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    Large Language Model Frameworks

    A course by
    Data Science Dojo
    Nov/2024 9 lessons English

    This course provides a comprehensive guide to building advanced applications with large language models (LLMs) using the LangChain framework. You’ll dive into the key components of LangChain, such as models, prompts, parsers, memory, chains, and question-answering, empowering you to create dynamic and scalable LLM applications. By the end of the course, you’ll have hands-on experience in developing complex workflows, optimizing retrieval, retaining context, and making decisions dynamically with agents.

    What you'll learn

    • Learn why orchestration tools like LangChain are essential for LLM development.
    • Understand how LangChain manages inputs and outputs for LLMs using prompts and models.
    • Discover how to integrate external data using document loaders, transformers, & vector stores.
    • Create complex LLM workflows with various chain types and summarize documents.
    • Retain context and track conversations with different memory types to overcome token limits.
    • Enable dynamic decision-making with agents like Self Ask, ReAct, and structured chat.
    • Monitor and log LLM applications using callbacks for better performance.
    • Practice exercises on Model I/O, Memory, Chains, and Agents to reinforce your learning.

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