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https://js.langchain.com/v0.1/docs/modules/data_connection/text_embedding/caching_embeddings/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Get started](/v0.1/docs/get_started/) * [Introduction](/v0.1/docs/get_started/introduction/) * [Installation](/v0.1/docs/get_started/installation/) * [Quickstart](/v0.1/docs/get_started/quickstart/) * [LangChain Expression Language](/v0.1/docs/expression_language/) * [Get started](/v0.1/docs/expression_language/get_started/) * [Why use LCEL?](/v0.1/docs/expression_language/why/) * [Interface](/v0.1/docs/expression_language/interface/) * [Streaming](/v0.1/docs/expression_language/streaming/) * [How to](/v0.1/docs/expression_language/how_to/routing/) * [Cookbook](/v0.1/docs/expression_language/cookbook/) * [LangChain Expression Language (LCEL)](/v0.1/docs/expression_language/) * [Modules](/v0.1/docs/modules/) * [Model I/O](/v0.1/docs/modules/model_io/) * [Retrieval](/v0.1/docs/modules/data_connection/) * [Document loaders](/v0.1/docs/modules/data_connection/document_loaders/) * [Text Splitters](/v0.1/docs/modules/data_connection/document_transformers/) * [Retrievers](/v0.1/docs/modules/data_connection/retrievers/) * [Retrieval](/v0.1/docs/modules/data_connection/) * [Text embedding models](/v0.1/docs/modules/data_connection/text_embedding/) * [Dealing with API errors](/v0.1/docs/modules/data_connection/text_embedding/api_errors/) * [Caching](/v0.1/docs/modules/data_connection/text_embedding/caching_embeddings/) * [Dealing with rate limits](/v0.1/docs/modules/data_connection/text_embedding/rate_limits/) * [Adding a timeout](/v0.1/docs/modules/data_connection/text_embedding/timeouts/) * [Vector stores](/v0.1/docs/modules/data_connection/vectorstores/) * [Indexing](/v0.1/docs/modules/data_connection/indexing/) * [Experimental](/v0.1/docs/modules/data_connection/experimental/multimodal_embeddings/google_vertex_ai/) * [Chains](/v0.1/docs/modules/chains/) * [Agents](/v0.1/docs/modules/agents/) * [More](/v0.1/docs/modules/memory/) * [Security](/v0.1/docs/security/) * [Guides](/v0.1/docs/guides/) * [Ecosystem](/v0.1/docs/ecosystem/) * [LangGraph](/v0.1/docs/langgraph/) * * * * * [](/v0.1/) * [Modules](/v0.1/docs/modules/) * [Retrieval](/v0.1/docs/modules/data_connection/) * [Text embedding models](/v0.1/docs/modules/data_connection/text_embedding/) * Caching On this page Caching ======= Embeddings can be stored or temporarily cached to avoid needing to recompute them. Caching embeddings can be done using a `CacheBackedEmbeddings` instance. The cache backed embedder is a wrapper around an embedder that caches embeddings in a key-value store. The text is hashed and the hash is used as the key in the cache. The main supported way to initialized a `CacheBackedEmbeddings` is the `fromBytesStore` static method. This takes in the following parameters: * `underlying_embedder`: The embedder to use for embedding. * `document_embedding_cache`: The cache to use for storing document embeddings. * `namespace`: (optional, defaults to "") The namespace to use for document cache. This namespace is used to avoid collisions with other caches. For example, set it to the name of the embedding model used. Attention: Be sure to set the namespace parameter to avoid collisions of the same text embedded using different embeddings models. Usage, in-memory[](#usage-in-memory "Direct link to Usage, in-memory") ----------------------------------------------------------------------- tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai @langchain/community yarn add @langchain/openai @langchain/community pnpm add @langchain/openai @langchain/community Here's a basic test example with an in memory cache. This type of cache is primarily useful for unit tests or prototyping. Do not use this cache if you need to actually store the embeddings for an extended period of time: import { OpenAIEmbeddings } from "@langchain/openai";import { CacheBackedEmbeddings } from "langchain/embeddings/cache_backed";import { InMemoryStore } from "langchain/storage/in_memory";import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";import { FaissStore } from "@langchain/community/vectorstores/faiss";import { TextLoader } from "langchain/document_loaders/fs/text";const underlyingEmbeddings = new OpenAIEmbeddings();const inMemoryStore = new InMemoryStore();const cacheBackedEmbeddings = CacheBackedEmbeddings.fromBytesStore( underlyingEmbeddings, inMemoryStore, { namespace: underlyingEmbeddings.modelName, });const loader = new TextLoader("./state_of_the_union.txt");const rawDocuments = await loader.load();const splitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000, chunkOverlap: 0,});const documents = await splitter.splitDocuments(rawDocuments);// No keys logged yet since the cache is emptyfor await (const key of inMemoryStore.yieldKeys()) { console.log(key);}let time = Date.now();const vectorstore = await FaissStore.fromDocuments( documents, cacheBackedEmbeddings);console.log(`Initial creation time: ${Date.now() - time}ms`);/* Initial creation time: 1905ms/// The second time is much faster since the embeddings for the input docs have already been added to the cachetime = Date.now();const vectorstore2 = await FaissStore.fromDocuments( documents, cacheBackedEmbeddings);console.log(`Cached creation time: ${Date.now() - time}ms`);/ Cached creation time: 8ms/// Many keys logged with hashed valuesconst keys = [];for await (const key of inMemoryStore.yieldKeys()) { keys.push(key);}console.log(keys.slice(0, 5));/ [ 'text-embedding-ada-002ea9b59e760e64bec6ee9097b5a06b0d91cb3ab64', 'text-embedding-ada-0023b424f5ed1271a6f5601add17c1b58b7c992772e', 'text-embedding-ada-002fec5d021611e1527297c5e8f485876ea82dcb111', 'text-embedding-ada-00262f72e0c2d711c6b861714ee624b28af639fdb13', 