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Free Download RAG-Powered AI - Build a Chatbot inPython, LangChain & Ollama Published: 3/2025 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 3h 17m | Size: 1.58 GB Learn to Build an AI-Powered PDF Q&A Chatbot with RAG, Ollama, LangChain, and Vector Embeddings in Python What you'll learn Understand Retrieval-Augmented Generation (RAG) - Learn how RAG improves LLM responses by combining real-world data with AI-generated text. Build a PDF Q&A Chatbot - Develop a working chatbot that extracts and retrieves relevant information from a PDF using LangChain, Ollama Implement Vector Embeddings & Semantic Search - Generate vector embeddings for document text and use a local database for information retrieval Run Local AI Models with Ollama - Set up and interact with local large language models (LLMs) like Mistral and Llama3 to generate AI-driven responses. Requirements Basic Python Knowledge - Familiarity with Python programming (variables, functions, and loops). Fundamental Understanding of AI/LLMs - Some exposure to large language models (LLMs) and AI concepts is helpful but not required. VS Code & Command Line Basics - Ability to install and run Python packages using the terminal or command prompt. No Prior Experience with LangChain or Ollama Needed - The course covers these tools from scratch. Description Course Description:Welcome to "Building a RAG Application with Ollama, LangChain, and Vector Embeddings in Python"! This hands-on course is designed for Python developers, data scientists, and AI enthusiasts looking to dive into the world of Retrieval-Augmented Generation (RAG) and learn how to build intelligent document-based applications.In this course, you will learn how to create a powerful PDF Q&A chatbot using state-of-the-art AI tools like Ollama, LangChain, and Vector Embeddings. You'll gain practical experience in processing PDF documents, extracting and generating meaningful information, and integrating a local Large Language Model (LLM) to provide context-aware responses to user queries.What you will learn:What is RAG (Retrieval-Augmented Generation) and how it enhances the power of LLMsHow to process PDF documents using LangChainExtracting text from PDFs and splitting it into chunks for efficient retrievalGenerating vector embeddings using semantic search for better accuracyHow to query and retrieve relevant information from documents using Vector DBIntegrating a local LLM with Ollama to generate context-aware responsesPractical tips for fine-tuning and improving AI model responsesCourse Highlights:Step-by-step guidance on setting up your development environment with VS Code, Python, and necessary libraries.Practical projects where you'll build a fully functional PDF Q&A chatbot from scratch.Hands-on experience with Ollama (a powerful tool for running local LLMs) and LangChain (for document-based AI processing).Learn the fundamentals of vector embeddings and how they improve the search and response accuracy of your AI system.Build your skills in Python and explore how to apply machine learning techniques to real-world scenarios.By the end of the course, you'll have the skills to build and deploy your own AI-powered document Q&A chatbot. Whether you are looking to implement AI in a professional setting, develop your own projects, or explore advanced AI concepts, this course will provide the tools and knowledge to help you succeed.Who is this course for?Python Developers who want to integrate AI into their projects.Data Scientists looking to apply RAG-based techniques to their workflows.AI Enthusiasts and learners who want to deepen their knowledge of LLMs and AI tools like Ollama and LangChain.Beginners interested in working with AI and machine learning to build real-world applications.Get ready to dive into the exciting world of AI, enhance your Python skills, and start building your very own intelligent PDF-based chatbot! Who this course is for Python developers interested in AI and LLM-powered applications. Data scientists & ML engineers exploring Retrieval-Augmented Generation (RAG). Tech enthusiasts & AI beginners who want to build AI-driven document Q&A systems. Students & researchers looking to extract insights from large PDF documents using AI. Homepage: https://www.udemy.com/course/rag-powered-ai-build-a-chatbot-inpython-langchain-ollama/ [b]AusFile[/b] https://ausfile.com/pweaqh9d26xy/mypzi.RAGPowered.AI.Build.a.Chatbot.inPython.LangChain..Ollama.part1.rar.html https://ausfile.com/41u0fmu55d3j/mypzi.RAGPowered.AI.Build.a.Chatbot.inPython.LangChain..Ollama.part2.rar.html Rapidgator https://rg.to/file/78392706d2a09c54b31473340679a289/mypzi.RAGPowered.AI.Build.a.Chatbot.inPython.LangChain..Ollama.part1.rar.html https://rg.to/file/533dd15f5ae29c268f1c6a954dc9134a/mypzi.RAGPowered.AI.Build.a.Chatbot.inPython.LangChain..Ollama.part2.rar.html No Password - Links are Interchangeable

