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Build an AI-powered multimodal RAG system with Docling and Granite | IBM

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Welcome Overview Machine learning types Machine learning algorithms Statistical machine learning Linear algebra for machine learning Uncertainty quantification Bias variance tradeoff Bayesian Statistics Singular value decomposition Overview Feature selection Feature extraction Vector embedding Latent space Principal component analysis Linear discriminant analysis Upsampling Downsampling Synthetic data Data leakage Overview Linear regression Lasso regression Ridge regression State space model Time series Autoregressive model Overview Decision trees K-nearest neighbors (KNNs)Naive bayes Random forest Support vector machine Logistic regression Overview Boosting Bagging Gradient boosting Gradient boosting classifier Overview Transfer learning Overview Overview K means clustering Hierarchical clustering A priori algorithm Gaussian mixture model Anomaly detection Overview Collaborative filtering Content based filtering Overview Reinforcement learning human feedback Deep reinforcement learning Overview Overview Backpropagation Encoder-decoder model Recurrent neural networks Long short-term memory (LSTM)Convolutional neural networks Overview Attention mechanism Grouped query attention Positional encoding Autoencoder Mamba model Graph neural network Overview Generative model Generative AI vs. predictive AI Overview Reasoning models Small language models Instruction tuning LLM parameters LLM temperature LLM benchmarks LLM customization LLM alignment Diffusion models Variational autoencoder (VAE)Generative adversarial networks (GANs)Overview Vision language models Tutorial: Build an AI stylist Tutorial: Multimodal AI queries using Llama Tutorial: Multimodal AI queries using Pixtral Tutorial: Automatic podcast transcription with Granite Tutorial: PPT AI image analysis answering system Overview GraphRAG Tutorial: Build a multimodal RAG system with Docling and Granite Tutorial: Evaluate RAG pipline using Ragas Tutorial: RAG chunking strategies Tutorial: Graph RAG using knowledge graphs Tutorial: Inference scaling to improve multimodal RAG Overview Vibe coding Visit the 2025 Guide to AI Agents Overview LLM training Loss function Training data Model parameters Gradient descent Stochastic gradient descent Hyperparameter tuning Learning rate Overview Parameter efficient fine tuning (PEFT)LoRA Tutorial: Fine tuning Granite model with LoRA Regularization Foundation models Overfitting Underfitting Few shot learning Zero shot learning Knowledge distillation Meta learning Data augmentation Catastrophic forgetting Overview Scikit-learn XGboost PyTorch Overview AI lifecyle AI inference Model deployment Machine learning pipeline Data labeling Model risk management Model drift AutoML Model selection Federated learning Distributed machine learning AI stack Overview Natural language understanding Overview Sentiment analysis Tutorial: Spam text classifier with PyTorch Machine translation Overview Information retrieval Information extraction Topic modeling Latent semantic analysis Latent Dirichlet Allocation Named entity recognition Word embeddings Bag of words Intelligent search Speech recognition Stemming and lemmatization Text summarization Conversational AI Conversational analytics Natural language generation Overview Image classification Object detection Instance segmentation Semantic segmentation Optical character recognition Image recognition Visual inspection BJ Hargrave Erika Russi Docling IBM Granite vision embeddings generative AI Replicate HuggingFace Retrieval-augmented generation (RAG)fine-tuning multimodal Docling Granite LangChain Granite Snack Cookbook GitHub Granite Embeddings models this Embeddings Model recipe ibm-granite Getting Started with Replicate LLM component recipe LLM component recipe Docling this Vector Store recipe Ebook Unlock the power of generative AI and ML Learn how to confidently incorporate generative AI and machine learning into your business. Read the ebook Tutorial IBM Developer: RAG tutorials Explore all IBM Developer retrieval augmented generation (RAG) tutorials. Start learning Techsplainers Podcast Retrieval augmented generation (RAG) explained Techsplainers by IBM breaks down the essentials of RAG, from key concepts to real‑world use cases. Clear, quick episodes help you learn the fundamentals fast. Listen now Blog IBM RAG Cookbook Explore a comprehensive collection of best practices, considerations and tips for building RAG solutions tailored to business applications. Read the blog Article IBM Developer: RAG articles Explore all IBM Developer retrieval augmented generation (RAG) articles. Get started Architecture Retrieval augmented generation (RAG) architecture Discover proven architecture patterns that accelerate the creation of technology solutions to meet your business challenges. Explore architecture Tutorial Build a RAG agent to answer complex questions Use Python, LangGraph, watsonx.ai®, Elasticsearch and Tavily to build a customized, modular agentic AI system. Start learning Tutorial Quick start: Prompt a foundation model with the retrieval-augmented generation pattern Learn how to use foundation models in IBM watsonx.ai to generate factually accurate output grounded in information in a knowledge base by applying the retrieval augmented generation pattern. Start learning Explore watsonx Orchestrate Explore AI development tools Explore AI services Explore watsonx Orchestrate Explore AI development tools

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