'text-embedding-ada-00262d58882330038a4e6e25ea69a938f4391541874' ]/ #### API Reference: [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [CacheBackedEmbeddings](https://api.js.langchain.com/classes/langchain_embeddings_cache_backed.CacheBackedEmbeddings.html) from `langchain/embeddings/cache_backed` * [InMemoryStore](https://api.js.langchain.com/classes/langchain_core_stores.InMemoryStore.html) from `langchain/storage/in_memory` * [RecursiveCharacterTextSplitter](https://api.js.langchain.com/classes/langchain_textsplitters.RecursiveCharacterTextSplitter.html) from `langchain/text_splitter` * [FaissStore](https://api.js.langchain.com/classes/langchain_community_vectorstores_faiss.FaissStore.html) from `@langchain/community/vectorstores/faiss` * [TextLoader](https://api.js.langchain.com/classes/langchain_document_loaders_fs_text.TextLoader.html) from `langchain/document_loaders/fs/text` Usage, Convex[](#usage-convex "Direct link to Usage, Convex") -------------------------------------------------------------- Here's an example with a [Convex](https://convex.dev/) as a cache. ### Create project[](#create-project "Direct link to Create project") Get a working [Convex](https://docs.convex.dev/) project set up, for example by using: npm create convex@latest ### Add database accessors[](#add-database-accessors "Direct link to Add database accessors") Add query and mutation helpers to `convex/langchain/db.ts`: convex/langchain/db.ts export * from "langchain/util/convex"; ### Configure your schema[](#configure-your-schema "Direct link to Configure your schema") Set up your schema (for indexing): convex/schema.ts import { defineSchema, defineTable } from "convex/server";import { v } from "convex/values";export default defineSchema({ cache: defineTable({ key: v.string(), value: v.any(), }).index("byKey", ["key"]),}); ### Example[](#example "Direct link to Example") "use node";import { TextLoader } from "langchain/document_loaders/fs/text";import { CacheBackedEmbeddings } from "langchain/embeddings/cache_backed";import { OpenAIEmbeddings } from "@langchain/openai";import { ConvexKVStore } from "@langchain/community/storage/convex";import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";import { ConvexVectorStore } from "@langchain/community/vectorstores/convex";import { action } from "./_generated/server.js";export const ask = action({ args: {}, handler: async (ctx) => { const underlyingEmbeddings = new OpenAIEmbeddings(); const cacheBackedEmbeddings = CacheBackedEmbeddings.fromBytesStore( underlyingEmbeddings, new ConvexKVStore({ ctx }), { namespace: underlyingEmbeddings.modelName, } ); const loader = new TextLoader("./state_of_the_union.txt"); const rawDocuments = await loader.load(); const splitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000, chunkOverlap: 0, }); const documents = await splitter.splitDocuments(rawDocuments); let time = Date.now(); const vectorstore = await ConvexVectorStore.fromDocuments( documents, cacheBackedEmbeddings, { ctx } ); console.log(`Initial creation time: ${Date.now() - time}ms`); /* Initial creation time: 1808ms / // The second time is much faster since the embeddings for the input docs have already been added to the cache time = Date.now(); const vectorstore2 = await ConvexVectorStore.fromDocuments( documents, cacheBackedEmbeddings, { ctx } ); console.log(`Cached creation time: ${Date.now() - time}ms`); / Cached creation time: 33ms / },}); #### API Reference: [TextLoader](https://api.js.langchain.com/classes/langchain_document_loaders_fs_text.TextLoader.html) from `langchain/document_loaders/fs/text` * [CacheBackedEmbeddings](https://api.js.langchain.com/classes/langchain_embeddings_cache_backed.CacheBackedEmbeddings.html) from `langchain/embeddings/cache_backed` * [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [ConvexKVStore](https://api.js.langchain.com/classes/langchain_community_storage_convex.ConvexKVStore.html) from `@langchain/community/storage/convex` * [RecursiveCharacterTextSplitter](https://api.js.langchain.com/classes/langchain_textsplitters.RecursiveCharacterTextSplitter.html) from `langchain/text_splitter` * [ConvexVectorStore](https://api.js.langchain.com/classes/langchain_community_vectorstores_convex.ConvexVectorStore.html) from `@langchain/community/vectorstores/convex` Usage, Redis[](#usage-redis "Direct link to Usage, Redis") ----------------------------------------------------------- Here's an example with a Redis cache. You'll first need to install `ioredis` as a peer dependency and pass in an initialized client: * npm * Yarn * pnpm npm install ioredis yarn add ioredis pnpm add ioredis import { Redis } from "ioredis";import { OpenAIEmbeddings } from "@langchain/openai";import { CacheBackedEmbeddings } from "langchain/embeddings/cache_backed";import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";import { FaissStore } from "@langchain/community/vectorstores/faiss";import { TextLoader } from "langchain/document_loaders/fs/text";import { RedisByteStore } from "@langchain/community/storage/ioredis";const underlyingEmbeddings = new OpenAIEmbeddings();// Requires a Redis instance running at http://localhost:6379.