the multi-query approach, which allows for a better retrieval process and improved relevance with sets of documents retrieved for every query.

Dive into the world of advanced RAG techniques with this comprehensive lecture. We'll explore using RAG Fusion and Reciprocal Rank Fusion (RRF) to improve the quality of generated responses in AI systems. The lecture will guide you in using LangChain to create multiple optimized queries from a single user input. This technique helps to overcome the challenges of vague or context-lacking queries. We'll also delve into the retrieval-augmented generation (RAG) process, demonstrating how to re-rank results based on score values for more relevant outputs. You'll get hands-on experience with code examples and learn how to implement these techniques practically. By the end of this lecture, you'll have a solid understanding of advanced RAG retrieval strategies, the role of language models (LLMs) in generative AI, and how to enhance your AI agent's performance using these cutting-edge techniques.

In this lecture, we delve into the world of advanced RAG (Retrieval-Augmented Generation) techniques, focusing on using LangChain and Reciprocal Rank Fusion (RRF) to enhance information retrieval. We will walk you through generating a final RAG chain, defining the context, and specifying the user's question. You will learn to use tools like Runnable, Passthrough, and ItemGetter to retrieve values for specific keys. We will also demonstrate how to structure prompts for augmented content generation and send instructions to the LLM (Language Model). The lecture will guide you through parsing the answer with a string output parser and optimizing the final response. You will also get hands-on experience re-ranking search results using the RRF formula and generating the best and most optimized answer.

Dive into the world of advanced RAG techniques with this comprehensive lecture on Corrective Retrieval Augmented Generation (CRAG). This lecture will guide you through enhancing traditional RAG implementation with an extra layer of complexity, the Retrieval Evaluator. You'll learn how to assess the relationship between retrieved documents and a user query, a crucial step in improving the RAG process's robustness, accuracy, and efficiency.

The lecture will also cover the limitations of large language models (LLMs) and how RAG techniques can address these by expanding the model's knowledge with external data. You'll get hands-on experience with the Langchain package, OpenAI, and other dependencies, which we'll use for knowledge search.

We'll walk you through installation, setting up your API keys, and initializing the tool with table search results. You'll also learn how to use the lightweight retrieval evaluator to measure the correctness and relevance of a query and the retrieved documents.

By the end of this lecture, you'll be able to implement CRAG, evaluate the relevance of retrieved information, and trigger actions for knowledge refinement or knowledge search based on the evaluation results.

Dive into the world of advanced RAG (Retrieval Augmented Generation) techniques in this comprehensive lecture. We'll explore the process of using language models to retrieve and assess documents, focusing on the retrieval evaluator's role in determining the retrieved information's relevance. You'll learn how to use the corrective RAG (CRAG) technique to enhance the robustness of your AI systems and how to implement a lightweight retrieval evaluator (LLM) for efficient document assessment.

The lecture will guide you through setting up a chain of components, starting with the retrieval grader and using function calls to trigger actions based on decisions. We'll delve into assigning binary scores, transforming document sets into strings, and using the retriever to get relevant documents based on user queries.

You'll also get hands-on experience with the LangChain tool, which allows for the seamless integration of large language models (LLMs) into your workflow. By the end of this lecture, you'll have a solid understanding of using advanced RAG techniques for knowledge refinement and optimizing your queries for better retrieval search results.

In this lecture, we delve into the advanced RAG (Retrieval Augmented Generation) techniques, focusing on implementing CRAG (Corrective Retrieval Augmented Generation) for knowledge refinement. We start by defining the rewrite prompt and sending instructions to the language model to rephrase questions, optimizing the query for improved content generation. We then introduce the web search tool provided by Tavidi Search Results, explicitly designed for AI agents to search for complementary information from the web. The lecture also covers formatting the retrieved documents into a single string and creating a unique document. We discuss the evaluation process, where we check whether the retrieved information is relevant and the steps taken in each scenario. The lecture concludes with a demonstration of how to enhance the robustness of the retrieval process and improve content generation using these advanced RAG techniques.