Free Download Semantic Search API with S-BERT and Search API with RAG/LLM Published: 3/2025 Created by: André Vieira de Lima MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Intermediate | Genre: eLearning | Language: English | Duration: 58 Lectures ( 7h 21m ) | Size: 3.55 GB Using Artificial Intelligence (NLP) to build a semantic text query API with BERT and RAG (LangChain/LLM) What you'll learn Implement semantic text search engine API using S-BERT. Implement a search engine API using Retrieval-Augmented Generation (RAG) and LLM. Bootcamp for building an artificial intelligence API with resources used in companies like Google. Acquisition of knowledge in Natural Language Processing (NLP) for text processing with Machine Learning. Using NLP tools like NLTK, Spacy, Sentence Transformers for building search engine. Using NLP tools like NLTK, Spacy, Sentence Transformers for building search engine. Hands on (practical project) in building a complete Artificial Intelligence / Machine Learning project in Python. Develop an LLM agent using LangChain. Requirements Python knowledge Pandas Knowledge Description In a rich Artificial Intelligence Bootcamp, learn S-BERT and RAG(LLM) through Natural Processing Language (NLP) with Python, and develop a semantic text search engine API by solving a real problem of a purchasing analysis system.As content:Fundamentals.Learn the fundamentals of Data Science;Learn the fundamentals of Machine Learning;Learn the fundamentals of Natural Language Processing;Learn the fundamentals of Data Cleaning, Word Embendings, Stopwords, and Lemmatization;Learn the fundamentals of text search by keywords and semantic text search;Practice Data Science to understand the problem, prepare the database and statistical analysis;Practical project.This course is divided into two modules where you will learn concepts and build a text search application in a practical way.BERT In this module, you will work with:Python to develop the application;Data cleaning techniques to prepare the database;Using the SpaCy library for Natural Language Processing;Generating Word Embeddings and calculating similarity for data recovery;Transformers model for data recovery by context;S-BERT as a semantic text search tool;Flask and Flassger for developing APIs.Retrieval-augmented generation (RAG)In this module you will work with:Python to develop the application;Large Language Models (LLMs). Advanced AI models that understand and generate natural language;Using the OpenAI API to build AI productsLangChain to build applications that use LLMs;Flask and Flassger for developing APIs.Optional module: learn how to develop API with Flask.Welcome and have fun. Who this course is for Interested in innovation in the latest and most valuable Data Science and Artificial Intelligence technologies. Interested in deepening Natural Language Processing (NLP) techniques Interested in building a semantic text search engine that evaluates synonyms in search terms. Homepage: https://www.udemy.com/course/semantic-search-api-with-s-bert-and-search-api-with-ragllm/ Rapidgator https://rg.to/file/7d35d59e6009ab55c21a4efe7448874c/cnoyv.Semantic.Search.API.with.SBERT.and.Search.API.with.RAGLLM.part4.rar.html https://rg.to/file/9666fba5ea155b4607b68408a59323dd/cnoyv.Semantic.Search.API.with.SBERT.and.Search.API.with.RAGLLM.part1.rar.html https://rg.to/file/d534b264753c8c08d3fbc698097c7031/cnoyv.Semantic.Search.API.with.SBERT.and.Search.API.with.RAGLLM.part3.rar.html https://rg.to/file/ffe6d0093b733b5ba7c39caa5bc4ca47/cnoyv.Semantic.Search.API.with.SBERT.and.Search.API.with.RAGLLM.part2.rar.html Fikper Free Download https://fikper.com/2POJcnjf8Q/cnoyv.Semantic.Search.API.with.SBERT.and.Search.API.with.RAGLLM.part2.rar.html https://fikper.com/2cLvzXL5Wa/cnoyv.Semantic.Search.API.with.SBERT.and.Search.API.with.RAGLLM.part3.rar.html https://fikper.com/B05J6OHs1g/cnoyv.Semantic.Search.API.with.SBERT.and.Search.API.with.RAGLLM.part4.rar.html https://fikper.com/xci3iacRwA/cnoyv.Semantic.Search.API.with.SBERT.and.Search.API.with.RAGLLM.part1.rar.html No Password - Links are Interchangeable

Free Download Mastering Chatbots with Botpress, Transformers, RAG & LLMs Last updated: 12/2024 Created by: Abu Bakr Soliman MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 142 Lectures ( 16h 48m ) | Size: 8.6 GB All you need to develop your next AI Chatbot using Open-Source tools like Botpress, Rasa, Transformers and FastAPI What you'll learn Developing Chatbots using Open-Source tools like Botpress, Rasa and Transformers. No cloud based solutions. Understand all of the Chatbot developing pillars like intent-detection, entity-recognition, conversation flow and more. Learn the Concepts of Prompt Engineering using LangChain, ChatGPT and HuggingFace Develop Neural Networks models to detect entities and Recognize entities in the user messages. Integrate with third parties and APIs to develop mature chatbots with live data. Develop web applications using fastAPI to support the chatbot services. Learn via developing a set of real-world chatbot projects. Requirements A big portion of this course does not require any programming skills. Just the basics of Python and JavaScript are required for the advanced levels Description Are you ready to learn how to build powerful and AI-supported chatbots from scratch?there are a lot of courses out there that teach you how to develop chatbots. So what makes this course DIFFERENT?We're