// See https://github.com/redis/ioredis for full config options.const redisClient = new Redis();const redisStore = new RedisByteStore({ client: redisClient,});const cacheBackedEmbeddings = CacheBackedEmbeddings.fromBytesStore( underlyingEmbeddings, redisStore, { namespace: underlyingEmbeddings.modelName, });const loader = new TextLoader("./state_of_the_union.txt");const rawDocuments = await loader.load();const splitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000, chunkOverlap: 0,});const documents = await splitter.splitDocuments(rawDocuments);let time = Date.now();const vectorstore = await FaissStore.fromDocuments( documents, cacheBackedEmbeddings);console.log(`Initial creation time: ${Date.now() - time}ms`);/* Initial creation time: 1808ms/// The second time is much faster since the embeddings for the input docs have already been added to the cachetime = Date.now();const vectorstore2 = await FaissStore.fromDocuments( documents, cacheBackedEmbeddings);console.log(`Cached creation time: ${Date.now() - time}ms`);/ Cached creation time: 33ms/// Many keys logged with hashed valuesconst keys = [];for await (const key of redisStore.yieldKeys()) { keys.push(key);}console.log(keys.slice(0, 5));/ [ 'text-embedding-ada-002fa9ac80e1bf226b7b4dfc03ea743289a65a727b2', 'text-embedding-ada-0027dbf9c4b36e12fe1768300f145f4640342daaf22', 'text-embedding-ada-002ea9b59e760e64bec6ee9097b5a06b0d91cb3ab64', 'text-embedding-ada-002fec5d021611e1527297c5e8f485876ea82dcb111', 'text-embedding-ada-002c00f818c345da13fed9f2697b4b689338143c8c7' ]/ #### API Reference: [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [CacheBackedEmbeddings](https://api.js.langchain.com/classes/langchain_embeddings_cache_backed.CacheBackedEmbeddings.html) from `langchain/embeddings/cache_backed` * [RecursiveCharacterTextSplitter](https://api.js.langchain.com/classes/langchain_textsplitters.RecursiveCharacterTextSplitter.html) from `langchain/text_splitter` * [FaissStore](https://api.js.langchain.com/classes/langchain_community_vectorstores_faiss.FaissStore.html) from `@langchain/community/vectorstores/faiss` * [TextLoader](https://api.js.langchain.com/classes/langchain_document_loaders_fs_text.TextLoader.html) from `langchain/document_loaders/fs/text` * [RedisByteStore](https://api.js.langchain.com/classes/langchain_community_storage_ioredis.RedisByteStore.html) from `@langchain/community/storage/ioredis` * * * #### Help us out by providing feedback on this documentation page: [ Previous Dealing with API errors ](/v0.1/docs/modules/data_connection/text_embedding/api_errors/)[ Next Dealing with rate limits ](/v0.1/docs/modules/data_connection/text_embedding/rate_limits/) * [Usage, in-memory](#usage-in-memory) * [Usage, Convex](#usage-convex) * [Create project](#create-project) * [Add database accessors](#add-database-accessors) * [Configure your schema](#configure-your-schema) * [Example](#example) * [Usage, Redis](#usage-redis) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/modules/data_connection/text_embedding/rate_limits/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Get started](/v0.1/docs/get_started/) * [Introduction](/v0.1/docs/get_started/introduction/) * [Installation](/v0.1/docs/get_started/installation/) * [Quickstart](/v0.1/docs/get_started/quickstart/) * [LangChain Expression Language](/v0.1/docs/expression_language/) * [Get started](/v0.1/docs/expression_language/get_started/) * [Why use LCEL?](/v0.1/docs/expression_language/why/) * [Interface](/v0.1/docs/expression_language/interface/) * [Streaming](/v0.1/docs/expression_language/streaming/) * [How to](/v0.1/docs/expression_language/how_to/routing/) * [Cookbook](/v0.1/docs/expression_language/cookbook/) * [LangChain Expression Language (LCEL)](/v0.1/docs/expression_language/) * [Modules](/v0.1/docs/modules/) * [Model I/O](/v0.1/docs/modules/model_io/) * [Retrieval](/v0.1/docs/modules/data_connection/) * [Document loaders](/v0.1/docs/modules/data_connection/document_loaders/) * [Text Splitters](/v0.1/docs/modules/data_connection/document_transformers/) * [Retrievers](/v0.1/docs/modules/data_connection/retrievers/) * [Retrieval](/v0.1/docs/modules/data_connection/) * [Text embedding models](/v0.1/docs/modules/data_connection/text_embedding/) * [Dealing with API errors](/v0.1/docs/modules/data_connection/text_embedding/api_errors/) * [Caching](/v0.1/docs/modules/data_connection/text_embedding/caching_embeddings/) * [Dealing with rate limits](/v0.1/docs/modules/data_connection/text_embedding/rate_limits/) * [Adding a timeout](/v0.1/docs/modules/data_connection/text_embedding/timeouts/) * [Vector stores](/v0.1/docs/modules/data_connection/vectorstores/) * [Indexing](/v0.1/docs/modules/data_connection/indexing/) * [Experimental](/v0.1/docs/modules/data_connection/experimental/multimodal_embeddings/google_vertex_ai/) * [Chains](/v0.1/docs/modules/chains/) * [Agents](/v0.1/docs/modules/agents/) * [More](/v0.1/docs/modules/memory/) * [Security](/v0.1/docs/security/) * [Guides](/v0.1/docs/guides/) * [Ecosystem](/v0.1/docs/ecosystem/) * [LangGraph](/v0.1/docs/langgraph/) * * * * * [](/v0.1/) * [Modules](/v0.1/docs/modules/) * [Retrieval](/v0.1/docs/modules/data_connection/) * [Text embedding models](/v0.1/docs/modules/data_connection/text_embedding/) * Dealing with rate limits Dealing with rate limits ======================== Some providers have rate limits. If you exceed the rate limit, you'll get an error. To help you deal with this, LangChain provides a `maxConcurrency` option when instantiating an Embeddings model. This option allows you to specify the maximum number of concurrent requests you want to make to the provider. If you exceed this number, LangChain will automatically queue up your requests to be sent as previous requests complete. For example, if you set `maxConcurrency: 5`, then LangChain will only send 5 requests to the provider at a time. If you send 10 requests, the first 5 will be sent immediately, and the next 5 will be queued up. Once one of the first 5 requests completes, the next request in the queue will be sent. To use this feature, simply pass `maxConcurrency: <number>` when you instantiate the LLM. For example: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai yarn add @langchain/openai pnpm add @langchain/openai import { OpenAIEmbeddings } from "@langchain/openai";const model = new OpenAIEmbeddings({ maxConcurrency: 5 }); * * * #### Help us out by providing feedback on this documentation page: [ Previous