LangChain Chains
Introduction
Chains are a core building block of LangChain applications that allow you to compose multiple components together into a sequence of operations. Chains make it easy to build complex, modular applications by chaining together calls to different components like LLMs, data sources, and other chains.
Key Concepts
Modularity
Chains compose multiple components together into a sequence of operations. This modular design makes it easy to build complex applications in a structured way.
Statefulness
Chains can be initialized with a Memory object, which will persist data across calls to the chain. This makes a Chain stateful.
Debugging
It can be hard to debug a Chain object solely from its output as most Chain objects involve a fair amount of input prompt preprocessing and LLM output post-processing. See the debugging guide for tips on debugging chains.
Asynchronicity
LangChain provides async support for Chains by leveraging the asyncio library. This allows you to run chains asynchronously to improve performance.
How To Guides
Beginner
Using LLMChain: The basic building block chain.
Adding memory: Make your chain stateful.
Intermediate
Debugging chains: Tips for debugging chains.
Custom chains: Implement your own custom chain.
Advanced
Async chains: Run your chains asynchronously.
OpenAI functions: Incorporate OpenAI function APIs into chains.
Summary
Chains are a powerful paradigm that enables complex applications to be built in a modular way. Key concepts include statefulness, debugging, asynchronicity, and modularity. The how-to guides provide walkthroughs for working with chains from basic to advanced usage.