Karel Rodriguez’s Post

Top LangChain Books to Read in 2024 https://lnkd.in/eiivpiEH Here are some top books to read for practical guidance on using LangChain and generative AI in 2024: 1. "Quick Start Guide to Large Language Models": Provides practical guidance on working with and deploying LLMs such as GPT-4, BERT, T5, and LLaMA to solve real-world problems, including sample codes. 2. "Introduction to Generative AI": Covers the fundamentals of generative AI and how to use it safely and effectively in personal and professional workflows. 3. "Generative AI with LangChain": A guide to using the LangChain framework to develop and deploy production-ready LLM applications, including prompt engineering to improve performance. 4. "LangChain Crash Course": Covers the fundamentals of LangChain and teaches how to build LLM-powered applications using hands-on exercises. 5. "LangChain in your Pocket": A guide to creating powerful applications using LLMs, covering topics like Auto-SQL, NER, RAG, and Autonomous AI agents with step-by-step code explanations. 6. "Generative AI on AWS": Covers the entire generative AI project lifecycle on Amazon Bedrock, including using LangChain to develop agents and actions. 7. "Machine Learning Engineering with Python": A comprehensive guide to building and scaling machine-learning projects, including a section on generative AI and building LLM-powered pipelines using LangChain. 8. "Developing Apps With GPT-4 and ChatGPT": Teaches how to create applications with large language models, covering topics like prompt engineering, model fine-tuning, and frameworks like LangChain. 9. "LangChain Handbook": A complete guide to integrating and implementing LLMs using the LangChain framework, covering applications like chatbots, document analysis, and code analysis. 10. "LangChain for Everyone": Covers the practical ways the LangChain framework can be leveraged to develop LLM-powered applications in various industries. For more information and free consultation, you can reach out to AI Lab in Telegram @aiscrumbot or follow @itinaicom on Twitter. #productmanagement #ai #ainews #llm #ml #startup #innovation #uxproduct #artificialintelligence #machinelearning #technology #ux #datascience #deeplearning #tech #robotics #aimarketing #bigdata #computerscience #aibusiness #automation #aitransformation

If you're in the Seattle area don't miss this workshop tonight(6/12) with Niels Bantilan! Build a Custom Chatbot with LLMs + RAG. Most modern chatbots today use Large Language Models(LLMs), Retrieval Augmented Generation(RAG), and scalable workflows to create a unique conversational experience! This workshop will equip you with the skills to effectively build your own chatbot and scalable AI workflows for LLMs using and union.ai The modern MLOps tooling will also provide a reliable framework for your machine learning operations by streamlining processes, increasing efficiency, and adding reproducibility to your AI applications. https://lnkd.in/g3WeQjZt #workshop #llms #rag

🚀 New Video: Building a Perplexity AI-inspired Developer Search Tool with LangChain.js I'm excited to share my latest tutorial on creating a specialized search tool for developers using LangChain.js, inspired by Perplexity AI! 🔍 Why is this important? Understanding how to augment traditional APIs with Large Language Models (LLMs) is becoming crucial in the AI-driven world. This skill allows developers to create more intelligent, context-aware applications that can process and synthesize information from multiple sources. 🤖 The Gateway to Building AI Agents This project serves as a foundation for understanding how AI agents work. By learning to combine different data sources and LLMs, you're taking the first step towards building more complex AI agents that can perform a wide range of tasks autonomously. 🛠️ What we built: A tool that searches a developer's GitHub profile and Google presence Uses custom GitHub API and Google Search API integrations Leverages LangChain.js to orchestrate the workflow and interact with LLMs Summarizes a developer's impact and contributions using GPT-3.5 Whether you're new to AI development or looking to expand your skills, this tutorial offers valuable insights into the world of LLMs and API integrations. Check out the video to learn how to build your own AI-powered search tool and take your first steps towards creating advanced AI agents! #AIEngineering #MachineLearning #JavaScript #LangChain #PerplexityAI #TechTutorial

#LLMs are useful beyond just #chatbots and #RAG They are great at text classification: sentiment analysis, request triaging, document labeling... In this article, I explore using #langchain4j with #Gemini to classify text: https://lnkd.in/eyTK_7zA First, I take advantage of #Gemini's intrinsic training knowledge to do sentiment analysis (zero-shot), then I use a few-shot prompting technique to give the model examples of classification. Then, I use an Embedding Models based approach to calculate vector embeddings of labeled samples, to compare them with the text to classify, thanks to #langchain4j's EmbeddingModelTextClassifier class. I used #VertexAI's latest embedding model to compute vectors, and #Gemini to prepare some sample data for each label. #GenerativeAI is more than just chatbots and RAG! And it can be useful for plenty of other tasks!