Caching ](/v0.1/docs/modules/data_connection/text_embedding/caching_embeddings/)[ Next Adding a timeout ](/v0.1/docs/modules/data_connection/text_embedding/timeouts/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/bedrock/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * Bedrock On this page Bedrock ======= > [Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes Foundation Models (FMs) from leading AI startups and Amazon available via an API. You can choose from a wide range of FMs to find the model that is best suited for your use case. Setup[](#setup "Direct link to Setup") --------------------------------------- You'll need to install a few official AWS packages as peer dependencies: * npm * Yarn * pnpm npm install @aws-crypto/sha256-js @aws-sdk/credential-provider-node @smithy/protocol-http @smithy/signature-v4 @smithy/eventstream-codec @smithy/util-utf8 @aws-sdk/types yarn add @aws-crypto/sha256-js @aws-sdk/credential-provider-node @smithy/protocol-http @smithy/signature-v4 @smithy/eventstream-codec @smithy/util-utf8 @aws-sdk/types pnpm add @aws-crypto/sha256-js @aws-sdk/credential-provider-node @smithy/protocol-http @smithy/signature-v4 @smithy/eventstream-codec @smithy/util-utf8 @aws-sdk/types You can also use Bedrock in web environments such as Edge functions or Cloudflare Workers by omitting the `@aws-sdk/credential-provider-node` dependency and using the `web` entrypoint: * npm * Yarn * pnpm npm install @aws-crypto/sha256-js @smithy/protocol-http @smithy/signature-v4 @smithy/eventstream-codec @smithy/util-utf8 @aws-sdk/types yarn add @aws-crypto/sha256-js @smithy/protocol-http @smithy/signature-v4 @smithy/eventstream-codec @smithy/util-utf8 @aws-sdk/types pnpm add @aws-crypto/sha256-js @smithy/protocol-http @smithy/signature-v4 @smithy/eventstream-codec @smithy/util-utf8 @aws-sdk/types Usage[](#usage "Direct link to Usage") --------------------------------------- tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/community yarn add @langchain/community pnpm add @langchain/community Note that some models require specific prompting techniques. For example, Anthropic's Claude-v2 model will throw an error if the prompt does not start with `Human:` . import { Bedrock } from "@langchain/community/llms/bedrock";// Or, from web environments:// import { Bedrock } from "@langchain/community/llms/bedrock/web";// If no credentials are provided, the default credentials from// @aws-sdk/credential-provider-node will be used.const model = new Bedrock({ model: "ai21.j2-grande-instruct", // You can also do e.g. "anthropic.claude-v2" region: "us-east-1", // endpointUrl: "custom.amazonaws.com", // credentials: { // accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID!, // secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY!, // }, // modelKwargs: {},});const res = await model.invoke("Tell me a joke");console.log(res);/* Why was the math book unhappy? Because it had too many problems!/ #### API Reference: [Bedrock](https://api.js.langchain.com/classes/langchain_community_llms_bedrock.Bedrock.html) from `@langchain/community/llms/bedrock` * * * #### Help us out by providing feedback on this documentation page: [ Previous Azure OpenAI ](/v0.1/docs/integrations/llms/azure/)[ Next Cloudflare Workers AI ](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Setup](#setup) * [Usage](#usage) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/aws_sagemaker/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * AWS SageMakerEndpoint On this page AWS SageMakerEndpoint ===================== LangChain.js supports integration with AWS SageMaker-hosted endpoints. Check [Amazon SageMaker JumpStart](https://aws.amazon.com/sagemaker/jumpstart/) for a list of available models, and how to deploy your own. Setup[](#setup "Direct link to Setup") --------------------------------------- You'll need to install the official SageMaker SDK as a peer dependency: * npm * Yarn * pnpm npm install @aws-sdk/client-sagemaker-runtime yarn add @aws-sdk/client-sagemaker-runtime pnpm add @aws-sdk/client-sagemaker-runtime tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/community yarn add @langchain/community pnpm add @langchain/community Usage[](#usage "Direct link to Usage") --------------------------------------- import { SageMakerEndpoint, SageMakerLLMContentHandler,} from "@langchain/community/llms/sagemaker_endpoint";interface ResponseJsonInterface { generation: { content: string; };}// Custom for whatever model you'll be usingclass LLama213BHandler implements SageMakerLLMContentHandler { contentType = "application/json"; accepts = "application/json"; async transformInput( prompt: string, modelKwargs: Record<string, unknown> ): Promise<Uint8Array> { const payload = { inputs: [[{ role: "user", content: prompt }]], parameters: modelKwargs, }; const stringifiedPayload = JSON.stringify(payload); return new TextEncoder().encode(stringifiedPayload); } async transformOutput(output: Uint8Array): Promise<string> { const response_json = JSON.parse( new TextDecoder("utf-8").decode(output) ) as ResponseJsonInterface[]; const content = response_json[0]?.generation.content ?? ""; return content; }}const contentHandler = new LLama213BHandler();const model = new SageMakerEndpoint({ endpointName: "aws-llama-2-13b-chat", modelKwargs: { temperature: 0.5, max_new_tokens: 700, top_p: 0.9, }, endpointKwargs: { CustomAttributes: "accept_eula=true", }, contentHandler, clientOptions: { region: "YOUR AWS ENDPOINT REGION", credentials: { accessKeyId: "YOUR AWS ACCESS ID", secretAccessKey: "YOUR AWS SECRET ACCESS KEY", }, },});const res = await model.invoke( "Hello, my name is John Doe, tell me a joke about llamas ");console.log(res);/* [ { content: "Hello, John Doe! Here's a llama joke for you: Why did the llama become a gardener? Because it was great at