28 Feb LangChain Tutorial

LangChain is a framework designed to build applications powered by large language models (LLMs). It provides tools and abstractions to make it easier to integrate LLMs into applications, enabling tasks like question answering, text generation, retrieval-augmented generation (RAG), chatbots, and more.

Features of LangChain

• LangChain supports integration with various LLMs, including OpenAI’s GPT, Hugging Face models, and others.
• LangChain allows you to create chains of operations, where the output of one step is fed as input to the next.
• LangChain supports memory to store and retrieve context across interactions.
• LangChain provides tools for indexing and retrieving documents, making it easier to build retrieval-augmented generation (RAG) systems.
• LangChain supports agents, which are systems that use LLMs to decide what actions to take and in what order.
• LangChain provides tools for managing and optimizing prompts, including templating and dynamic prompt generation.
• LangChain is highly modular, allowing developers to mix and match components to build custom workflows.

Viewers

The LangChain tutorial is prepared for students, engineers, and professionals. It will help machine learning and data science enthusiasts understand the concept of AI.

LangChain Tutorial – Index

1. LangChain – Introduction, Features, and Use Cases
2. What is Chaining in LangChain
3. Components of LangChain
4. Preprocessing Component of LangChain
5. Models Component of LangChain
6. Prompts Component of LangChain
7. Memory Component of LangChain
8. Chains Component of LangChain
9. Indexes Component of LangChain
10. Agents Component of LangChain
11. LangChain for RAG – Workflow
12. LangChain for RAG – Process
13. LangChain for RAG – Final Example

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