A highlevel Intro to Langchain

LangChain is a framework for developing applications powered by language models. It enables applications that:

Are context-aware: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc.)

Reason: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc.)

This framework consists of several parts.

LangChain Libraries: The Python and JavaScript libraries. Contains interfaces and integrations for a myriad of components, a basic run time for combining these components into chains and agents, and off-the-shelf implementations of chains and agents.

LangChain Templates: A collection of easily deployable reference architectures for a wide variety of tasks.

LangServe: A library for deploying LangChain chains as a REST API.

LangSmith: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.

Together, these products simplify the entire application lifecycle:

Develop: Write your applications in LangChain/LangChain.js. Hit the ground running using Templates for reference.

Productionize: Use LangSmith to inspect, test and monitor your chains, so that you can constantly improve and deploy with confidence.

Deploy: Turn any chain into an API with LangServe.

LangChain Libraries

The main value props of the LangChain packages are:

Components: composable tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not

Off-the-shelf chains: built-in assemblages of components for accomplishing higher-level tasks

Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones.

The LangChain libraries themselves are made up of several different packages.

langchain-core: Base abstractions and LangChain Expression Language.

langchain-community: Third party integrations.

langchain: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.

References:

https://python.langchain.com/docs/get_started/introduction