llama-scaping!" } ] / #### API Reference: [SageMakerEndpoint](https://api.js.langchain.com/classes/langchain_community_llms_sagemaker_endpoint.SageMakerEndpoint.html) from `@langchain/community/llms/sagemaker_endpoint` * [SageMakerLLMContentHandler](https://api.js.langchain.com/types/langchain_community_llms_sagemaker_endpoint.SageMakerLLMContentHandler.html) from `@langchain/community/llms/sagemaker_endpoint` * * * #### Help us out by providing feedback on this documentation page: [ Previous AlephAlpha ](/v0.1/docs/integrations/llms/aleph_alpha/)[ Next Azure OpenAI ](/v0.1/docs/integrations/llms/azure/) * [Setup](#setup) * [Usage](#usage) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/text_embedding/bedrock/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Alibaba Tongyi](/v0.1/docs/integrations/text_embedding/alibaba_tongyi/) * [Azure OpenAI](/v0.1/docs/integrations/text_embedding/azure_openai/) * [Baidu Qianfan](/v0.1/docs/integrations/text_embedding/baidu_qianfan/) * [Bedrock](/v0.1/docs/integrations/text_embedding/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/text_embedding/cloudflare_ai/) * [Cohere](/v0.1/docs/integrations/text_embedding/cohere/) * [Fireworks](/v0.1/docs/integrations/text_embedding/fireworks/) * [Google AI](/v0.1/docs/integrations/text_embedding/google_generativeai/) * [Google PaLM](/v0.1/docs/integrations/text_embedding/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/text_embedding/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/text_embedding/gradient_ai/) * [HuggingFace Inference](/v0.1/docs/integrations/text_embedding/hugging_face_inference/) * [Llama CPP](/v0.1/docs/integrations/text_embedding/llama_cpp/) * [Minimax](/v0.1/docs/integrations/text_embedding/minimax/) * [Mistral AI](/v0.1/docs/integrations/text_embedding/mistralai/) * [Nomic](/v0.1/docs/integrations/text_embedding/nomic/) * [Ollama](/v0.1/docs/integrations/text_embedding/ollama/) * [OpenAI](/v0.1/docs/integrations/text_embedding/openai/) * [Prem AI](/v0.1/docs/integrations/text_embedding/premai/) * [TensorFlow](/v0.1/docs/integrations/text_embedding/tensorflow/) * [Together AI](/v0.1/docs/integrations/text_embedding/togetherai/) * [HuggingFace Transformers](/v0.1/docs/integrations/text_embedding/transformers/) * [Voyage AI](/v0.1/docs/integrations/text_embedding/voyageai/) * [ZhipuAI](/v0.1/docs/integrations/text_embedding/zhipuai/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * Bedrock Bedrock ======= [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-service.html) is a fully managed service that makes base models from Amazon and third-party model providers accessible through an API. When this documentation was written, Bedrock supports one model for text embeddings, the Titan Embeddings G1 - Text model (amazon.titan-embed-text-v1). This model supports text retrieval, semantic similarity, and clustering. The maximum input text is 8K tokens and the maximum output vector length is 1536. Setup[](#setup "Direct link to Setup") --------------------------------------- To use this embedding, please ensure you have the Bedrock runtime client installed in your project. * npm * Yarn * pnpm npm i @aws-sdk/client-bedrock-runtime@^3.422.0 yarn add @aws-sdk/client-bedrock-runtime@^3.422.0 pnpm add @aws-sdk/client-bedrock-runtime@^3.422.0 tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/community yarn add @langchain/community pnpm add @langchain/community tip We're unifying model params across all packages. We now suggest using `model` instead of `modelName`, and `apiKey` for API keys. Usage[](#usage "Direct link to Usage") --------------------------------------- The `BedrockEmbeddings` class uses the AWS Bedrock API to generate embeddings for a given text. It strips new line characters from the text as recommended. /* eslint-disable @typescript-eslint/no-non-null-assertion /import { BedrockEmbeddings } from "@langchain/community/embeddings/bedrock";const embeddings = new BedrockEmbeddings({ region: process.env.BEDROCK_AWS_REGION!, credentials: { accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID!, secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY!, }, model: "amazon.titan-embed-text-v1", // Default value});const res = await embeddings.embedQuery( "What would be a good company name a company that makes colorful socks?");console.log({ res }); #### API Reference: [BedrockEmbeddings](https://api.js.langchain.com/classes/langchain_community_embeddings_bedrock.BedrockEmbeddings.html) from `@langchain/community/embeddings/bedrock` Configuring the Bedrock Runtime Client[](#configuring-the-bedrock-runtime-client "Direct link to Configuring the Bedrock Runtime Client") ------------------------------------------------------------------------------------------------------------------------------------------ You can pass in your own instance of the `BedrockRuntimeClient` if you want to customize options like `credentials`, `region`, `retryPolicy`, etc. import { BedrockRuntimeClient } from "@aws-sdk/client-bedrock-runtime";import { BedrockEmbeddings } from "langchain/embeddings/bedrock";const client = new BedrockRuntimeClient({ region: "us-east-1", credentials: getCredentials(),});const embeddings = new BedrockEmbeddings({ client,}); * * * #### Help us out by providing feedback on this documentation page: [ Previous Baidu Qianfan ](/v0.1/docs/integrations/text_embedding/baidu_qianfan/)[ Next Cloudflare Workers AI ](/v0.1/docs/integrations/text_embedding/cloudflare_ai/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/document_loaders/web_loaders/s3/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [File Loaders](/v0.1/docs/integrations/document_loaders/file_loaders/) * [Web