Building Blocks of LangChain

Previously, we provided an overview of LangChain. In this article, we’ll dive into LangChain’s architecture and explore its six core building blocks. Acting as middleware, LangChain connects your application to Large Language Models (LLMs), vector stores, embedding models, and other data sources—providing abstractions that simplify integration and accelerate development. Below is a high-level diagram illustrating these components:

Component	Purpose	Examples
Model I/O	Manages prompt formatting, response parsing, and streaming	OpenAI, Anthropic, Hugging Face
Memory	Persists conversational context or state	Redis, in-memory cache
Retrieval	Retrieves relevant documents or embeddings	Pinecone, FAISS, Weaviate
Agents	Orchestrates decision-making across tools and APIs	Custom toolkits, action chains
Embeddings	Converts text into vectors for similarity search	OpenAI Embeddings, Cohere
External Data	Integrates external knowledge sources (databases, APIs)	SQL/NoSQL, RESTful APIs

Each building block plays a vital role in crafting production-grade applications with LangChain. In the sections that follow, we’ll examine each component in detail and show you how to leverage them effectively.

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Key Use Cases

Model IO

Introduction

Overview of LangChain

Key Components of LangChain

Interacting with LLMs

Tips, Tricks, and Resources

Introduction to LCEL

Adding Memory to LLM Apps

Performing Retrieval

Implementing Chains

Using Tools

Building Agents

Conclusion

Building Blocks of LLM Apps

Building Blocks of LangChain

Watch Video

Introduction

Overview of LangChain

Key Components of LangChain

Interacting with LLMs

Tips, Tricks, and Resources

Introduction to LCEL

Adding Memory to LLM Apps

Performing Retrieval

Implementing Chains

Using Tools

Building Agents

Conclusion

Building Blocks of LLM Apps

Documentation Index

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