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Free Download Gen AI - LLM RAG Two in One - LangChain + LlamaIndex Published 10/2024 Created by Manas Dasgupta MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 27 Lectures ( 9h 13m ) | Size: 4.44 GB Gen AI - Learn to develop RAG Applications using LangChain an LlamaIndex Frameworks using LLMs and Vector Databases What you'll learn Be able to develop your own RAG Applications using either LangChain or LlamaIndex Be able to use Vector Databases effectively within your RAG Applications Craft Effective Prompts for your RAG Application Create Agents and Tools as parts of your RAG Applications Create RAG Conversational Bots Perform Tracing for your RAG Applications using LangGraph Requirements Python Programming Knowledge Description This course leverages the power of both LangChain and LlamaIndex frameworks, along with OpenAI GPT and Google Gemini APIs, and Vector Databases like ChromaDB and Pinecone. It is designed to provide you with a comprehensive understanding of building advanced LLM RAG applications through in-depth conceptual learning and hands-on sessions. The course covers essential aspects of LLM RAG apps, exploring components from both frameworks such as Agents, Tools, Chains, Memory, QueryPipelines, Retrievers, and Query Engines in a clear and concise manner. You'll also delve into Language Embeddings and Vector Databases, enabling you to develop efficient semantic search and similarity-based RAG applications. Additionally, the course covers various Prompt Engineering techniques to enhance the efficiency of your RAG applications.List of Projects/Hands-on included: Develop a Conversational Memory Chatbot using downloaded web data and Vector DBCreate a CV Upload and Semantic CV Search App Invoice Extraction RAG AppCreate a Structured Data Analytics App that uses Natural Language Queries ReAct Agent: Create a Calculator App using a ReAct Agent and ToolsDocument Agent with Dynamic Tools: Create multiple QueryEngineTools dynamically and orchestrate queries through AgentsSequential Query Pipeline: Create Simple Sequential Query PipelinesDAG Pipeline: Develop complex DAG PipelinesDataframe Pipeline: Develop complex Dataframe Analysis Pipelines with Pandas Output Parser and Response SynthesizerWorking with SQL Databases: Develop SQL Database ingestion BotThis twin-framework approach will provide you with a broader perspective on RAG development, allowing you to leverage the strengths of both LangChain and LlamaIndex in your projects. Who this course is for Software Developers, Data Scientists, ML Engineers, DevOps Engineers, Support Engineers, Test / QA Engineers Homepage https://www.udemy.com/course/llm-rag-langchain-llamaindex/ Screenshot Rapidgator https://rg.to/file/7230881180fe2e920d73cb67f10d58f5/bcoul.Gen.AI..LLM.RAG.Two.in.One..LangChain..LlamaIndex.part2.rar.html https://rg.to/file/7b692f6bb84c20b1ae035f2a1a779a01/bcoul.Gen.AI..LLM.RAG.Two.in.One..LangChain..LlamaIndex.part4.rar.html https://rg.to/file/8462791b936870fe70b703ba287bf8e7/bcoul.Gen.AI..LLM.RAG.Two.in.One..LangChain..LlamaIndex.part1.rar.html https://rg.to/file/e43c085f370c303e6c476ae920466a80/bcoul.Gen.AI..LLM.RAG.Two.in.One..LangChain..LlamaIndex.part5.rar.html https://rg.to/file/fc2f130578d5c0ff7724b6095f427297/bcoul.Gen.AI..LLM.RAG.Two.in.One..LangChain..LlamaIndex.part3.rar.html Fikper Free Download https://fikper.com/0MdfBVuUR1/bcoul.Gen.AI..LLM.RAG.Two.in.One..LangChain..LlamaIndex.part1.rar.html https://fikper.com/9vG7HFRHEl/bcoul.Gen.AI..LLM.RAG.Two.in.One..LangChain..LlamaIndex.part2.rar.html https://fikper.com/DlsgBhYalk/bcoul.Gen.AI..LLM.RAG.Two.in.One..LangChain..LlamaIndex.part3.rar.html https://fikper.com/QCriLU0toN/bcoul.Gen.AI..LLM.RAG.Two.in.One..LangChain..LlamaIndex.part4.rar.html https://fikper.com/w5pOwFLOz3/bcoul.Gen.AI..LLM.RAG.Two.in.One..LangChain..LlamaIndex.part5.rar.html No Password - Links are Interchangeable