NOT going to use any cloud-based chatbot solutions like Dialogflow, IBM Watson, or Microsoft Azure. Instead, we'll be focusing on free and open-source technologies that are just as robust and powerful.We're NOT just going to talk only about the basics of chatbot development. We're going to dive deeply into this world.This course is full of project-based tutorials. A lot of techniques will be derived via developing a set of chatbot projectsChatbots are everywhere and are becoming an increasingly important part of our daily lives. They're used for a wide range of applications, from customer service to online shopping, and they're only getting more advanced and sophisticated.In the course, we delve into the different types of chatbots and their use cases, including rule-based chatbots, AI-powered chatbots, and conversational AI. We also cover the various technologies and platforms that are used to build chatbots, such as natural language processing (NLP), machine learning (ML), and chatbot development open-source projects like Botpress, SetFit, GLiNER, Transformers, langChain, fastAPI, Docker, and more.In this course, you will learn:How to Setup Your Development Environment ToolsHow to Install and start your first Botpress projectYou will Understand what the conversation flow studio isDevelop the different types of chatbot response templatesYou will learn how to Integrate with third parties and APIs to provide external information for usersHow to Develop a QnA chatbotsUnderstand the problem intent detection and how to solve it using either rule-based or neural network techniquesHow to recognize entities in the user message and how to fill the slots.How to collect user data and forward them to an external API or store them in a database.How to develop your Transformers Chatbot Assistant models (Rasa, SetFit and GLiNER)How to integrate Botpress with Rasa Chatbot AssistantHow to develop a fastAPI app to serve your AI projectsHow to integrate your chatbot with popular messaging platforms like Facebook Messenger and TelegramHow to use the modern Large Language Models (LLMs) like OpenAI to support your chatbotsLearn all the basics of building a robust application using ChatGPT and open-source Large Language ModelsHow to use Drage-Drop UI Tools like Flowise to Develop LLM chatbotsHow to use LLMs to develop AI Engines and ChatbotsBuild the style of "Chat with your data" modern apps.Learn in detail how to build RAG LLM apps.More ..By the end of the course, students will have a comprehensive understanding of the current state of chatbot technology and how it is being used in real-world applications. This knowledge will equip students with the skills and confidence to embark on their chatbot projects and contribute to the rapidly evolving field of conversational AI. 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Released: 02/2025 Free Download Duration: 54m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 143 MB Level: Advanced | Genre: eLearning | Language: English Unlock the power of retrieval-augmented generation (RAG) with this hands-on course on RAFT (retrieval-augmented fine-tuning). Learn to integrate fine-tuning with RAG to create domain-specific models that deliver accurate, contextually relevant responses. From understanding core concepts to implementing advanced techniques like RAFT and using tools like Azure AI Studio, this course equips you with the skills to enhance and deploy sophisticated RAG systems. Ideal for AI practitioners aiming to optimize model performance in specialized domains. Homepage: https://www.linkedin.com/learning/rag-fine-tuning-advanced-techniques-for-accuracy-and-model-performance Rapidgator https://rg.to/file/0406aad1e579ee2c55c3be9353c8f625/adsfc.RAG.FineTuning.Advanced.Techniques.for.Accuracy.and.Model.Performance.rar.html Fikper Free Download https://fikper.com/rYAK0LWfXN/adsfc.RAG.FineTuning.Advanced.Techniques.for.Accuracy.and.Model.Performance.rar.html : No Password - Links are Interchangeable

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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Zadymiarze / Rag Union / Tryapichnyy soyuz (2015) PL.WEB-DL.Xvid-K12 / Lektor PL Re??yseria: Mikhail Mestetskiy Scenariusz: Mikhail Mestetskiy Gatunek: Dramat, Komedia Kraj: Rosja Rok produkcji: 2015 Czas trwania: 98 min. Szmaciany Sojusz to grupa trzech przyjaci???? z r????nym ??yciowym baga??em. Pe??ni energii, nieustraszeni, niezwyciÄ???eni, ze zwinno??ciÄ? wiewi??rek skaczÄ? przez nagrobki i po dachach samochod??w. WierzÄ?, ??e si??Ä? woli sÄ? w stanie przesunÄ?Ä? mury. Parkour i sztuka to ich spos??b na zastany porzÄ?dek rzeczy. Taka w??a??nie jest Szmaciana Rewolucja, kt??ra zmieni ??wiat. Gdyby Wania nie by?? taki nie??mia??y, to pewnie nie wziÄ???by tej pracy, a tak trafi?? na cmentarz jako ??ywa reklama zak??adu pogrzebowego. Z drugiej strony, gdyby nie by?? wtedy na cmentarzu, nie pozna??by Szmaciarzy. Szmaciarze nie wylÄ?dowaliby w domku po jego babci. Domek by wciÄ??? sta??... Ale Wania by?? nie??mia??y i wziÄ??? tÄ? pracÄ?. https://openload.co/f/wAT8SJdJsfg/Zadymiarze_%282015%29_PL.WEB-DL.Xvid-K12.avi

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