About langchain
Langchain is a cutting-edge npm package designed to supercharge the development of applications using large language models (LLMs) by leveraging the power of composability. This innovative node module offers TypeScript bindings that make it incredibly easy for developers to integrate advanced language capabilities into their applications. With Langchain, developers can build more robust, scalable, and versatile language-based applications. The package is particularly beneficial for projects aiming to utilize the latest advancements in AI and natural language processing technologies, providing a structured and efficient way to harness the capabilities of LLMs.
For developers looking to get started with Langchain, the process is straightforward with the "npm install langchain" command. This simple installation procedure allows for immediate integration into existing projects, enabling developers to quickly begin constructing sophisticated applications that can understand, interpret, and manipulate human language. By using Langchain, developers can significantly reduce the complexity and time required to implement LLMs, making it an invaluable tool for anyone looking to innovate at the forefront of technology. The availability of TypeScript bindings further enhances this utility, ensuring type safety and improving the development experience by facilitating easier debugging and maintenance.
Langchain is supported by a robust suite of CI tools that ensure the reliability and stability of the package, as evidenced by its continuous integration status badge on GitHub. This reassurance is crucial for developers who need dependable tools that integrate seamlessly into professional development environments. With its focus on composability, Langchain empowers developers to piece together different functionalities of LLMs like building blocks, creating tailored solutions that can meet specific needs or explore new possibilities in the realm of language-driven technology.
Dependencies
Core dependencies of this npm package and its dev dependencies.
turbo, @tsconfig/recommended, @types/jest, @types/semver, commander, dotenv, lint-staged, prettier, semver, typescript
Documentation
A README file for the langchain code repository. View Code
π¦οΈπ LangChain.js
β‘ Building applications with LLMs through composability β‘
Looking for the Python version? Check out LangChain.
To help you ship LangChain apps to production faster, check out LangSmith. LangSmith is a unified developer platform for building, testing, and monitoring LLM applications. Fill out this form to get on the waitlist or speak with our sales team.
β‘οΈ Quick Install
You can use npm, yarn, or pnpm to install LangChain.js
npm install -S langchain or yarn add langchain or pnpm add langchain
import { ChatOpenAI } from "langchain/chat_models/openai";
π Supported Environments
LangChain is written in TypeScript and can be used in:
- Node.js (ESM and CommonJS) - 18.x, 19.x, 20.x
- Cloudflare Workers
- Vercel / Next.js (Browser, Serverless and Edge functions)
- Supabase Edge Functions
- Browser
- Deno
π€ What is 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 runtime for combining these components into chains and agents, and off-the-shelf implementations of chains and agents.
- LangChain Templates: (currently Python-only) A collection of easily deployable reference architectures for a wide variety of tasks.
- LangServe: (currently Python-only) 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.
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.
Integrations may also be split into their own compatible packages.
This library aims to assist in the development of those types of applications. Common examples of these applications include:
βQuestion Answering over specific documents
- Documentation
- End-to-end Example: Doc-Chatbot
π¬ Chatbots
- Documentation
- End-to-end Example: Chat-LangChain
π How does LangChain help?
The main value props of the LangChain libraries 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.
Components fall into the following modules:
π Model I/O:
This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.
π Retrieval:
Data Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples include summarization of long pieces of text and question/answering over specific data sources.
π€ Agents:
Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end-to-end agents.
π Documentation
Please see here for full documentation, which includes:
- Getting started: installation, setting up the environment, simple examples
- Overview of the interfaces, modules and integrations
- Use case walkthroughs and best practice guides
- Reference: full API docs
π Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see here.
Please report any security issues or concerns following our security guidelines.
ποΈ Relationship with Python LangChain
This is built to integrate as seamlessly as possible with the LangChain Python package. Specifically, this means all objects (prompts, LLMs, chains, etc) are designed in a way where they can be serialized and shared between languages.