Introduction to LangChain Chains
Understand the concept of chains in LangChain and how they facilitate multi-step operations with LLMs.
What are LangChain Chains?
Welcome to LangChain Chains! Imagine you have a complex task for an AI, like writing a blog post or summarizing a long document. A single command to a Large Language Model (LLM) might not be enough.
This is where 'Chains' come in. They help you break down complex AI tasks into smaller, manageable steps, executed in a specific order. Think of them as a blueprint for AI workflows.
Why We Need Chains
An LLM is powerful, but for multi-step problems or interactions requiring specific formatting, you need more structure. Chains provide this structure by:
- Enabling multi-step reasoning: Allowing the LLM to process information iteratively.
- Connecting components: Linking LLMs with prompts, output parsers, or other tools.
- Building structured workflows: Ensuring tasks are performed in a predefined sequence, making complex applications manageable.
All lessons in this course
- Introduction to LangChain Chains
- Sequential & Simple Chains
- Customizing Chain Logic
- Routing and Conditional Chains