Free Download E-Commerce Product Recommendation RAG Systems Published 10/2024 Created by Ahmad Varasteh MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 7 Lectures ( 38m ) | Size: 278 MB Get hands-on with a practical Generative AI course. What you'll learn Use OpenAI APIs to perform context-based searches efficiently and effectively. Prepare vector data for a Retrieval-Augmented Generation (RAG) system using OpenAI's text embedding models. Implement cosine similarity to enhance recommendation systems by identifying and understanding data relationships and patterns. Craft context-reach prompt for a user-friendly product recommendation system Requirements Basic understanding of data analysis concepts Fundamental understanding of Python programming for data analysis and API interaction Access to OpenAI API Key Description By the end of this project, you will be equipped to perform context-based searches using Retrieval-Augmented Generation (RAG) systems and the OpenAI API, as well as develop a personalized recommendation system. You've been hired by ShopVista, a leading e-commerce platform offering products ranging from electronics to home goods. Your goal is to improve the platform's product recommendation system by creating a context-driven search feature that delivers tailored suggestions based on users' search phrases. You'll work with a dataset of product titles, descriptions, and identifiers to build a recommendation system that enhances the shopping experience.Learning Objectives:Prepare vector data for a Retrieval-Augmented Generation (RAG) system using OpenAI's text embedding models.Implement cosine similarity to identify and understand data relationships and patterns, improving recommendation systems.Utilize OpenAI APIs to perform efficient and effective context-based searches.Design and develop context-rich prompts for a user-friendly product recommendation system.This project will provide you with a comprehensive understanding of AI-powered search and recommendation systems, enabling you to grasp how cutting-edge technologies such as Retrieval-Augmented Generation (RAG) and OpenAI's models can be applied to solve real-world challenges. As you work through the project, you'll learn how to prepare and manage large datasets, leverage advanced text embedding techniques, and use AI to improve user interactions with e-commerce platforms.By implementing context-based searches and personalized recommendation features, you'll enhance your technical capabilities in areas such as natural language processing, vector-based data retrieval, and algorithm development. Furthermore, the practical experience gained from building a recommendation system for a leading e-commerce platform like ShopVista will deepen your problem-solving skills, allowing you to address complex customer needs with AI-driven solutions. This hands-on experience will not only strengthen your expertise in the e-commerce domain but also broaden your ability to design user-centric applications that deliver personalized, relevant, and intuitive experiences. Who this course is for Data scientists who are looking for more hands-on practice with RAG systems and Generative AI. Homepage https://www.udemy.com/course/e-commerce-product-recommendation-rag-systems/ Screenshot Rapidgator https://rg.to/file/d1634c304c58cb20a8d34959a1250a6f/mujpl.ECommerce.Product.Recommendation..RAG.Systems.rar.html Fikper Free Download https://fikper.com/WqbwFNnuSA/mujpl.ECommerce.Product.Recommendation..RAG.Systems.rar.html No Password - Links are Interchangeable

Free Download Advanced RAG Applications with Vector Databases Released 10/2024 With Yujian Tang MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill level: Advanced | Genre: eLearning | Language: English + subtitle | Duration: 1h 18m 15s | Size: 148 MB Discover cutting-edge methods to perform retrieval-augmented generation (RAG) with a vector database. Course details Retrieval-augmented generation (RAG) is everywhere these days, and vector databases are what give them their power. But RAG isn't as simple as some companies claim, so it can be easy to get overwhelmed. In this course, discover state-of-the-art RAG methods, including how to optimize text-based RAG via chunking, embedding, and metadata usage, and how to conduct basic image search with a vector database. You'll also get a chance to practice multimodal RAG by embedding and storing data and querying images with text. Along the way, instructor Yujian Tang provides practical, hands-on demonstrations and exercise challenges to test out your new skills. Homepage https://www.linkedin.com/learning/advanced-rag-applications-with-vector-databases Screenshot Rapidgator https://rg.to/file/6fdc7c82f9e5b7e4dacdc38199a8b65a/khltl.Advanced.RAG.Applications.with.Vector.Databases.rar.html Fikper Free Download https://fikper.com/g1b5CSviyM/khltl.Advanced.RAG.Applications.with.Vector.Databases.rar.html No Password - Links are Interchangeable

