LangChain Runnables

This repository demonstrates the use of LangChain Runnables, a flexible abstraction that makes it easy to compose, chain, and manage the execution of different components within the LangChain ecosystem.

The project includes examples of five core Runnables:

1. RunnableSequence – Run multiple steps in a defined order.
2. RunnableParallel – Execute tasks concurrently.
3. RunnablePassthrough – Pass input directly through the chain.
4. RunnableLambda – Wrap Python functions into reusable runnables.
5. RunnableBranch – Define conditional execution paths.

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🚀 Runnables Overview

1. RunnableSequence

• What it does: Chains multiple steps together in a sequence.
• Use case: Building structured multi-step pipelines like document retrieval → summarization → Q&A.

2. RunnableParallel

• What it does: Runs multiple tasks simultaneously and merges results.
• Use case: Performing sentiment analysis and keyword extraction at the same time.

3. RunnablePassthrough

• What it does: Passes inputs unchanged through the chain (helpful for debugging or data forwarding).
• Use case: Retaining the original prompt alongside model outputs.

4. RunnableLambda

• What it does: Wraps any Python function as a runnable.
• Use case: Adding lightweight transformations such as text cleaning, parsing, or validation.

5. RunnableBranch

• What it does: Directs execution down different branches depending on conditions.
• Use case: Choosing between summarization for short text vs. chunking for long text.