28 Feb What is Chaining in LangChain

Chaining in LangChain refers to the process of combining multiple components or steps into a sequence to accomplish a specific task.

Why Is Chaining Important?

Let us see why chaining is important:

• Modularity: You can mix and match components to create custom workflows.
• Context Awareness: Chaining allows you to build systems that use context from previous steps (e.g., retrieved documents, chat history).
• Scalability: Complex tasks can be broken down into smaller, manageable steps.

Examples of Chaining in LangChain

Let us see a quick example to understand the concept of chaining more on LangChain. Imagine building a Retrieval-Augmented Generation (RAG) system:

1. Retrieve: Use a retriever to fetch relevant documents from a vector store.
2. Generate: Pass the retrieved documents and a user’s question to an LLM to generate an answer.
3. Output: Return the answer to the user.

This sequence of steps is a chain that combines retrieval and generation into a single workflow.

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