Free Download Multimodal RAG - AI Search & Recommender Systems with GPT-4 Published 9/2024 Created by Paulo Dichone | Software Engineer, AWS Cloud Practitioner & Instructor MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 22 Lectures ( 1h 31m ) | Size: 1.2 GB Mastering Multimodal RAG: Build AI-Powered Search & Recommender Systems with GPT-4, CLIP, and ChromaDB What you'll learn: Understand and implement Retrieval-Augmented Generation (RAG) with multimodal data (text, images). Build AI-powered search and recommender systems using GPT-4, CLIP, and ChromaDB. Generate and utilize text and image embeddings to perform multimodal searches. Develop interactive applications with Streamlit to handle user queries and provide AI-driven recommendations Requirements: Basic understanding of Python programming. Familiarity with machine learning concepts (embeddings, vectors). No prior experience with multimodal systems is needed, but knowledge of AI tools like GPT or CLIP will be helpful. A computer with internet access and the ability to install Python libraries like Streamlit, OpenAI, and ChromaDB. Description: Are you ready to dive into the cutting-edge world of AI-powered search and recommender systems? This course will guide you through the process of building Multimodal Retrieval-Augmented Generation (RAG) systems that combine text and image data for advanced information retrieval and recommendations.In this hands-on course, you'll learn how to leverage state-of-the-art tools such as GPT-4, CLIP, and ChromaDB to build AI systems capable of processing multimodal data-enhancing traditional search methods with the power of machine learning and embeddings.What You'll Learn:Master Multimodal RAG: Understand the concept of Retrieval-Augmented Generation (RAG) and how to implement it for both text and image-based data.Build AI-Powered Search & Recommendation Systems: Learn how to construct search engines and recommender systems that can handle multimodal queries, using powerful AI models like GPT-4 and CLIP.Utilize Embeddings for Cross-Modal Search: Gain practical experience generating and using embeddings to enable search and recommendations based on text or image input.Develop Interactive Applications with Streamlit: Create user-friendly applications that allow real-time querying and recommendations based on user-provided text or image data.Key Technologies You'll Work With:GPT-4: A cutting-edge language model that powers the AI-driven recommendations.CLIP: An advanced AI model for generating image and text embeddings, making it possible to search images with text.ChromaDB: A high-performance vector database that enables fast and efficient querying for multimodal embeddings.Streamlit: A simple yet powerful framework for building interactive web applications.No prior experience with multimodal systems? No problem!This course is designed to make advanced AI concepts accessible, with detailed, step-by-step instructions that guide you through each process-from generating embeddings to building complete AI systems. Basic Python knowledge and a curiosity for AI are all you need to get started.Enroll today and take your AI development skills to the next level by mastering the art of multimodal RAG systems! Who this course is for: Aspiring AI Developers: Individuals looking to build AI-powered applications that integrate text and image data. Data Scientists: Professionals aiming to enhance their skills in multimodal data processing and retrieval. Machine Learning Engineers: Those seeking to implement advanced search and recommender systems using state-of-the-art models. Homepage https://anonymz.com/https://www.udemy.com/course/multimodal-rag/ Rapidgator https://rg.to/file/089f61804f8d40592dbf48b60b41a26c/vglnz.Multimodal.RAG.AI.Search..Recommender.Systems.with.GPT4.part1.rar.html https://rg.to/file/6370fb9e607e28fdc88206885992158e/vglnz.Multimodal.RAG.AI.Search..Recommender.Systems.with.GPT4.part2.rar.html Fikper Free Download https://fikper.com/XnSbtleiyl/vglnz.Multimodal.RAG.AI.Search..Recommender.Systems.with.GPT4.part1.rar.html https://fikper.com/4WLjjrOdGv/vglnz.Multimodal.RAG.AI.Search..Recommender.Systems.with.GPT4.part2.rar.html No Password - Links are Interchangeable

