Introduction to LangChain

• LangChain is an open-source framework designed to build applications with Large Language Models (LLMs).
• It provides tools, abstractions and modules to connect LLMs with external data, tools and real-world actions.
• It simplifies the development of complex LLM applications like chatbots, agents, RAG systems and document analysis.
• LangChain is language-agnostic but most popular with Python.

• Provides high-level abstractions for LLM apps.
• Easy integration with multiple LLM providers (OpenAI, Anthropic, Google, etc.).
• Built-in support for document loaders, text splitters, vector stores and tools.
• Helps in building production-ready applications.
• Modular and extensible architecture.
• Large community and active development.
• Useful for building RAG, agents, chatbots and multi-step workflows.

The framework also reaches out to external data such as PDFs, databases and APIs.

1

Models

LLMs and chat models.

2

Prompts

Templates to create structured inputs.

3

Chains

Sequence of operations using LLMs.

4

Agents

Use tools to perform actions.

5

Memory

Maintain conversation history.

6

Document Loaders

Load data from various sources.

7

Text Splitters

Split large documents.

8

Vector Stores

Store and search embeddings.

9

Retrievers

Fetch relevant documents.

10

Tools

Integrate with external APIs, DBs, etc.

11

Callbacks

Track and monitor token usage.

12

Output Parsers

Structure the LLM output.