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  1. Урок 1. 00:03:13
    Introduction
  2. Урок 2. 00:02:21
    Course Structure + How to get the best of Udemy [PLEASE DO NOT SKIP]
  3. Урок 3. 00:02:15
    What is LangChain?
  4. Урок 4. 00:01:52
    Course's Discord Server
  5. Урок 5. 00:06:11
    Project Setup (Pycharm) recommend)
  6. Урок 6. 00:08:29
    Project Setup (vscode) - optional
  7. Урок 7. 00:12:23
    Your First LangChain application - Chaining a simple prompt
  8. Урок 8. 00:00:39
    Quick Check In
  9. Урок 9. 00:01:03
    Ice Breaker- What are we building here?
  10. Урок 10. 00:12:55
    Integrating Linkedin Data Processing - Part 1 - Scraping
  11. Урок 11. 00:04:23
    Linkedin Data Processing - Part 2 - Agents Theory
  12. Урок 12. 00:05:24
    Linkedin Data Processing- Part 3: Tools, AgentType & initialize_agent
  13. Урок 13. 00:19:30
    Linkedin Data Processing- Part 4: Custom Agent Implementation & Testing
  14. Урок 14. 00:08:57
    [Optional] Twitter Data Processing- Part 1- Scraping
  15. Урок 15. 00:11:12
    [Optional] Twitter Data Processing- Part 2- Agents (Optional)
  16. Урок 16. 00:11:24
    Output Parsers- Getting Ready to work with a Frontend
  17. Урок 17. 00:06:35
    FullsStack App- Building our LLM powered by LangChain FullStack Application
  18. Урок 18. 00:01:52
    What are we building? ReAct AgentExecutor from scratch
  19. Урок 19. 00:05:36
    Environment Setup + ReAct Algorithm overview
  20. Урок 20. 00:10:48
    Defining Tools for our ReAct agent
  21. Урок 21. 00:14:59
    ReAct prompt, LLM Reasoning Engine, Output Parsing and Tool Execution
  22. Урок 22. 00:07:41
    AgentAction, AgentFinish, ReAct Loop
  23. Урок 23. 00:08:30
    CallbackHandlers, ReAct Prompt and finalizing the ReAct Agent loop
  24. Урок 24. 00:13:59
    Theoretical Introduction to embeddings, Vectorstores & RetrievalQA chain (RAG)
  25. Урок 25. 00:09:48
    LangChain classes review: Pinecone, OpenAIEmbeddings, RetrievalQA, TextLoader
  26. Урок 26. 00:04:07
    Medium Analyzer- Boilerplate Project Setup
  27. Урок 27. 00:12:56
    Medium Analyzer- Implementation
  28. Урок 28. 00:11:38
    Chat With Your PDF- FAISS Local Vectorstore
  29. Урок 29. 00:02:14
    What are we building?
  30. Урок 30. 00:18:11
    Building an AI LangChain Chat Assistant- Vectorstore Pincone Ingestion
  31. Урок 31. 00:13:17
    Building an AI LangChain Chat Assistant- RetrievalQA chain (prompt augmentation)
  32. Урок 32. 00:17:09
    Building an AI LangChain Chat Assistant- "Frontend" with Streamlit (UI)
  33. Урок 33. 00:11:43
    Building an AI LangChain Chat Assistant- Memory & ConversationalRetrievalChain
  34. Урок 34. 00:05:33
    What are we building? (A slim Version of GPT Code-Interpreter)
  35. Урок 35. 00:02:14
    Project Setup
  36. Урок 36. 00:08:27
    Python Agent
  37. Урок 37. 00:09:31
    CSV Agent
  38. Урок 38. 00:09:19
    Wrapping Everything: Router Agent + OpenAI functions
  39. Урок 39. 00:11:30
    LangChain Token Limitation Handeling Strategies
  40. Урок 40. 00:20:54
    LangChain Memory Deepdive
  41. Урок 41. 00:03:54
    The GIST of LLMs
  42. Урок 42. 00:02:56
    What is a Prompt? Composition of a formal prompt
  43. Урок 43. 00:02:43
    Zero Shot Prompting
  44. Урок 44. 00:08:27
    Few Shot Prompting
  45. Урок 45. 00:08:34
    Chain of Thought Prompting
  46. Урок 46. 00:07:19
    ReAct
  47. Урок 47. 00:09:00
    Prompt Engineering Quick Tips
  48. Урок 48. 00:02:11
    Have a technical issue? WATCH THIS FIRST. I Promise this will help!
  49. Урок 49. 00:05:50
    Tweet API- tweepy.errors.Forbidden: 403 Forbidden
  50. Урок 50. 00:08:34
    LLM Applications in Production
  51. Урок 51. 00:04:03
    LLM Application Development landscape
  52. Урок 52. 00:06:01
    Finished course? Whats next!
  53. Урок 53. 00:04:09
    LangChain Hub - Downloads prompt from the community
  54. Урок 54. 00:03:53
    TextSplitting Playground