LangChain for Beginners (OpenAI / LLMOps)
Learn how to build LLM-powered Python applications with LangChain and OpenAI, with core concepts like chains, agents, and memory.
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TECHNOLOGIES
WATCH TIME
67 minutes
Lessons
Calling Prompts with LLMs
Learn how to interact with language models by sending prompts and interpreting responses.
4 min
Storing and Retrieving Chat History
Learn techniques to save and retrieve past interactions, improving your chatbot's performance and user experience.
7 min
Memory and Chat Bots
Learn about the importance of memory for chatbots and how to implement it for more contextual conversations through a terminal-based chat bot.
7 min
Human as a Tool
Learn how to integrate human inputs and decisions into the LLM thought process, leveraging human strengths.
3 min
Prompt Templating and Chaining
Learn to structure and sequence your prompts for more complex and controlled model responses.
6 min
Using Different Large Language Models
Learn to explore, compare, and select among different Language Learning Models (LLMs) for various tasks from HuggingFace.
5 min
Simple Sequential Chains
Learn how to chain together multiple LLM chains, taking the output of one chain as the input of the next via simple sequential chains.
6 min
Getting Started with LangChain
Learn about LangChain, LLMs, getting your OpenAI access token and setting up your coding environment.
4 min
Plan and Execute Agents
Learn about another type of LangChain agent, the plan and execute agent and how it plans its steps first and then executes each one.
9 min
Action Agents and Tools
Learn how to create dynamic AI agents that can perform actions, react, and interact with real-world information.
7 min
Document Loading and Q&A Retrieval
Learn how to load and process documents with DocumentLoaders for deeper information extraction and interaction, as well as using Retrieval chains.
13 min