What is @langchain/openai?
@langchain/openai is an npm package that provides tools and utilities for integrating OpenAI's language models into your applications. It allows you to easily interact with OpenAI's API, enabling functionalities such as text generation, conversation handling, and more.
What are @langchain/openai's main functionalities?
Text Generation
This feature allows you to generate text based on a given prompt using OpenAI's language models. The code sample demonstrates how to use the `generate` method to create text.
const { OpenAI } = require('@langchain/openai');
const openai = new OpenAI('your-api-key');
async function generateText(prompt) {
const response = await openai.generate({
model: 'text-davinci-003',
prompt: prompt,
max_tokens: 100
});
console.log(response.data.choices[0].text);
}
generateText('Once upon a time');
Conversation Handling
This feature allows you to handle conversations by sending a series of messages to OpenAI's conversational models. The code sample demonstrates how to use the `converse` method to manage a conversation.
const { OpenAI } = require('@langchain/openai');
const openai = new OpenAI('your-api-key');
async function handleConversation(messages) {
const response = await openai.converse({
model: 'gpt-3.5-turbo',
messages: messages
});
console.log(response.data.choices[0].message.content);
}
const messages = [
{ role: 'system', content: 'You are a helpful assistant.' },
{ role: 'user', content: 'What is the weather like today?' }
];
handleConversation(messages);
Custom Fine-Tuning
This feature allows you to fine-tune OpenAI's models with custom training data. The code sample demonstrates how to use the `fineTune` method to train a model with specific data.
const { OpenAI } = require('@langchain/openai');
const openai = new OpenAI('your-api-key');
async function fineTuneModel(trainingData) {
const response = await openai.fineTune({
model: 'text-davinci-003',
training_data: trainingData
});
console.log(response.data);
}
const trainingData = [
{ prompt: 'Translate the following English text to French: "Hello, world!"', completion: 'Bonjour, le monde!' }
];
fineTuneModel(trainingData);
Other packages similar to @langchain/openai
openai
The `openai` npm package is the official OpenAI API client for Node.js. It provides similar functionalities for interacting with OpenAI's models, including text generation, conversation handling, and more. Compared to @langchain/openai, it is the direct client provided by OpenAI and may have more up-to-date features and support.
node-openai
The `node-openai` package is a community-maintained client for OpenAI's API. It provides basic functionalities for text generation and other interactions with OpenAI's models. It may lack some of the advanced features and optimizations found in @langchain/openai.
@langchain/openai
This package contains the LangChain.js integrations for OpenAI through their SDK.
Installation
npm install @langchain/openai
This package, along with the main LangChain package, depends on @langchain/core
.
If you are using this package with other LangChain packages, you should make sure that all of the packages depend on the same instance of @langchain/core.
You can do so by adding appropriate fields to your project's package.json
like this:
{
"name": "your-project",
"version": "0.0.0",
"dependencies": {
"@langchain/openai": "^0.0.9",
"langchain": "0.0.207"
},
"resolutions": {
"@langchain/core": "0.1.5"
},
"overrides": {
"@langchain/core": "0.1.5"
},
"pnpm": {
"overrides": {
"@langchain/core": "0.1.5"
}
}
}
The field you need depends on the package manager you're using, but we recommend adding a field for the common yarn
, npm
, and pnpm
to maximize compatibility.
Chat Models
This package contains the ChatOpenAI
class, which is the recommended way to interface with the OpenAI series of models.
To use, install the requirements, and configure your environment.
export OPENAI_API_KEY=your-api-key
Then initialize
import { ChatOpenAI } from "@langchain/openai";
const model = new ChatOpenAI({
apiKey: process.env.OPENAI_API_KEY,
modelName: "gpt-4-1106-preview",
});
const response = await model.invoke(new HumanMessage("Hello world!"));
Streaming
import { ChatOpenAI } from "@langchain/openai";
const model = new ChatOpenAI({
apiKey: process.env.OPENAI_API_KEY,
modelName: "gpt-4-1106-preview",
});
const response = await model.stream(new HumanMessage("Hello world!"));
Embeddings
This package also adds support for OpenAI's embeddings model.
import { OpenAIEmbeddings } from "@langchain/openai";
const embeddings = new OpenAIEmbeddings({
apiKey: process.env.OPENAI_API_KEY,
});
const res = await embeddings.embedQuery("Hello world");
Development
To develop the OpenAI package, you'll need to follow these instructions:
Install dependencies
yarn install
Build the package
yarn build
Or from the repo root:
yarn build --filter=@langchain/openai
Run tests
Test files should live within a tests/
file in the src/
folder. Unit tests should end in .test.ts
and integration tests should
end in .int.test.ts
:
$ yarn test
$ yarn test:int
Lint & Format
Run the linter & formatter to ensure your code is up to standard:
yarn lint && yarn format
Adding new entrypoints
If you add a new file to be exported, either import & re-export from src/index.ts
, or add it to scripts/create-entrypoints.js
and run yarn build
to generate the new entrypoint.