create-langchain-integration

@langchain/<ADD_PACKAGE_NAME_HERE>

This package contains the LangChain.js integrations for <ADD_NAME_HERE> through their SDK.

Installation

npm install @langchain/<ADD_PACKAGE_NAME_HERE> @langchain/core

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 field to your project's package.json like this:

{
  "name": "your-project",
  "version": "0.0.0",
  "dependencies": {
    "@langchain/<ADD_PACKAGE_NAME_HERE>": "^0.0.0",
    "@langchain/core": "^0.3.0"
  },
  "resolutions": {
    "@langchain/core": "^0.3.0"
  },
  "overrides": {
    "@langchain/core": "^0.3.0"
  },
  "pnpm": {
    "overrides": {
      "@langchain/core": "^0.3.0"
    }
  }
}

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 <ADD_CLASS_NAME_HERE> class, which is the recommended way to interface with the <ADD_NAME_HERE> series of models.

To use, install the requirements, and configure your environment.

export <ADD_ENV_NAME_HERE>=your-api-key

Then initialize

import { <ADD_CLASS_NAME_HERE> } from "@langchain/<ADD_PACKAGE_NAME_HERE>";

const model = new ExampleChatClass({
  apiKey: process.env.EXAMPLE_API_KEY,
});
const response = await model.invoke(new HumanMessage("Hello world!"));

Streaming

import { <ADD_CLASS_NAME_HERE> } from "@langchain/<ADD_PACKAGE_NAME_HERE>";

const model = new ExampleChatClass({
  apiKey: process.env.EXAMPLE_API_KEY,
});
const response = await model.stream(new HumanMessage("Hello world!"));

Embeddings

This package also adds support for <ADD_NAME_HERE> embeddings model.

import { <ADD_CLASS_NAME_HERE> } from "@langchain/<ADD_PACKAGE_NAME_HERE>";

const embeddings = new ExampleEmbeddingClass({
  apiKey: process.env.EXAMPLE_API_KEY,
});
const res = await embeddings.embedQuery("Hello world");

Development

To develop the <ADD_NAME_HERE> 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/<ADD_PACKAGE_NAME_HERE>

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 the entrypoints field in the config variable located inside langchain.config.js and run yarn build to generate the new entrypoint.