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Implement RAG with LangChain to Explore IBM Quantum Research

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Artificial IntelligenceIT automationQuantumBryan ClarkBJ HargraveErika Russiretrieval augmented generationGranite® 4.0quantum computingquantum computersLangChainlarge language models (LLMs)fine-tuningautonomous agentsvectorLangGraphframeworkembeddingsIBM Cloud® accountdownloadingthis recipeGitHubwatsonx.ai Runtime(API) keywatsonx.aiHugging Facethis embeddings model recipethis vector store recipefoundation modelswatsonx.aiLLM component recipeIBM paperLangSmithhereTutorial IBM Developer: RAG tutorials Explore all IBM Developer retrieval augmented generation (RAG) tutorials. Start learningTechsplainers 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 nowBlog IBM RAG Cookbook Explore a comprehensive collection of best practices, considerations and tips for building RAG solutions tailored to business applications. Read the blogArticle IBM Developer: RAG articles Explore all IBM Developer retrieval augmented generation (RAG) articles. Get startedArchitecture Retrieval augmented generation (RAG) architecture Discover proven architecture patterns that accelerate the creation of technology solutions to meet your business challenges. Explore architectureTutorial 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 learningTutorial 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 learningExplore GraniteExplore AI solutionsExplore AI servicesDiscover GraniteDownload the models

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