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LangChain Mastery

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  1. Introduction

    Welcome to the course
    3 Temas
  2. Set up your environment
    6 Temas
  3. Module 1 - Custom Assistants
    LLMs and LangChain
    3 Temas
  4. Prompt Engineering
    7 Temas
  5. Introduction to chains
    4 Temas
  6. Using other LLM providers
    5 Temas
  7. Create a full-stack app
    9 Temas
  8. Module 2 - RAG Applications
    Introduction to RAG
    3 Temas
  9. Introduction to LCEL
    3 Temas
  10. Data loaders
    5 Temas
  11. Vector databases
    8 Temas
  12. Similarity Search (optional)
    4 Temas
  13. Create your RAG assistant
    11 Temas
    1. Our Streamlit application design
    2. Add your secrets
    3. Create your chat interface
    4. Implement your conversation chain
    5. Enable conversation history
    6. Implement your data loader (PDFs)
    7. Split your documents
    8. Initialize your vector store
    9. Implement your retrieval chain
    10. Test your application
    11. CHALLENGE: Add a web based loader to your app
  14. Module 3 - Agents as Assistants
    Introduction to agents
    5 Temas
  15. Using tools
    8 Temas
  16. Create your own agent
    3 Temas
  17. Observe your agent
    4 Temas
  18. Create your agent app
    4 Temas
  19. BONUS: Deploy your LLM Applications
    Set up for deployment
    5 Temas
  20. Reminder of Git and GitHub
    5 Temas
  21. Managed Hosting
    2 Temas
  22. Containerize your Application
    7 Temas
  23. Deploy your container
    5 Temas
  24. CI/CD
    4 Temas

Participants 1

LangChain Mastery Create your RAG assistant
Lección 13 of 24
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Create your RAG assistant

Alejandro AO diciembre 23, 2024
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