Loaders](/v0.1/docs/integrations/document_loaders/web_loaders/) * [Cheerio](/v0.1/docs/integrations/document_loaders/web_loaders/web_cheerio/) * [Puppeteer](/v0.1/docs/integrations/document_loaders/web_loaders/web_puppeteer/) * [Playwright](/v0.1/docs/integrations/document_loaders/web_loaders/web_playwright/) * [Apify Dataset](/v0.1/docs/integrations/document_loaders/web_loaders/apify_dataset/) * [AssemblyAI Audio Transcript](/v0.1/docs/integrations/document_loaders/web_loaders/assemblyai_audio_transcription/) * [Azure Blob Storage Container](/v0.1/docs/integrations/document_loaders/web_loaders/azure_blob_storage_container/) * [Azure Blob Storage File](/v0.1/docs/integrations/document_loaders/web_loaders/azure_blob_storage_file/) * [Browserbase Loader](/v0.1/docs/integrations/document_loaders/web_loaders/browserbase/) * [College Confidential](/v0.1/docs/integrations/document_loaders/web_loaders/college_confidential/) * [Confluence](/v0.1/docs/integrations/document_loaders/web_loaders/confluence/) * [Couchbase](/v0.1/docs/integrations/document_loaders/web_loaders/couchbase/) * [Figma](/v0.1/docs/integrations/document_loaders/web_loaders/figma/) * [Firecrawl](/v0.1/docs/integrations/document_loaders/web_loaders/firecrawl/) * [GitBook](/v0.1/docs/integrations/document_loaders/web_loaders/gitbook/) * [GitHub](/v0.1/docs/integrations/document_loaders/web_loaders/github/) * [Hacker News](/v0.1/docs/integrations/document_loaders/web_loaders/hn/) * [IMSDB](/v0.1/docs/integrations/document_loaders/web_loaders/imsdb/) * [Notion API](/v0.1/docs/integrations/document_loaders/web_loaders/notionapi/) * [PDF files](/v0.1/docs/integrations/document_loaders/web_loaders/pdf/) * [Recursive URL Loader](/v0.1/docs/integrations/document_loaders/web_loaders/recursive_url_loader/) * [S3 File](/v0.1/docs/integrations/document_loaders/web_loaders/s3/) * [SearchApi Loader](/v0.1/docs/integrations/document_loaders/web_loaders/searchapi/) * [SerpAPI Loader](/v0.1/docs/integrations/document_loaders/web_loaders/serpapi/) * [Sitemap Loader](/v0.1/docs/integrations/document_loaders/web_loaders/sitemap/) * [Sonix Audio](/v0.1/docs/integrations/document_loaders/web_loaders/sonix_audio_transcription/) * [Blockchain Data](/v0.1/docs/integrations/document_loaders/web_loaders/sort_xyz_blockchain/) * [YouTube transcripts](/v0.1/docs/integrations/document_loaders/web_loaders/youtube/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Web Loaders](/v0.1/docs/integrations/document_loaders/web_loaders/) * S3 File S3 File ======= Compatibility Only available on Node.js. This covers how to load document objects from an s3 file object. Setup[](#setup "Direct link to Setup") --------------------------------------- To run this index you'll need to have Unstructured already set up and ready to use at an available URL endpoint. It can also be configured to run locally. See the docs [here](https://js.langchain.com/docs/modules/indexes/document_loaders/examples/file_loaders/unstructured) for information on how to do that. You'll also need to install the official AWS SDK: * npm * Yarn * pnpm npm install @aws-sdk/client-s3 yarn add @aws-sdk/client-s3 pnpm add @aws-sdk/client-s3 Usage[](#usage "Direct link to Usage") --------------------------------------- Once Unstructured is configured, you can use the S3 loader to load files and then convert them into a Document. You can optionally provide a s3Config parameter to specify your bucket region, access key, and secret access key. If these are not provided, you will need to have them in your environment (e.g., by running `aws configure`). import { S3Loader } from "langchain/document_loaders/web/s3";const loader = new S3Loader({ bucket: "my-document-bucket-123", key: "AccountingOverview.pdf", s3Config: { region: "us-east-1", credentials: { accessKeyId: "AKIAIOSFODNN7EXAMPLE", secretAccessKey: "wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY", }, }, unstructuredAPIURL: "http://localhost:8000/general/v0/general", unstructuredAPIKey: "", // this will be soon required});const docs = await loader.load();console.log(docs); #### API Reference: * [S3Loader](https://api.js.langchain.com/classes/langchain_document_loaders_web_s3.S3Loader.html) from `langchain/document_loaders/web/s3` * * * #### Help us out by providing feedback on this documentation page: [ Previous Recursive URL Loader ](/v0.1/docs/integrations/document_loaders/web_loaders/recursive_url_loader/)[ Next SearchApi Loader ](/v0.1/docs/integrations/document_loaders/web_loaders/searchapi/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/azure/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * Azure OpenAI On this page Azure OpenAI ============ [Azure OpenAI](https://azure.microsoft.com/products/ai-services/openai-service/) is a cloud service to help you quickly develop generative AI experiences with a diverse set of prebuilt and curated models from OpenAI, Meta and beyond. LangChain.js supports integration with [Azure OpenAI](https://azure.microsoft.com/products/ai-services/openai-service/) using either the dedicated [Azure OpenAI SDK](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai) or the [OpenAI SDK](https://github.com/openai/openai-node). You can learn more about Azure OpenAI and its difference with the OpenAI API on [this page](https://learn.microsoft.com/azure/ai-services/openai/overview). If you don't have an Azure account, you can [create a free account](https://azure.microsoft.com/free/) to get started. Using the Azure OpenAI SDK[](#using-the-azure-openai-sdk "Direct link to Using the Azure OpenAI SDK") ------------------------------------------------------------------------------------------------------ You'll first need to install the [`@langchain/azure-openai`](https://www.npmjs.com/package/@langchain/azure-openai) package: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install -S @langchain/azure-openai yarn add @langchain/azure-openai pnpm add @langchain/azure-openai tip We're unifying model params across all packages. We now suggest using `model` instead of `modelName`, and `apiKey` for API keys. You'll also need to have an Azure OpenAI instance deployed. You can deploy a version on Azure Portal following [this guide](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource?pivots=web-portal). Once you have your instance running, make sure you have the endpoint and key. You can find them in the Azure Portal, under the "Keys and Endpoint" section of your instance. You can then define the following environment variables to use the service: AZURE_OPENAI_API_ENDPOINT=<YOUR_ENDPOINT>AZURE_OPENAI_API_KEY=<YOUR_KEY>AZURE_OPENAI_API_DEPLOYMENT_NAME=<YOUR_DEPLOYMENT_NAME> Alternatively, you can pass the values directly to the `AzureOpenAI` constructor: import { AzureOpenAI } from "@langchain/azure-openai";const model = new AzureOpenAI({ azureOpenAIEndpoint: "<your_endpoint>", apiKey: "<your_key>", azureOpenAIApiDeploymentName: "<your_deployment_name",}); If you're using Azure Managed Identity, you can also pass the credentials directly to the constructor: import { DefaultAzureCredential } from "@azure/identity";import { AzureOpenAI } from "@langchain/azure-openai";const credentials = new DefaultAzureCredential();const model = new AzureOpenAI({ credentials, azureOpenAIEndpoint: "<your_endpoint>", azureOpenAIApiDeploymentName: "<your_deployment_name",}); If you're using Azure Managed Identity, you can also pass the credentials directly to the constructor: import { DefaultAzureCredential } from "@azure/identity";import { AzureOpenAI } from "@langchain/azure-openai";const credentials = new DefaultAzureCredential();const model = new AzureOpenAI({ credentials, azureOpenAIEndpoint: "<your_endpoint>", azureOpenAIApiDeploymentName: "<your_deployment_name", model: "<your_model>",}); ### LLM usage example[](#llm-usage-example "Direct link to LLM usage example") import { AzureOpenAI } from "@langchain/azure-openai";export const run = async () => { const model = new AzureOpenAI({ model: "gpt-4", temperature: 0.7, maxTokens: 1000, maxRetries: 5, }); const res = await model.invoke( "Question: What would be a good company name for a company that makes colorful socks?\nAnswer:" ); console.log({ res });}; #### API Reference: * [AzureOpenAI](https://api.js.langchain.com/classes/langchain_azure_openai.AzureOpenAI.html) from `@langchain/azure-openai` ### Chat usage example[](#chat-usage-example "Direct link to Chat usage example") import { AzureChatOpenAI } from "@langchain/azure-openai";export const run = async () => { const model = new AzureChatOpenAI({ model: "gpt-4", prefixMessages: [ { role: "system", content: "You are a helpful assistant that answers in pirate language", }, ], maxTokens: 50, }); const res = await model.invoke( "What would be a good company name for a company that makes colorful socks?" ); console.log({ res });}; #### API Reference: * [AzureChatOpenAI](https://api.js.langchain.com/classes/langchain_azure_openai.AzureChatOpenAI.html) from `@langchain/azure-openai` Using OpenAI SDK[](#using-openai-sdk "Direct link to Using OpenAI SDK") ------------------------------------------------------------------------ You can also use the `OpenAI` class to call OpenAI models hosted on Azure. For example, if your Azure instance is hosted under `https://{MY_INSTANCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}`, you could initialize your instance like this: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai yarn add @langchain/openai pnpm add @langchain/openai import { OpenAI } from "@langchain/openai";const model = new OpenAI({ temperature: 0.9, apiKey: "YOUR-API-KEY", azureOpenAIApiVersion: "YOUR-API-VERSION", azureOpenAIApiInstanceName: "{MY_INSTANCE_NAME}", azureOpenAIApiDeploymentName: "{DEPLOYMENT_NAME}",});const res = await model.invoke( "What would be a good company name for a company that makes colorful socks?");console.log({ res }); If your instance is hosted under a domain other than the default `openai.azure.com`, you'll need to use the alternate `AZURE_OPENAI_BASE_PATH` environment variable. For example, here's how you would connect to the domain `https://westeurope.api.microsoft.com/openai/deployments/{DEPLOYMENT_NAME}`: import { OpenAI } from "@langchain/openai";const model = new OpenAI({ temperature: 0.9, apiKey: "YOUR-API-KEY", azureOpenAIApiVersion: "YOUR-API-VERSION", azureOpenAIApiDeploymentName: "{DEPLOYMENT_NAME}", azureOpenAIBasePath: "https://westeurope.api.microsoft.com/openai/deployments", // In Node.js defaults to process.env.AZURE_OPENAI_BASE_PATH});const res = await model.invoke( "What would be a good company name for a company that makes colorful socks?");console.log({ res }); * * * #### Help us out by providing feedback on this documentation page: [ Previous AWS SageMakerEndpoint ](/v0.1/docs/integrations/llms/aws_sagemaker/)[ Next Bedrock ](/v0.1/docs/integrations/llms/bedrock/) * [Using the Azure OpenAI SDK](#using-the-azure-openai-sdk) * [LLM usage example](#llm-usage-example) * [Chat usage example](#chat-usage-example) * [Using OpenAI SDK](#using-openai-sdk) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/chat_memory/dynamodb/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Astra DB Chat Memory](/v0.1/docs/integrations/chat_memory/astradb/) * [Cassandra Chat Memory](/v0.1/docs/integrations/chat_memory/cassandra/) * [Cloudflare D1-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/cloudflare_d1/) * [Convex Chat Memory](/v0.1/docs/integrations/chat_memory/convex/) * [DynamoDB-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/dynamodb/) * [Firestore Chat Memory](/v0.1/docs/integrations/chat_memory/firestore/) * [IPFS Datastore Chat Memory](/v0.1/docs/integrations/chat_memory/ipfs_datastore/) * [Momento-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/momento/) * [MongoDB