LangChain & LLMs - Build Autonomous AI Tools Masterclass

Set up your development environment with Python and VSCode, obtain an OpenAI API key, and securely manage credentials and billing while exploring the API playground.

Learn to install Python across Windows, Mac, and Linux by following a detailed online guide, verifying the installation with version commands, and optional customization.

Install and configure Visual Studio Code with the Python extension pack and environment manager to streamline Python development for building autonomous AI tools with the OpenAI API.

Explore what a large language model is, how it’s trained on vast textual data, and how transformers with self-attention generate natural, human-like text.

Explore the LangChain ecosystem from core packages and the LangChain expression language to Langraph, Langsmith, and Langserve, plus integration packages.

Explore LangChain building blocks: chains, agents, and modules, through hands-on installation, Python environments, and OpenAI integration, with documentation guiding retrieval chains, memory, and callbacks.

Explore how large language models are trained and how Lang Chain functions as a framework, then dive into the building blocks of Lang Chain.

Discover how LangChain prompt templates reduce long prompt complexity, leverage built-in prompts for summarizing, question answering, and APIs, and create chat prompts that inject variables at runtime.

Explore output parsers hands-on by building simple chains using string, json, and pedantic output parsers, and transition to structured output to extract typed data from LLM responses.

discover how to use langchain output parsers, including the pydantic output parser, to declare a python data model with a json schema and parse lms data with validation.

Learn to build robust data extraction with Pydantic and a pedantic parser to define a vacation data model, validate fields, and extract structured trip details from emails.

Explore LangChain concepts, including lcl and runnable chains, prompts, chat prompt templates, and output parsers, and learn to build a basic chain with a prompt, model, parser, and pipe operator.

Explore chain branching by building a conditional routing in LangChain chains using a classifier prompt and runnable branches, then test code vs general prompts with two LLM calls per request.

Explore memory in LangChain using the modern message history approach within a session store, comparing buffer, window, and summary strategies, and learn to persist chat history with SQLite.

Learn how LangChain document loaders convert raw files like pdf, text, html, and csv into document objects with content and metadata. Compare pypdfloader, textloader, web-based loader, and unstructured loader.

Explore document splitting with LangChain and LLMs, using pypdfloader to load a pdf and a recursive character text splitter to create chunked documents with metadata.