LangChain is a framework that makes it easier to build applications using large language models (LLMs) by connecting them with data, tools and APIs. It helps developers move beyond simple text generation and create intelligent workflows.
Basics
This section introduces LangChain and explains its purpose, core features, and main modules for building LLM-powered applications.
Prerequisites
This section covers the basic requirements for working with LangChain.
- Python Programming Basics
- APIs and Environment Setup
- Basics of Large Language Models (LLMs)
- Fundamentals of Prompt Engineering
Core Components
Here you’ll explore the essential parts of LangChain which plays a important role in building efficient, context-aware AI applications.
Working with Prompts
Prompts are the foundation of LLM interactions. In this section, you’ll learn about prompt templates and how to parse outputs effectively.
Building Chains and Agents
Chains helps connect multiple steps into structured workflows, while agents make decisions to build task-oriented, multi-step AI applications.
- Sequential Chains
- LLM Chains
- Retrieval Chains
- Custom Chains
- Agents and tools
- Building a Math Application with LangChain Agents
Memory
Memory makes applications more human-like by retaining context across conversations.
- Memory in LangChain
- Conversation Buffer Memory
- Conversation Buffer Window Memory
- Conversation Summary Memory
- Vector Store Memory
Indexes and Vector Stores
This section explains how to store and retrieve information using embeddings, indexing, vector databases and RAG.
- Indexing
- Inverted Index
- Vector Stores
- Popular Vector Stores: Pinecone, FAISS, ChromaDB, Qdrant, Milvus
- Retrieval Methods: k-NN, RAG, filtering, reranking
- Scaling & Evaluation: ANN, recall/precision, latency
Integrations
LangChain supports integrations with APIs, databases and external tools.
Ecosystem Tools
LangChain offers ecosystem tools like LangGraph for workflows, LangSmith for debugging and LCEL for easier development.
- LangChain Ecosystem
- What is LangGraph
- Difference Between LangChain and LangGraph
- What is LangSmith
- Debugging And Testing LLMs in LangSmith
- LangChain Expression Language (LCEL)
Practical Applications
This section focuses on real-world projects like:
- Knowledge Graphs
- Build RAG pipeline using Open Source Large Language Models
- Chatbot Webapp with LangChain
- Building a Basic PDF Summarizer LLM Application
- Building an AI application with LlamaIndex
- RAG(Retrieval-Augmented Generation) using LLama3
- Make your Documents Gen AI-ready
Advanced Topics
This part covers customizing agents, handling streaming responses, tracing and callbacks for LangChain apps.
