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Exploring the LLM Frontier

LangChain: A Powerful Tool for Working with LLMs

Instructors: Carlos Lizárraga, Enrique Noriega

October 3rd, 2024

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What is LangChain?

  • Definition: A Python library for building LLM-powered applications
  • Key features: modular, flexible, simplifies common tasks

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How LangChain Works

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Chains: Sequences of actions that process queries

Links: Individual steps within chains

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Core Components of Langchain

  • LLM Interface: Simplifies connecting to different LLMs
  • Prompt Templates: Pre-formatted queries
  • Agents: Custom action sequences
  • Memory and Callbacks: Retains information and tracks events

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Common Tasks Simplified

  • Text Generation
  • Summarization
  • Translation
  • Question Answering
  • Data augmentation
  • Virtual agents

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Advantages/Disadvantages of LangChain

  • Advantages:
    • Efficiency
    • Versatility
    • Community Support
    • Modularity
  • Disadvantages:
    • Learning Curve
    • Dependency on LLMs

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Alternatives to LangChain

  • LlamaIndex, Haystack, Auto-GPT, Semantic Kernel, Cohere,...

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Thank you!

We return to our notes…

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