Dive into the world of advanced RAG (Retrieval Augmented Generation) techniques with this comprehensive lecture. We'll explore the implementation of Corrective RAG, a plug-and-play system that refines retrieved information to optimize key insights extraction and minimize non-essentials. You'll learn how to use LangChain to write prompts, send instructions to the large language model (LLM), and generate answers in a string format. We'll also evaluate the relevance of retrieved documents and user queries using a lightweight retrieval evaluator. The lecture will guide you through rewriting user queries for knowledge refinement, running web searches, and generating answers based on the optimized queries. By the end of this lecture, you'll have a solid understanding of how to enhance the robustness of your AI systems with generative techniques like Corrective RAG. This lecture is perfect for those interested in AI, LLMs, and generative AI and looking forward to the future of these technologies in 2024 and beyond.

This lecture will guide you through preparing data for vector stores and language models, focusing on handling large volumes of data. You'll explore different levels of text splitting, from simple to complex, and discover how to integrate these strategies into the RAG pipeline to create an optimized system.

You'll learn about the Character Text Splitter and Recursive Character Text Splitter and the importance of chunking and chunk overlaps in creating vector representations of words. We'll also delve into the differences between these two methods and how to choose the best one based on your use case.

The lecture also covers managing different data types, such as code splitting and PDF documents, and extracting elements from a PDF document. You'll explore two experimental approaches that put semantics at the center stage of the document-splitting strategy, using Longchain and pseudocode.

By the end of this lecture, you'll have a comprehensive understanding of the RAG system, the importance of chunking, and how to choose the right chunking strategy for your needs. You'll also gain insights into the applications of the Language Model (LLM), the power of embeddings, and the role of the vector database in natural language processing and semantic search.

Dive into the world of advanced chunking techniques and strategies in this comprehensive lecture. Use Character Text Splitter, a level 1 chunking method, to split documents into smaller chunks effectively. Understand the importance of chunk size and overlap and how to optimize these parameters for efficient retrieval and semantic search. The lecture also introduces Longchain, a tool that simplifies the chunking process and helps create and load embeddings. You'll also explore using metadata to tag documents with unique, relevant attributes, enhancing search and retrieval. The lecture provides a practical use case, demonstrating the chunking process on a real document - a speech given by the U.S. president. You'll learn how to load the document using a text loader, define chunk size and overlap, and print the details of the chunking process. The lecture also delves into the advanced chunking techniques of recursive character text splitter, a level 2 chunking method. By the end of this lecture, you'll have a solid understanding of different chunking strategies, their applications in natural language processing, and how to choose the right chunking strategy for your RAG system.

Dive into the world of advanced chunking strategies with this comprehensive lecture. Learn how to optimize your retrieval using the Recursive Character Text Splitter, a sophisticated tool that addresses the limitations of basic text splitting. This lecture will guide you through dividing the text into smaller chunks based on multiple criteria, such as separators and text structure, including paragraphs, sentences, and words. You'll explore using LangChain and ChangViz, two tools that aid in semantic chunking and help optimize RAG performance. The lecture also covers the importance of choosing the right strategy and the benefits of implementing chunking in your natural language processing tasks. By the end of this lecture, you'll have a solid understanding of advanced chunking techniques, LLM applications, and how to effectively use a vector database for semantic search and question answering.

In this lecture, we delve into advanced chunking strategies and techniques, focusing on using Language Models (LLMs) and Retrieval Augmented Generation (RAG) for semantic search and retrieval. We explore different chunking methods, including document-specific splitting with Markdown, code splitting with Python, and handling data types like PDFs. The lecture provides a step-by-step guide on parsing and indexing content, loading it into a vector store, and using it for retrieval. We also discuss optimizing RAG performance by adjusting chunk size and overlap parameters. By the end of this lecture, you will have a solid understanding of how to implement effective chunking techniques, optimize your RAG system, and leverage LLM applications for semantic chunking.

Dive into advanced chunking strategies and techniques in this comprehensive lecture. We'll explore using a web-based loader to load a document about CodeSplitter and extract specific code snippets from a web page. You'll learn to use the language model (LLM) to process and extract specific code snippets for a particular programming language, focusing on Python and JavaScript.

We'll delve into using a recursive character text splitter to split content in a code format and create documents from it. You'll understand how to define and structure the prompt to send instructions to the language model and how to generate the output in a string format.

