langchain

What is langchain?

The langchain npm package is designed to facilitate the development of applications that leverage language models. It provides tools for chaining together different language model operations, managing prompts, and integrating with various data sources.

What are langchain's main functionalities?

Prompt Management

This feature allows you to create and manage prompts easily. You can define templates and format them with dynamic data.

const { PromptTemplate } = require('langchain');

const template = new PromptTemplate('Translate the following text to French: {text}');
const prompt = template.format({ text: 'Hello, how are you?' });
console.log(prompt); // Output: Translate the following text to French: Hello, how are you?

Chaining Operations

This feature allows you to chain together multiple operations, where the output of one step becomes the input to the next.

const { Chain } = require('langchain');

const chain = new Chain();
chain.addStep(async (input) => `Step 1: ${input}`);
chain.addStep(async (input) => `Step 2: ${input}`);

chain.run('Initial Input').then(console.log); // Output: Step 2: Step 1: Initial Input

Integration with Data Sources

This feature allows you to integrate with various data sources, making it easy to fetch and use data within your language model operations.

const { DataSource } = require('langchain');

const dataSource = new DataSource('https://api.example.com/data');
dataSource.fetch().then(data => console.log(data));

Other packages similar to langchain

openai

The openai npm package provides a simple interface to interact with OpenAI's GPT-3 and other models. While it focuses on direct interaction with OpenAI's API, langchain offers more advanced features like prompt management and chaining operations.

node-nlp

The node-nlp package is a natural language processing library for Node.js. It provides tools for entity extraction, sentiment analysis, and more. While it offers a broad range of NLP functionalities, langchain is more specialized in chaining language model operations and managing prompts.

compromise

Compromise is a lightweight NLP library for Node.js. It focuses on text processing and manipulation. Compared to langchain, compromise is more about text analysis and less about chaining language model operations or managing prompts.

README

🦜️🔗 LangChain.js

⚡ Building applications with LLMs through composability ⚡

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 off the waitlist or speak with our sales team.

⚡️ 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";

🌐 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

🤔 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:

❓Question Answering over specific documents

• Documentation
• End-to-end Example: Doc-Chatbot

💬 Chatbots

• Documentation
• End-to-end Example: Chat-LangChain

🚀 How does LangChain help?

The main value props of the LangChain libraries are:

1. 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
2. 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:

📃 Model I/O:

This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.

📚 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.

🤖 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.

📖 Documentation

Please see here for full documentation, which includes:

• Getting started: installation, setting up the environment, simple examples
• Tutorials: interactive guides and walkthroughs of common use cases/tasks.
• Use case walkthroughs and best practices for every component of the LangChain library.
• Reference: full API docs

💁 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.

🖇️ 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.