LangChain - LLM Frameworks

1. Introduction

LangChain is an innovative framework designed to facilitate the development of applications using large language models (LLMs). It provides a structured approach to building LLM-powered applications by offering various components that can be easily integrated.

2. Key Concepts

• Chains: Sequences of calls to LLMs or other components.
• Agents: Components that can make decisions based on input and output.
• Memory: Mechanisms to store and recall information across interactions.

3. Installation

To install LangChain, run the following command:

pip install langchain

4. Basic Usage

The basic usage of LangChain involves creating a simple chain:

from langchain import LLM, Chain

llm = LLM(model="gpt-3.5-turbo")
chain = Chain(llm=llm)

response = chain.run("What is LangChain?")
print(response)

5. Advanced Features

LangChain also supports advanced features such as:

• Custom agents for dynamic decision-making.
• Memory integration for context-aware applications.
• Multiple LLM support for diverse application needs.

6. Best Practices

When using LangChain, consider the following best practices:

• Use chain modularity to simplify debugging.
• Implement logging to track model performance.
• Regularly update your models to harness improvements.

7. FAQ