The lecture will also cover creating a retriever chain, passing context and programming language as parameters, and displaying the retrieved code snippets. You'll learn how to split the retrieved code, create a vector store to index and load these documents as embeddings, and create another retriever to return the most relevant results from a user query.

We'll also discuss how to use the retriever to generate answers to user queries and how to use the language model to explain the steps in natural language processing. You'll understand the importance of chunking, the different methods, and how to choose the right chunking strategy for optimal RAG performance.

This lecture will guide you through transforming text into smaller, more manageable chunks, using the unstructured.io library to extract elements from PDF documents and prepare them for the RAG pipeline. You'll also learn how to create vector representations of these documents, a key step in the retrieval process. The lecture will also cover using the LLM (Language Model) and choosing the right chunking strategy for different use cases. By the end of this lecture, you'll have a solid understanding of how to implement advanced chunking techniques to optimize your RAG system and improve the effectiveness of your retrieval process.

Dive into the world of advanced chunking techniques in this comprehensive lecture. We'll start by exploring the utility function used to convert elements into documents for ingestion into the vector store. You'll learn how to transform a two-dimensional array into a flattened list of elements, a crucial step in the RAG (Retrieval-Augmented Generation) process. We'll then delve into converting these elements into documents, making them available for further processing.

This lecture will guide you through processing PDF documents, extracting elements, and integrating them into the RAG pipeline. You'll gain hands-on experience with Python scripts such as process_documents.py and main.py and learn how to load documents into a vector store, create embeddings, and generate responses to specific queries.

We'll also discuss the importance of chunking in optimizing RAG performance and how different chunking strategies, including semantic chunking, can enhance the retrieval of relevant information. You'll understand how to choose the right chunking strategy and the role of chunk size in effective chunking.

By the end of this lecture, you'll have a solid understanding of advanced chunking techniques, LLM (Language Model) applications, and how to optimize your RAG system for semantic search and question-answering tasks.

In this lecture, we dive into the world of advanced chunking strategies, exploring the power of semantic chunking in optimizing the Retrieval-Augmented Generation (RAG) system. We'll delve into the use of embeddings and vector databases to smartly divide documents into smaller chunks based on semantic similarity, moving beyond the limitations of fixed-size chunking.

You'll learn how to leverage the Long Language Model (LLM) applications to create relationships between texts, enhancing the retrieval process and improving RAG performance. We'll also discuss different chunking methods, including percentile, standard deviations, interquartile, and gradients, and how they influence text splitting into smaller, more manageable chunks.

This lecture will guide you through loading example data, creating a semantic chunker, and defining breakpoints to split text effectively. We'll compare traditional chunking techniques with the experimental approach of semantic chunking, demonstrating how the latter can yield more contextually relevant information.

By the end of this lecture, you'll understand the importance of chunking in Natural Language Processing and how choosing the right strategy can significantly enhance your RAG pipeline.

This lecture will walk you through setting up a Langsmith account, creating a project, and configuring your API key. You'll learn how to trace and evaluate the execution of your chains, ensuring the highest quality for your LLM application development.

We'll also provide a hands-on example to help you get up to speed quickly with Langsmith and Langchain. You'll discover critical features of Langsmith, including its user-friendly interface and real-time tracing capabilities.

Whether working with datasets, managing an annotation queue, or exploring natural language processing, Langsmith offers the necessary tools and technologies.

Dive into the world of LLM application development with our comprehensive guide on using Langsmith, a vital feature of the Langchain framework. This lecture provides a step-by-step walkthrough on tracing and monitoring every step of your LLM-powered application's lifecycle, from indexing and retrieving documents to content generation. Learn how to set up Langsmith, integrate it into your projects, and use an API key to trace the execution of your application. We'll also cover how to use the Langsmith platform for real-time tracing and debugging of LLMS, ensuring you can effectively manage latency and workflow. You'll get hands-on experience with the Langsmith Python SDK, learning how to run evaluations, annotate your dataset, and understand the metadata. By the end of this lecture, you'll have a solid understanding of using Langsmith for LLM application development, enhancing your skills in natural language processing and LLM development.

