LangChain archive

Developer study notes for building LLM applications with LangChain. 17 modules covering setup, models and prompts, chains, agents, tools and memory, then document loaders, embeddings, vector databases and RAG.

17 modules141 sections16 diagrams

Curriculum structure

FoundationsWhat LangChain is, how it is built and how to set it up

01Introduction to LangChain5 sections 02LangChain Architecture and Core Components5 sections 03Setting Up LangChain (Environment and Installation)8 sections

Models and promptsWorking with LLM providers and prompt templates

04LLM Models and Integrations7 sections 05Prompts and Prompt Templates8 sections 11LangChain with LLM Providers10 sections

Chains and memoryComposing steps and remembering the conversation

06Chains in LangChain8 sections 08Memory in LangChain8 sections

Agents and toolsLetting the LLM choose tools and act

09Tools in LangChain10 sections 07Agents in LangChain8 sections 10Agents in LangChain (Advanced)9 sections 17LangChain Chains, Agents and Tools9 sections

Retrieval and RAGLoading, splitting, embedding and searching your own data

14Document Loaders and Text Splitters9 sections 15Embeddings and Semantic Search11 sections 13LangChain with Vector Databases9 sections 12Retrieval Augmented Generation (RAG) in LangChain8 sections 16Retrieval-Augmented Generation (RAG) in LangChain9 sections

Topic threads

Getting started

From the big picture to a working install.

01Introduction to LangChain 02LangChain Architecture and Core Components 03Setting Up LangChain (Environment and Installation)

Models and providers

Pick a model, swap providers, shape the prompt.

04LLM Models and Integrations 11LangChain with LLM Providers 05Prompts and Prompt Templates

Chains

Fixed pipelines, from a basic chain to RAG chains.

06Chains in LangChain 08Memory in LangChain 17LangChain Chains, Agents and Tools

Agents and tools

Tools first, then the agents that use them.

09Tools in LangChain 07Agents in LangChain 10Agents in LangChain (Advanced)17LangChain Chains, Agents and Tools

The RAG pipeline

Load, split, embed, store, retrieve, answer.

14Document Loaders and Text Splitters 15Embeddings and Semantic Search 13LangChain with Vector Databases 12Retrieval Augmented Generation (RAG) in LangChain 16Retrieval-Augmented Generation (RAG) in LangChain