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Премиум
  • Урок 1. 00:05:25
    Introduction
  • Урок 2. 00:07:16
    Introduction to LangChain
  • Урок 3. 00:07:06
    Setting Up the Environment: LangChain, Python-dotenv
  • Урок 4. 00:06:30
    ChatModels: GPT-3.5-Turbo and GPT-4
  • Урок 5. 00:04:57
    Caching LLM Responses
  • Урок 6. 00:02:58
    LLM Streaming
  • Урок 7. 00:05:36
    Prompt Templates
  • Урок 8. 00:05:55
    ChatPromptTemplate
  • Урок 9. 00:06:56
    Simple Chains
  • Урок 10. 00:07:15
    Sequential Chains
  • Урок 11. 00:04:01
    Introduction to LangChain Agents
  • Урок 12. 00:07:41
    LangChain Agents in Action: Python REPL
  • Урок 13. 00:11:08
    LangChain Tools: DuckDuckGo and Wikipedia
  • Урок 14. 00:13:30
    Creating a React Agent
  • Урок 15. 00:04:50
    Testing the React Agent
  • Урок 16. 00:01:53
    Short Recap of Embeddings
  • Урок 17. 00:06:58
    Introduction to Vector Databases
  • Урок 18. 00:04:27
    Authenticating to Pinecone
  • Урок 19. 00:09:32
    Working with Pinecone Indexes
  • Урок 20. 00:08:43
    Working with Vectors
  • Урок 21. 00:06:44
    Namespaces
  • Урок 22. 00:09:20
    Splitting and Embedding Text Using LangChain
  • Урок 23. 00:08:50
    Inserting the Embeddings into a Pinecone Index
  • Урок 24. 00:07:54
    Asking Questions (Similarity Search)
  • Урок 25. 00:04:21
    Getting a Gemini API Key
  • Урок 26. 00:05:14
    Gemini Multimodal Models: Nano, Pro, and Ultra
  • Урок 27. 00:04:31
    Installing the Python Libraries for Gemini and Authenticating to Gemini
  • Урок 28. 00:06:02
    Integrating Gemini with LangChain
  • Урок 29. 00:06:32
    Using a System Prompt and Enabling Streaming
  • Урок 30. 00:14:13
    Multimodal AI With Gemini
  • Урок 31. 00:06:09
    Project Introduction
  • Урок 32. 00:07:28
    Loading Your Custom (Private) PDF Documents
  • Урок 33. 00:05:13
    Loading Different Document Formats
  • Урок 34. 00:04:38
    Public and Private Service Loaders
  • Урок 35. 00:06:39
    Chunking Strategies and Splitting the Documents
  • Урок 36. 00:13:34
    Embedding and Uploading to a Vector Database (Pinecone)
  • Урок 37. 00:10:34
    Asking and Getting Answers
  • Урок 38. 00:11:11
    Using Chroma as a Vector DB
  • Урок 39. 00:09:26
    Adding Memory to the RAG System (Chat History)
  • Урок 40. 00:08:10
    Using a Custom Prompt