This lecture will guide you through setting up an account on LangSmith, creating a project, and installing the necessary packages. You'll learn how to use the LangSmith platform to run an LLM with a chat model in a chain and how to trace and report this tracing to the project. We'll also cover adding credentials to your project, replacing your API key, and executing a basic regular chain. You'll gain hands-on experience with LangChain, a key feature of LangSmith, and understand how to use it to retrieve documents, send instructions to language models, and return generated outputs in a string format. The lecture also includes a detailed debugging and testing LLMS walkthrough, focusing on tracing and debugging.

By the end of this lecture, you'll be well-versed in LLM application development, from setting up your project to testing and debugging your LLMS.

This lecture will walk you through creating stateful multi-agent workflows and applications with large language models (LLMs).

You'll learn how to leverage the Corrective Retrieval Augmented Generation (CRAP) technique to make your RAG pipeline flexible and responsive. We'll demonstrate how to use LangGraph to build a graph that coordinates multiple AI agents and actions and how to use LangChain to call upon language models for writing and optimizing queries.

You'll also learn how to integrate the Tableau Search API to trigger actions and search the web for additional information. The lecture will cover the process of adding a retriever to provide context, defining a prompt to send instructions to the language models, and generating a response.

Dive into the world of Retrieval-Augmented Generation (RAG) and AI automation with this comprehensive lecture. We'll explore the LangGraph library, a key component of the LangChain framework, and its application in building stateful multi-agent applications using large language models (LLMs). You'll learn how to create and coordinate multiple AI agents with different roles and capabilities within your app, using workflows as graphs to control flow and state. We'll delve into key features such as cycles and branching and how to set up steps (edges) to organize every node (action) in your application. You'll also discover how to use optional persistence to save every executed step within the graph.

This lecture will guide you through the integration of LangGraph with other LangChain libraries to set up and run chains, including using runnable as part of the nodes in the workflow. We'll also explore the use of the OpenAI API and the Tavli API tool for web search functionality, expanding the capabilities of your language model.

You'll get hands-on experience with defining steps (edges) in the workflow, using the Agentic RAG system, and monitoring the execution of your application with LongSmith. We'll also cover the use of .env for loading environment variables and the initialization of the model.

By the end of this lecture, you'll have a solid understanding of building Agentic RAG systems, creating an Agentic workflow, and exploring AI automation using LangGraph and LangChain.

This lecture provides a detailed walkthrough of the Agentic workflow, demonstrating how to use AI agents and LlamaIndex to handle queries and retrieve data. You'll learn how to use the RAG system to refine document retrieval processes and generate responses using Large Language Models (LLMs).

The lecture also covers the use of advanced RAG techniques, including Corrective RAG, to enhance the retrieval process's accuracy. These techniques are applied in a real-world scenario where the AI system fetches current weather data.

We'll also explore using Streamlit to create user interfaces, allowing for interactive engagement with the AI system. By the end of this lecture, you'll have a solid understanding of how to build Agentic RAG systems, the Retriever and the Language Model role in the RAG pipeline, and how to use LangGraph for workflow coordination.

In this lecture, we delve into the intricate process of building Agentic RAG (Retrieval-Augmented Generation) systems using LangGraph and LangChain. We start by defining an entry point and nodes, which act as functions to execute actions in the workflow. We then explore the process of assessing the relevance between retrieved documents and user queries, using the RAG system for grading.

The lecture discusses the grader's role in returning a binary score, which determines the next action in the workflow. We also cover the process of searching the web for additional information and rewriting nodes. The final step involves generating a response after rewriting the query and searching the web.

Throughout the lecture, we use tools like the OpenAI API and the LlamaIndex for indexing and retrieval. We also discuss the LangChain Expression Language and the power of large language models (LLMs) in optimizing and rephrasing queries.

The lecture provides a step-by-step guide on building the graph state, defining nodes, updating states, and defining edges to coordinate workflows between nodes. It also covers advanced RAG techniques and explores AI automation in the context of Agentic workflows.

This lecture will guide you through defining edges in your workflow, a crucial step in determining which sequence to execute. You'll understand the concept of a conditional edge and how it functions like a switch on a train track, directing the flow of your AI agent's actions. We'll explore the use of nodes, specifically the 'web search node' and 'rewrite query', and how they perform actions within your workflow. You'll also learn to avoid confusion between states and nodes in your RAG system.

