Chatbots with RAG - LangChain Full Walkthrough
Comprehensive walkthrough on building a chatbot using Retrieval Augmented Generation, covering LangChain, OpenAI's GPT-3.5-turbo, embeddings, and Pinecone vector database for enhanced accuracy and context.
Nvidia's NeMo Guardrails - Full Walkthrough for Chatbots - AI
Comprehensive walkthrough of Nvidia's NeMo Guardrails library for building safer, more controlled chatbots and AI assistants. Covers implementation, dialogue flows, and practical Python examples.
Fine-Tuning Llama 3 on a Custom Dataset for RAG Q&A - Training LLM on a Single GPU
Learn to fine-tune Llama 3 on custom datasets for improved LLM performance in specific tasks, using a single GPU. Covers dataset preparation, model setup, training, and evaluation.
Building an AI RAG Application with LangChain and Next.js
Learn to enhance chatbots using LangChain and Next.js, focusing on RAG applications. Covers key concepts, implementation steps, and practical examples for building advanced AI-powered chat systems.
RAG Agents: Designing AI Assistants with OpenAI API and 10,000 Document Support
Explore OpenAI Assistants API for RAG systems: create AI agents, upload files, and support 10,000 documents. Learn Python implementation and code review for advanced AI assistance.
Build a RAG-Based LLM App in 20 Minutes - Langflow Tutorial
Learn to build an AI app using RAG and Langflow without coding. Create a chatbot, integrate OpenAI, use vector databases, and implement retrieval-augmented generation in minutes.
Cybersecurity Meets Generative AI - Automating Compliance Operations
Explore how LLMs can streamline cybersecurity compliance, reducing costs and time. Includes RAG application demo and discussion on AI governance concerns in compliance automation.
Complete Langchain Course for Generative AI
Comprehensive guide to building AI applications with LangChain, covering chatbots, RAG pipelines, and integration with various tools and platforms for generative AI development.
Getting Started with Retrieval Augmented Generation (RAG) in Java and Spring AI
Learn to implement Retrieval Augmented Generation (RAG) using Java and Spring AI. Explore RAG's applications, setup process, and practical implementation with a simple example.
Orchestrating RAG: Retrieval, Canopy, and Pinecone
Explore orchestrating RAG pipelines, optimizing retrieval strategies, and leveraging Canopy and Pinecone for scalable AI applications with billions of documents.
Advanced RAG: Chunking, Embeddings, and Vector Databases
Explore advanced RAG concepts: chunking for efficient retrieval, embeddings for text representation, and vector databases for optimized storage and retrieval in LLM applications.
Building an LLMOps Stack for Large Language Models
Explore LLMOps architecture: MLFlow for LLMs, Vector Databases, RAG strategies, and prompt evaluation. Gain insights into optimizing and deploying large language models effectively.
Langchain: RAG QA Tool with Gemini Pro, Langchain, and Chroma
Develop a RAG-based QA tool using Langchain, Gemini Pro, and Chroma for legal document analysis and retrieval.
Llama 2, Ollama and Pinecone - Vector Database Search for Machine Learning and Data Science
Explore embedding and searching text using Llama 2, Ollama, and Pinecone Vector Database for efficient information retrieval in machine learning projects.
Microsoft AI: Semantic Kernel C# SDK - Semantic Memory with Chroma
Develop a RAG component using Semantic Kernel, Chroma, and OpenAI models in C#. Enhance machine learning projects with advanced AI techniques.
NVIDIA NIM Integration with Langchain for RAG Q&A
Explore NVIDIA NIM integration with Langchain, creating a RAG Q&A system using Faiss for enhanced machine learning and data science applications.
Multimodal Q&A RAG Tool with Gemini for Data Science and Machine Learning
Develop a multimodal Q&A RAG tool using Gemini for unstructured data analysis, enhancing data science and machine learning capabilities.
Building Multimodal Question-Answering (RAG) System with Gemini
Leverage Gemini's 2M context window and multimodal input to build a financial analysis Q&A system. Explore Vertex Gemini API, text-embeddings, and cross-modal reasoning capabilities.
Bringing GenAI to the Modern Enterprise - A Production Use-Case in Serverless Java
Develop cutting-edge GenAI applications for enterprises using Java, AI orchestration frameworks, and multiple LLMs. Learn hands-on with a real-world production use-case in a serverless environment.
Building a RAG Application with GitHub Models and Postgres from Scratch
Learn to build a Retrieval Augmented Generation (RAG) application from scratch using GitHub Models and PostgreSQL, enhancing AI model capabilities for smarter applications.
Bringing Value to Enterprises with RAG
Discover how Intel's RAG technology bridges enterprise data with AI models, enabling customized LLM applications while maintaining data security and reducing hallucinations.