Free Download DSPy - Develop a RAG app using DSPy, Weaviate, and FastAPI Published 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 1h 51m | Size: 1.12 GB Master Full-Stack RAG App Development with FastAPI, Weaviate, DSPy, and React What you'll learn Build and Deploy a Full-Stack RAG Application Efficient Data Management with Weaviate Document Parsing and File Handling Implement Advanced Backend Features with FastAPI Requirements Basic Knowledge of Python Familiarity with REST APIs Understanding of Frontend Development Development Environment Setup Description Learn to build a comprehensive full-stack Retrieval Augmented Generation (RAG) application from scratch using cutting-edge technologies like FastAPI, Weaviate, DSPy, and React. In this hands-on course, you will master the process of developing a robust backend with FastAPI, handling document uploads and parsing with DSPy, and managing vector data storage using Weaviate. You'll also create a responsive React frontend to provide users with an interactive interface. By the end of the course, you'll have the practical skills to develop and deploy AI-powered applications that leverage retrieval-augmented generation techniques for smarter data handling and response generation.Here's the structured outline of your course with sections and lectures:Section 1: IntroductionLecture 1: IntroductionLecture 2: Extra: Learn to Build an Audio AI AssistantLecture 3: Building the API with FastAPISection 2: File UploadLecture 4: Basic File Upload RouteLecture 5: Improved Upload RouteSection 3: Parsing DocumentsLecture 6: Parsing Text DocumentsLecture 7: Parsing PDF Documents with OCRSection 4: Vector Database, Background Tasks, and FrontendLecture 8: Setting Up a Weaviate Vector StoreLecture 9: Adding Background TasksLecture 10: The Frontend, Finally!Section 5: Extra - Build an Audio AI AssistantLecture 11: What You Will BuildLecture 12: The FrontendLecture 13: The BackendLecture 14: The End Who this course is for Backend Developers wanting to learn how to build APIs with FastAPI and integrate AI-driven features like document parsing and vector search. Full-Stack Developers seeking to gain practical experience in combining a React frontend with an AI-powered backend. Data Scientists and AI Practitioners who want to explore new ways to implement retrieval-augmented generation models for real-world applications. AI Enthusiasts curious about vector databases like Weaviate and the emerging field of RAG, with the motivation to learn and build AI-based apps from scratch. Homepage https://www.udemy.com/course/dspy-develop-a-rag-app-using-dspy-weaviate-and-fastapi/ Rapidgator https://rg.to/file/4f9e11b273a58d7c1f8970e4cb9c3661/ohdou.DSPy.Develop.a.RAG.app.using.DSPy.Weaviate.and.FastAPI.part1.rar.html https://rg.to/file/4e6240b776b2e7d50c5eafacd4ff1c1b/ohdou.DSPy.Develop.a.RAG.app.using.DSPy.Weaviate.and.FastAPI.part2.rar.html Fikper Free Download https://fikper.com/Ae2h0a6SuW/ohdou.DSPy.Develop.a.RAG.app.using.DSPy.Weaviate.and.FastAPI.part1.rar.html https://fikper.com/HDq2JkFu32/ohdou.DSPy.Develop.a.RAG.app.using.DSPy.Weaviate.and.FastAPI.part2.rar.html No Password - Links are Interchangeable

Free Download RAG Tuned AIs with the Cohere API Platform Released 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill Level: Beginner | Genre: eLearning | Language: English + srt | Duration: 21m | Size: 56 MB Explore the Cohere API platform and its powerful tools for fine-tuning AI models with Retrieval-Augmented Generation (RAG) techniques. Starting with an overview of Cohere's unique cloud-agnostic approach with their own fast release models focused on business needs. Learn to build and optimize AI models that are deployable across many cloud environments. Explore Cohere Developer tools and experiment with model customization and discover tools for building AI-driven applications. Homepage https://www.linkedin.com/learning/rag-tuned-ais-with-the-cohere-api-platform TakeFile https://takefile.link/f6q71th45y9u/gelgb.RAG.Tuned.AIs.with.the.Cohere.API.Platform.rar.html Rapidgator https://rg.to/file/3eec92efbe5e4eb7bd585b5d53bcabc6/gelgb.RAG.Tuned.AIs.with.the.Cohere.API.Platform.rar.html Fikper Free Download https://fikper.com/Nwh5xdmgul/gelgb.RAG.Tuned.AIs.with.the.Cohere.API.Platform.rar.html No Password - Links are Interchangeable

