1 - Introduction to the Course (62.34 MB)
2 - Business Applications of LangChain (37.34 MB)
3 - What Makes LangChain Powerful (18.3 MB)
4 - What Does the Course Cover (28.46 MB)
63 - Introduction to Reasoning Chatbots (13.14 MB)
64 - Tools Toolkits Agents and Agent Executors (29.59 MB)
66 - Creating a Wikipedia Tool and Piping It to a Chain (65.32 MB)
67 - Creating a Retriever and a Custom Tool (53.06 MB)
68 - LangChain Hub (49.27 MB)
69 - Creating a Tool Calling Agent and an Agent Executor (95.97 MB)
70 - AgentAction and AgentFinish (53.24 MB)
7 - Setting Up a Custom Anaconda Environment for Jupyter Integration (18.9 MB)
8 - Obtaining an OpenAI API Key (10.75 MB)
15 - ChatOpenAI (69.58 MB)
16 - System and Human Messages (59.34 MB)
17 - AI Messages (50 MB)
18 - Prompt Templates and Prompt Values (38.87 MB)
19 - Chat Prompt Templates and Chat Prompt Values (82.45 MB)
20 - FewShot Chat Message Prompt Templates (82.64 MB)
21 - LLMChain (27.81 MB)
22 - Chat Message History (57.39 MB)
23 - Conversation Buffer Memory Implementing the Setup (21.06 MB)
24 - Conversation Buffer Memory Configuring the Chain (91.26 MB)
25 - Conversation Buffer Window Memory (65.76 MB)
26 - Conversation Summary Memory (83.09 MB)
27 - Combined Memory (72.67 MB)
28 - String Output Parser (23.42 MB)
29 - CommaSeparated List Output Parser (35.27 MB)
30 - Datetime Output Parser (37.64 MB)
31 - Piping a Prompt Model and an Output Parser (65.83 MB)
32 - Batching (65.83 MB)
33 - Streaming (39.02 MB)
34 - The Runnable and RunnableSequence Classes (78.25 MB)
35 - Piping Chains and the RunnablePassthrough Class (64.33 MB)
36 - Graphing Runnables (15.87 MB)
37 - RunnableParallel (53.64 MB)
38 - Piping a RunnableParallel with Other Runnables (88.98 MB)
39 - RunnableLambda (24.65 MB)
40 - The chain Decorator (30.82 MB)
41 - Adding Memory to a Chain Part 1 Implementing the Setup (36.11 MB)
42 - RunnablePassthrough with Additional Keys (54.18 MB)
43 - Itemgetter (34.37 MB)
44 - Adding Memory to a Chain Part 2 Creating the Chain (105.03 MB)
45 - How to Integrate Custom Data into an LLM (16.53 MB)
46 - Introduction to RAG (19.86 MB)
47 - Introduction to Document Loading and Splitting (15.97 MB)
48 - Introduction to Document Embedding (35.82 MB)
49 - Introduction to Document Storing Retrieval and Generation (21.07 MB)
50 - Indexing Document Loading with PyPDFLoader (116.33 MB)
51 - Indexing Document Loading with Docx2txtLoader (33.62 MB)
52 - Indexing Document Splitting with Character Text Splitter Theory (9.61 MB)
53 - Indexing Document Splitting with Character Text Splitter Code Along (57.21 MB)
54 - Indexing Document Splitting with Markdown Header Text Splitter (86.44 MB)
55 - Indexing Text Embedding with OpenAI (82.86 MB)
56 - Indexing Creating a Chroma Vector Store (55.89 MB)
57 - Indexing Inspecting and Managing Documents in a Vector Store (53.51 MB)
58 - Retrieval Similarity Search (91.47 MB)
59 - Retrieval Maximal Marginal Relevance Search (141.15 MB)
60 - Retrieval Vector StoreBacked Retriever (48.92 MB)
61 - Generation Stuffing Documents (54.69 MB)
62 - Generation Generating a Response (77.13 MB)