About @langchain/openai
The "@langchain/openai" npm package is a crucial tool designed to enhance the capabilities of developers working with large language models (LLMs) through LangChain.js. This node module provides seamless OpenAI integrations, allowing developers to harness the power of advanced artificial intelligence within their applications. This integration facilitates a variety of AI-driven features, such as natural language understanding, text generation, and complex decision-making processes. By utilizing "@langchain/openai", developers can significantly streamline the implementation process, making it easier to build sophisticated, AI-powered applications that are both efficient and scalable.
To get started with this powerful integration, developers simply need to run the command 'npm install @langchain/openai' in their project's terminal. This command installs the package and all necessary dependencies, integrating smoothly with the existing LangChain.js setup. Once installed, developers can immediately begin to implement various OpenAI features directly into their applications. The package not only simplifies the process of adding complex AI functionalities but also ensures that applications remain robust and maintain high performance. By reducing the barrier to entry for working with large language models, "@langchain/openai" empowers developers to create more dynamic and intelligent applications.
The benefits of using "@langchain/openai" extend beyond simple integration. This package supports the rapid development of AI applications by providing tools that are both flexible and powerful. Developers can leverage a suite of functionalities to enhance application interaction, automate responses, and analyze large volumes of text efficiently. Furthermore, the continuous updates and support from the LangChain community ensure that the "@langchain/openai" package remains on the cutting edge of technology, providing users with access to the latest advancements in AI research and application development. This makes "@langchain/openai" an indispensable tool for developers looking to push the boundaries of what's possible with artificial intelligence in modern applications.
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/openai 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.