The lecture will then move on to the practical steps of building your workflow, adding nodes, constructing the graph, and finally compiling your application. You'll get hands-on experience with the LlamaIndex and other RAG techniques and understand how they fit into the larger picture of retrieval-augmented generation.

This lecture will guide you through defining nodes and building a graph using the LangGraph and LlamaIndex tools. You'll learn how to use the AddNode method, set an entry point, and add edges to your workflow. We'll also cover creating documents, rewriting queries, and searching the web using the WebSearchNode.

You'll gain hands-on experience with the RAG system, a key component of AI agent and retriever technologies. Based on GradeDocuments' evaluation, we'll also explore the use of conditional edges and how to decide when to search the web.

The lecture concludes with a detailed walkthrough of compiling the workflow and generating an answer.

This lecture will guide you through creating an AI agent that can handle complex queries, retrieve relevant documents, and generate responses.

You'll learn how to construct a workflow in LangGraph, a powerful tool for building AI systems, and how to use LlamaIndex for document retrieval. We'll also discuss the role of Large Language Models (LLMs) in the RAG system and how they contribute to generating responses. The lecture will walk you through building an Agentic RAG system, from defining the graph and nodes to executing and compiling the workflow. You'll also get hands-on experience with debugging and correcting errors in your code.

By the end of this lecture, you'll have a solid understanding of RAG techniques, the role of AI in retrieval strategies, and how to use LangChain Expression Language in your AI projects.

This lecture will guide you through creating an AI agent that can handle complex queries, retrieve relevant documents, and generate responses.

You'll learn how to construct a workflow in LangGraph, a powerful tool for building AI systems, and how to use LlamaIndex for document retrieval. We'll also discuss the role of Large Language Models (LLMs) in the RAG system and how they contribute to generating responses. The lecture will walk you through building an Agentic RAG system, from defining the graph and nodes to executing and compiling the workflow. You'll also get hands-on experience with debugging and correcting errors in your code.

By the end of this lecture, you'll have a solid understanding of RAG techniques, the role of AI in retrieval strategies, and how to use LangChain Expression Language in your AI projects.

In this lecture, we delve into AI and unstructured data, focusing on the Retrieval-Augmented Generation (RAG) system and GPT-4. We will guide you through extracting and processing images from PDF documents using the unstructured.io tool.

This lecture will cover integrating images into your pipeline, converting them into Base64, and leveraging them to create vector representations. We will also demonstrate how to use the Large Language Model (LLM) and GPT-4 Vision provided by OpenAI to describe images. You will learn how to optimize your RAG application by augmenting your vector database with these image descriptions. This lecture will also discuss best practices for handling unstructured data and the benefits of using the RAG system for semantic retrieval.

By the end of this lecture, you will have a solid understanding of integrating, chunking, and processing unstructured data sources using advanced RAG and GPT-4 techniques in your AI pipeline.

In this lecture, we delve into using AI and GPT-4 to enhance Retrieval-Augmented Generation (RAG) in unstructured datasets.

We'll walk you through processing PDF documents, extracting images, and describing these images using Vision. You'll learn how to integrate the Large Language Model (LLM) into your pipeline and optimize its use for image description. We'll also cover the best practices for splitting and chunking strategies in handling unstructured data.

By the end of this lecture, you'll have a solid understanding of how to leverage AI and GPT-4 in your RAG system, augment your data sources, and generate responses from AI.

Dive into AI and unstructured data as we explore the enhanced RAG (Retrieval-Augmented Generation) system in this lecture. Learn to integrate and optimize the RAG pipeline using large language models (LLMs) like GPT-4.

We'll walk you through managing different document types, extracting elements from PDFs, and registering them into a VectorStore. Discover how to leverage the power of AI to describe images and include these descriptions in your dataset, augmenting your RAG application. We'll also cover best practices for chunking strategies and how to handle unstructured data sources.

By the end of this lecture, you'll have a solid understanding of the retrieval process, how to use APIs and frameworks to enhance your RAG system, and how to leverage semantic transformer models for improved results.