Free Download RAG and Generative AI with Python 2024 Published 9/2024 Created by Diogo Alves de Resende MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 86 Lectures ( 9h 5m ) | Size: 6.33 GB Mastering Retrieval-Augmented Generation (RAG), Generative AI (Gen AI), Prompt Engineering and OpenAI API with Python What you'll learn: Gain a solid foundation in information retrieval concepts, including tokenization, preprocessing, indexing, querying, and ranking. Implement various retrieval models in Python, such as the Vector Space Model, Boolean Retrieval, and Probabilistic Retrieval, using real-world datasets. Understand how text generation models work, including the principles behind transformers and attention mechanisms. Acquire hands-on experience in using Python libraries to build, fine-tune, and deploy generative models like GPT for various text generation tasks. Learn how to effectively combine retrieval and generative models to build robust Retrieval-Augmented Generation (RAG) systems. Utilize Python for advanced RAG system components, such as tokenization, embedding creation, FAISS indexing, and context distance definition. Explore the integration of OpenAI's API in RAG systems to enhance retrieval and generation capabilities, including prompt engineering and embedding strategies. Develop skills to process and integrate unstructured data formats (Excel, Word, PowerPoint, EPUB, PDF) into RAG systems using Python. Learn to build multimodal RAG systems that combine text, audio, and image data using Python, leveraging models like CLIP and Whisper. Master techniques to improve the accuracy, efficiency, and effectiveness of RAG systems, preparing you for real-world applications and advanced AI research. Requirements: Python Proficiency (For loops, Functions) Description: Are you struggling to build RAGs? As the amount of digital content grows exponentially, it becomes increasingly challenging to create AI models that can efficiently sift through vast data to provide accurate and meaningful responses. Traditional search engines and basic AI models often fall short in delivering the context-aware results needed in today's fast-paced digital landscape.RAG and Generative AI with Python is designed to solve this problem by teaching you how to build powerful Retrieval-Augmented Generation (RAG) systems using Python. This course will guide you through the essentials of combining retrieval techniques with generative models to develop applications that are both highly responsive and contextually accurate.Throughout this course, you will:Understand RAG Systems: Learn how to integrate retrieval and generation to enhance your AI models' capabilities, making them more effective at understanding and generating relevant content.Learn Practical Python Applications: Gain hands-on experience with Python libraries and frameworks, enabling you to implement RAG systems and generative models from scratch.Explore Generative AI and Prompt Engineering: Delve into the mechanics of generative models and the art of prompt engineering to refine AI outputs, ensuring they meet specific user needs.Utilize OpenAI's API for Advanced Applications: Discover how to leverage OpenAI's API to enhance your models, adding a new layer of sophistication to your AI solutions.Handle Various Data Formats in AI Systems: Develop skills to manage unstructured data types, including text, images, and audio, and integrate them into multimodal RAG systems for comprehensive AI applications.Optimize AI Models for Real-World Use: Learn strategies to fine-tune your AI models for improved efficiency, accuracy, and performance in practical scenarios.This course is perfect for data scientists, software developers, AI enthusiasts, and anyone with a basic knowledge of Python who wants to build smarter, more efficient AI systems. If you're ready to overcome the limitations of traditional models and lead the charge in AI innovation, this course is for you.Take the next step in your AI journey with RAG and Generative AI with Python and learn how to create the advanced AI tools that the world needs now. Enroll today and start transforming the way you build AI systems! Who this course is for: Data Scientists and Machine Learning Engineers looking to deepen their knowledge of generative AI systems. AI Researchers and Enthusiasts interested in exploring the latest advancements in (RAG) and generative AI technologies. oftware Developers and Programmers who want to expand their skill set to include AI and machine learning techniques. Technical Product Managers and AI Strategists who manage AI projects and need a deeper technical understanding of how RAG systems work and their potential applications. AI Consultants and Data Analysts aiming to add AI capabilities to their skillset Entrepreneurs and business leaders in the tech space who want to understand the potential of RAG systems and generative AI to innovate. 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