The Complete LangChain & RAG Developer Course 2026

Learn to generate an OpenAI API key by logging into platform.openai.com and creating a new secret key in the dashboard.

Set up the base structure for RAG course projects by creating a dedicated folder, managing dependencies with a requirements.txt, and configuring a virtual environment for secure LLM and retriever experimentation.

Hands-on pdf extraction with LangChain: load a local pdf via pypdf loader, inspect page content and metadata, and print document details within a ragenv environment.

Load and extract content from .docx files using LangChain's docx-to-text loader. Leverage structured headings and metadata to improve chunking and retrieval in a RAG workflow, with a hands-on Word demo.

Tackle a real-world docx integration project using Python docx and a LangChain document loader, loading a 15-page file, exploring page-by-page reading, chunking, and future CSV reading.

Explore how structured CSV data fits into a rac system, load csv files with LangChain's csv loader, and apply row-based or column-based chunking for tabular data.

Learn why chunking is critical for rag systems and how LangChain splits large documents into chunks that fit the context window for efficient embedding, storage, and retrieval using different strategies.

Master LangChain text splitters by using CharacterTextSplitter to control chunk size and overlap, install LangChain, and run a hands-on example that preserves context.

Compare fixed size, recursive, token-based, and semantic chunking strategies. Recursive chunking preserves context with a paragraph-to-sentence hierarchy and improves retrieval; start here for RAG, then optimize.

Explore recursive character text splitting in LangChain with hands-on, industry-standard chunking, configuring chunk size, overlap, and separators to split by paragraphs, lines, and sentences for RAG workflows.

Explore embeddings, the means by which text becomes meaning-aware vectors in retrieval augmented generation, enabling semantic search and retrieving the most relevant documents.

Explore how embeddings become vectors and use cosine similarity to measure angle-based similarity. Learn how vector databases power semantic search by returning closest stored vectors to a query.

Explore embedding models in LangChain, switch OpenAI and Hugging Face with one line of code, and learn how different vector dimensions affect accuracy and local use.

Explore vector databases that store embeddings and enable fast approximate nearest neighbor search for near matches in chatbots, search engines, and rag pipelines, including FACE and CHROMA.

Build a FAISS-backed vector store with faze, using OpenAI embeddings and recursive character text splitter to create chunks, index them, and run similarity searches.

Explore Chroma, a modern vector database for AI applications, and learn to create documents, generate embeddings, and retrieve semantically similar documents with built-in persistence for production use.

Understand runnables, the building blocks of LangChain pipelines, and see how prompts, LLMs, and Python functions convert inputs into outputs through a pipe operator and a readable, composable pipeline.

Discover runnable parallel to run multiple steps simultaneously and combine outputs in a dictionary for efficient rag pipelines using runnable lambda and runnable parallel.

Chain runnable functions with the pipe operator in LangChain to build powerful sequences, using runnable lambda and chain invoke for add one and square operations.

Combine document loaders, text splitters, embeddings, and phase vector store with lang chain runnables. Build a research paper qa assistant using retrieval augmented generation that answers only from the paper.

Load a real document into a LangChain and RAG pipeline by using a pypdf loader on attention.pdf and inspect its content and metadata.

Chunk documents into configurable pieces to boost retrieval accuracy and handle token limits. Utilize langchain's recursive character text splitter to set chunk size and overlap for semantic search.

Create embeddings from documents using OpenAI embeddings and store them in a face vector store. Observe that embeddings map text to high-dimensional coordinates reflecting semantic similarity.

Build a semantic retriever using a vector store to fetch top-k relevant documents, leveraging chunks and embeddings in a LangChain and RAG workflow.

Orchestrate a runnable parallel chain to process the original question while retrieving context in parallel. Then join retrieved chunks into a single context string for downstream use.

Assemble a runnable parallel chain to pass a question through the retriever, collect document page content, and join them into a context for q_chain.invoke, illustrating retrieval and transformer behavior.

Design a prompt that reduces hallucinations and improves answer quality, guiding an AI research assistant to stay faithful to academic paper terminology with clear input variables and a prompt template.

Structure model outputs with pydantic models and a pydantic output parser to produce a transformer format with question and answers, each answer containing text and context.

Build and connect a q chain that accepts a string, sends it to prompt, routes to an LLM (chat open ai, GPT-3.5 turbo), and parses output into question and answers.

Run, test, and validate your AI application using a RAG pipeline in LangChain; see how attention-based transformers leverage global context and retrieval to produce grounded, context-rich answers.