🚀 Repo Grokking™: The AI That “Gets” Your Code! 💻✨ Imagine AI that truly understands your entire codebase. 🤯 Zencoder’s Repo Grokking™ dives deep into your repo to deliver spot-on code suggestions and fixes! 🔍💡 Check out our latest blog: https://hubs.la/Q02Ql_l40 Let’s bring the zen back to coding! 🌱 #Zencoderai #RepoGrokking #AI #CodingRevolution #MondayMotivation #AIForDevelopers #CodeSmarter

🚀 Repo Grokking™: The AI That “Gets” Your Code! 💻✨ Imagine AI that truly understands your entire codebase. 🤯 Zencoder’s Repo Grokking™ dives deep into your repo to deliver spot-on code suggestions and fixes! 🔍💡 Check out our latest blog: https://hubs.la/Q02Ql_pT0 Let’s bring the zen back to coding! 🌱 #Zencoderai #RepoGrokking #AI #CodingRevolution #MondayMotivation #AIForDevelopers #CodeSmarter

Comprehensive Guide to Build AI Agents from Scratch

💡 A new day brings us a new Generative AI fueled chatbot, Claude 3, ready to take on GPT-4. ODSC East will help you keep pace with the rapid advancement of Generative AI applications with our expert-led sessions. ✅ Iván Martínez Toro and Dr. Daniel Gallego Vico – Mastering PrivateGPT: Tailoring GenAI for your unique applications Explore the various configuration options and extensions of PrivateGPT, from  the default API and functionalities using its Python SDK to extending RAG functionalities. ✅ Heiko Hotz – Beyond Theory: Effective Strategies for Bringing Generative AI into Production Examine the critical concepts surrounding Foundation Model Operations and Large Language Model Operations (FMOps/LLMOps), especially the practical intricacies and challenges of  deploying, monitoring, maintaining and scaling generative AI models in enterprise production systems. ✅ Arun Verma, PhD – Generative Modeling in Quantitative Finance Discover the different ways generative modeling is changing quantitative finance, including generating realistic financial time-series, volatility and correlation estimation and portfolio optimization. ✅ Sanyam Bhutani –  LLM Best Practises: Training, Fine-Tuning and Cutting Edge Tricks from Research Explore the tips and tricks of creating, fine-tuning, and cutting-edge ideas of building Large Language Models. 🚀 Master the knowledge and skills you need to make generative AI work for you!🚀 ➡️ Don't miss out! You can find more sessions here: https://lnkd.in/dCsQEquJ  #ODSC #DataScience #MachineLearning #AI #Boston #Learning #Community

🚀 Exploring Agent Development Frameworks for AI Applications As AI continues to evolve, creating autonomous, decision-making systems—often referred to as agents—is becoming more accessible and exciting. Whether you're building chatbots, automating workflows, or solving complex problems, having the right framework can make all the difference. Here are some powerful frameworks to kickstart your journey: 1️⃣ LangChain 🛠️ Best for working with LLMs (like OpenAI and Hugging Face models). LangChain simplifies chaining prompts, managing memory, and integrating with diverse data sources. Perfect for conversational agents and knowledge-based systems. 2️⃣ Auto-GPT 🤖 Want agents that think, plan, and execute independently? Auto-GPT takes GPT models to the next level, enabling complex task automation with minimal human input. 3️⃣ Ray Serve ⚡ Scaling AI agents for real-world applications? Ray excels in distributed computing and ensures your agents can handle high loads efficiently. 4️⃣ Rasa 💬 Focused on conversational AI? Rasa's open-source framework helps you build context-aware chatbots with full customizability. 5️⃣ Hugging Face Transformers 📚 Pretrained models at your fingertips. While not exclusively for agents, Hugging Face simplifies language-based agent development with fine-tuning and deployment tools. These frameworks are shaping how we approach autonomous systems. From personal assistants to automated customer support, the possibilities are endless! 💡 What agent frameworks have you explored or are curious about? Let’s discuss in the comments! #AI 🤖 #MachineLearning #AgentDevelopment #LangChain #AutoGPT #Python #TechInnovation 🚀

Would you like to build your own AI employees? It is possible with a platform like CrewAI. Day 40 of #100DaysofAI - Building AI agents with CrewAI. CrewAI is a platform that lets you build teams of AI agents that perform various tasks of a project. The key to building good agents is to clearly define their role, the tasks that they should perform, the model and tools they can use. Often, a project involves people with different kinds of skillsets working. Likewise, different agents need different set of tools and models to perform different tasks of a project. Let us say, you are trying to build something like Devin. To accomplish a coding task, you need planning, research and execution(coding). In this case, we can create three different agents and make them work in a sequential process. Each agent is defined with their own role, tasks, model and a set of tools. For example, GPT-4 might be a good model choice for a research agent and for building a coding agent you would like to use WizardCoder-34B. The key point that differentiates an agent from a chatbot is its ability to use multiple external tools for task execution. With CrewAI, you can add different tools like search, calculator, file explorer etc., In our example, the research agent requires a search tool and the coding agent needs a file explorer to generate code files. You can learn in much more detail with these videos: https://lnkd.in/eG3dG47q https://lnkd.in/exZtNxCy CrewAI is available for free but you need API keys of the respective models and tools that are included. Have you built anything with CrewAI? Let me know in the comments. In the upcoming days of #100DaysofAI, we will get started with Computer Vision and Multimodal LLMs. #ai #tech #artificialintelligence #naturallanguageprocessing #datascience #python #transformers #generativeai #devin #agents