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Премиум
  • Урок 1. 00:01:15
    How to Get Help
  • Урок 2. 00:03:58
    What is LangChain?
  • Урок 3. 00:10:00
    How a Typical AI-Enabled App Works
  • Урок 4. 00:05:29
    Here It Is, This is Why We Use LangChain
  • Урок 5. 00:03:21
    Project Overview and Setup
  • Урок 6. 00:02:56
    Using LangChain the Simple Way
  • Урок 7. 00:10:09
    Introducing Chains
  • Урок 8. 00:04:11
    Adding a Chain
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    Parsing Command Line Arguments
  • Урок 10. 00:04:45
    Securing the API Key
  • Урок 11. 00:02:57
    Connecting Chains Together
  • Урок 12. 00:07:01
    Chains in Series with SequentialChain
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    App Overview
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    Receiving User Input
  • Урок 15. 00:10:10
    Chat vs Completion Style Models
  • Урок 16. 00:06:02
    Representing Messages with ChatPromptTemplates
  • Урок 17. 00:04:38
    Implementing a Chat Chain
  • Урок 18. 00:09:26
    Understanding Memory
  • Урок 19. 00:07:28
    Using ChatBufferMemory to Store Conversations
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    Saving and Extending Conversations
  • Урок 21. 00:09:43
    Summarizations Conversation Summary Memory
  • Урок 22. 00:03:29
    Project Overview
  • Урок 23. 00:01:54
    Project Setup
  • Урок 24. 00:06:15
    Loading Files with Document Loaders
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    Search Criteria
  • Урок 26. 00:10:32
    Introducing Embeddings
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    The Entire Embedding Flow
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    Chunking Text
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    Generating Embeddings
  • Урок 30. 00:10:02
    Introducing ChromaDB
  • Урок 31. 00:10:33
    Building a Retrieval Chain
  • Урок 32. 00:05:21
    What is a Retriever?
  • Урок 33. 00:28:09
    [Optional] Understanding Refine, MapReduce, and MapRerank
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    Removing Duplicate Documents
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    Creating a Custom Retriever
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    Custom Retriever in Action
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    Visualizing Embeddings
  • Урок 38. 00:04:14
    App Overview
  • Урок 39. 00:08:13
    Understanding Tools
  • Урок 40. 00:10:55
    Understanding ChatGPT Functions
  • Урок 41. 00:06:36
    Defining a Tool
  • Урок 42. 00:05:52
    Defining an Agent and AgentExecutor
  • Урок 43. 00:09:14
    Understanding Agents and AgentExecutors
  • Урок 44. 00:04:45
    Shortcomings in ChatGPT's Assumptions
  • Урок 45. 00:04:28
    Recovering from Errors in Tools
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    Adding Table Context
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    Adding a Table Description Tool
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    Being Direct with System Messages
  • Урок 49. 00:06:59
    Adding Better Descriptions for Tool Arguments
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    Tools with Multiple Arguments
  • Урок 51. 00:09:25
    Memory vs Agent Scratchpad
  • Урок 52. 00:02:38
    Preserving Messages with Agent Executor
  • Урок 53. 00:04:47
    Understanding Callbacks
  • Урок 54. 00:05:04
    Implementing a Basic Callback Handler
  • Урок 55. 00:11:23
    More Handler Implementaion
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    App Overview
  • Урок 57. 00:03:24
    Taking a Look at Mockups
  • Урок 58. 00:04:44
    Boilerplate Setup
  • Урок 59. 00:06:10
    How This App is Designed
  • Урок 60. 00:04:29
    Outlining the First Feature
  • Урок 61. 00:03:41
    Loading and Splitting From a PDF
  • Урок 62. 00:02:17
    Testing the PDF Upload
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    Introducing Pinecone
  • Урок 64. 00:05:54
    Initializing the Pinecone Client
  • Урок 65. 00:03:52
    Adding Documents to the Vector Store
  • Урок 66. 00:06:11
    Why is Processing Taking Forever?
  • Урок 67. 00:07:45
    Introducing Background Jobs
  • Урок 68. 00:01:56
    Redis Setup
  • Урок 69. 00:04:09
    Adding in the Worker
  • Урок 70. 00:04:04
    Queuing Up Jobs
  • Урок 71. 00:07:08
    Updating Document Metadata
  • Урок 72. 00:07:59
    Understanding the Apps Requirements
  • Урок 73. 00:12:09
    Persistent Message Storage
  • Урок 74. 00:10:36
    Introducing the Conversational Retrieval Chain
  • Урок 75. 00:04:57
    Building the Retriever
  • Урок 76. 00:04:44
    Custom History Objects
  • Урок 77. 00:08:53
    Building a Custom SQL History
  • Урок 78. 00:04:59
    Testing the Chain
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    Streaming Text Generation
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    Creating a Working Playground
  • Урок 81. 00:09:11
    Experimenting with a Streaming Language Model
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    Chains Don't Want to Stream
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    Receiving Chunks with a Callback
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    Extending a LLM Chain
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    Adding a Queue for Communication
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    The Chain Really Wants to Wait
  • Урок 87. 00:02:45
    Solving the Slow Chain
  • Урок 88. 00:02:41
    It Works!
  • Урок 89. 00:04:59
    Ending the Loop
  • Урок 90. 00:03:37
    Isolating the Queue and Handler
  • Урок 91. 00:04:47
    Using a Mixin Approach
  • Урок 92. 00:06:59
    Integrating the Streaming Code
  • Урок 93. 00:07:07
    Testing the Streaming Setup
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    Here's the Issue
  • Урок 95. 00:07:50
    Isolating the Handler
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    Streaming Complete!
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    Random Component Parts
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    Component Part Flow
  • Урок 99. 00:06:14
    Partial KWArg Application
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    Building Component Maps
  • Урок 101. 00:08:02
    Randomly Picking a Component
  • Урок 102. 00:10:09
    Generalizing Component Picking
  • Урок 103. 00:05:16
    Collecting User Feedback
  • Урок 104. 00:06:52
    Redis Connection Setup
  • Урок 105. 00:07:35
    Storing Votes in Redis
  • Урок 106. 00:03:03
    Weighted Randomness
  • Урок 107. 00:06:31
    Extracting Scores
  • Урок 108. 00:07:33
    Calculating the Average Score
  • Урок 109. 00:04:38
    Selecting Components By Score
  • Урок 110. 00:02:45
    Adding Score Observability
  • Урок 111. 00:03:50
    Building the Score Aggregate
  • Урок 112. 00:02:37
    Adding Another Form of Memory
  • Урок 113. 00:06:10
    Window Memory Implementation
  • Урок 114. 00:04:32
    Text Generation Tracing
  • Урок 115. 00:03:28
    Langfuse Signup
  • Урок 116. 00:06:50
    Adding in Tracing
  • Урок 117. 00:05:27
    Understanding the Trace
  • Урок 118. 00:10:32
    Automatic Trace Creation