Unlock the Power of LLMs
Build a Chatbot with LangChain

Understanding LangChain's Core Interface

LangChain simplifies working with Large Language Models (LLMs) to build sophisticated applications, like conversational chatbots. Let's start by understanding LangChain's fundamental interface, which offers flexibility and control.

LangChain's interface provides three key methods for LLM interaction: `__call__` for single inputs, `batch` for bulk processing, and `stream` for real-time token output. These methods also support retries, fallbacks, schema validation, and runtime configuration, making them robust and versatile.

Hands-on LangChain Examples: Call, Batch, and Stream

Let's illustrate the usage of the `__call__`, `batch`, and `stream` methods with simple examples.

This demonstration highlights LangChain's ease of use for diverse interaction scenarios, from individual queries to high-throughput processing and real-time responses. The 'call' method is the core sync call and great for simple interactions.

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LangChain makes it incredibly easy to build applications powered by LLMs.

Sreeni Ramadorai

Building Your Own A Simple Chatbot Implementation

Now, let's construct a conversational chatbot using LangChain, processing user queries with an LLM and returning concise answers. Below is a complete example.

This chatbot is designed to be interactive, accepting user queries until they choose to exit. It uses a prompt template for concise and user-friendly answers. Key features are enhanced flexibility, simplified logic, and leveraging LangChain's methods.

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The @chain decorator simplifies development by automatically inheriting the interface's methods.

LangChain Documentation

The Power of the Decorator in LangChain

The `@chain` decorator is a crucial element, simplifying development by automatically inheriting the interface's methods (`__call__`, `batch`, and `stream`) for any decorated function.

Using `@chain` offers enhanced flexibility, simplified logic, and built-in capabilities of the LangChain interface. It allows you to focus on the chatbot's core logic.