Chat Memory](/v0.1/docs/integrations/chat_memory/mongodb/) * [Motörhead Memory](/v0.1/docs/integrations/chat_memory/motorhead_memory/) * [PlanetScale Chat Memory](/v0.1/docs/integrations/chat_memory/planetscale/) * [Postgres Chat Memory](/v0.1/docs/integrations/chat_memory/postgres/) * [Redis-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/redis/) * [Upstash Redis-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/upstash_redis/) * [Xata Chat Memory](/v0.1/docs/integrations/chat_memory/xata/) * [Zep Memory](/v0.1/docs/integrations/chat_memory/zep_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * DynamoDB-Backed Chat Memory DynamoDB-Backed Chat Memory =========================== For longer-term persistence across chat sessions, you can swap out the default in-memory `chatHistory` that backs chat memory classes like `BufferMemory` for a DynamoDB instance. Setup[](#setup "Direct link to Setup") --------------------------------------- First, install the AWS DynamoDB client in your project: * npm * Yarn * pnpm npm install @aws-sdk/client-dynamodb yarn add @aws-sdk/client-dynamodb pnpm add @aws-sdk/client-dynamodb tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai @langchain/community yarn add @langchain/openai @langchain/community pnpm add @langchain/openai @langchain/community Next, sign into your AWS account and create a DynamoDB table. Name the table `langchain`, and name your partition key `id`. Make sure your partition key is a string. You can leave sort key and the other settings alone. You'll also need to retrieve an AWS access key and secret key for a role or user that has access to the table and add them to your environment variables. Usage[](#usage "Direct link to Usage") --------------------------------------- import { BufferMemory } from "langchain/memory";import { DynamoDBChatMessageHistory } from "@langchain/community/stores/message/dynamodb";import { ChatOpenAI } from "@langchain/openai";import { ConversationChain } from "langchain/chains";const memory = new BufferMemory({ chatHistory: new DynamoDBChatMessageHistory({ tableName: "langchain", partitionKey: "id", sessionId: new Date().toISOString(), // Or some other unique identifier for the conversation config: { region: "us-east-2", credentials: { accessKeyId: "<your AWS access key id>", secretAccessKey: "<your AWS secret access key>", }, }, }),});const model = new ChatOpenAI();const chain = new ConversationChain({ llm: model, memory });const res1 = await chain.invoke({ input: "Hi! I'm Jim." });console.log({ res1 });/{ res1: { text: "Hello Jim! It's nice to meet you. My name is AI. How may I assist you today?" }}/const res2 = await chain.invoke({ input: "What did I just say my name was?" });console.log({ res2 });/{ res1: { text: "You said your name was Jim." }}/ #### API Reference: * [BufferMemory](https://api.js.langchain.com/classes/langchain_memory.BufferMemory.html) from `langchain/memory` * [DynamoDBChatMessageHistory](https://api.js.langchain.com/classes/langchain_community_stores_message_dynamodb.DynamoDBChatMessageHistory.html) from `@langchain/community/stores/message/dynamodb` * [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [ConversationChain](https://api.js.langchain.com/classes/langchain_chains.ConversationChain.html) from `langchain/chains` * * * #### Help us out by providing feedback on this documentation page: [ Previous Convex Chat Memory ](/v0.1/docs/integrations/chat_memory/convex/)[ Next Firestore Chat Memory ](/v0.1/docs/integrations/chat_memory/firestore/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/text_embedding/azure_openai/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Alibaba Tongyi](/v0.1/docs/integrations/text_embedding/alibaba_tongyi/) * [Azure OpenAI](/v0.1/docs/integrations/text_embedding/azure_openai/) * [Baidu Qianfan](/v0.1/docs/integrations/text_embedding/baidu_qianfan/) * [Bedrock](/v0.1/docs/integrations/text_embedding/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/text_embedding/cloudflare_ai/) * [Cohere](/v0.1/docs/integrations/text_embedding/cohere/) * [Fireworks](/v0.1/docs/integrations/text_embedding/fireworks/) * [Google AI](/v0.1/docs/integrations/text_embedding/google_generativeai/) * [Google PaLM](/v0.1/docs/integrations/text_embedding/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/text_embedding/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/text_embedding/gradient_ai/) * [HuggingFace Inference](/v0.1/docs/integrations/text_embedding/hugging_face_inference/) * [Llama CPP](/v0.1/docs/integrations/text_embedding/llama_cpp/) * [Minimax](/v0.1/docs/integrations/text_embedding/minimax/) * [Mistral AI](/v0.1/docs/integrations/text_embedding/mistralai/) * [Nomic](/v0.1/docs/integrations/text_embedding/nomic/) * [Ollama](/v0.1/docs/integrations/text_embedding/ollama/) * [OpenAI](/v0.1/docs/integrations/text_embedding/openai/) * [Prem AI](/v0.1/docs/integrations/text_embedding/premai/) * [TensorFlow](/v0.1/docs/integrations/text_embedding/tensorflow/) * [Together AI](/v0.1/docs/integrations/text_embedding/togetherai/) * [HuggingFace Transformers](/v0.1/docs/integrations/text_embedding/transformers/) * [Voyage AI](/v0.1/docs/integrations/text_embedding/voyageai/) * [ZhipuAI](/v0.1/docs/integrations/text_embedding/zhipuai/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * Azure OpenAI On this page Azure OpenAI ============ [Azure OpenAI](https://azure.microsoft.com/products/ai-services/openai-service/) is a cloud service to help you quickly develop generative AI experiences with a diverse set of prebuilt and curated models from OpenAI, Meta and beyond. LangChain.js supports integration with [Azure OpenAI](https://azure.microsoft.com/products/ai-services/openai-service/) using either the dedicated [Azure OpenAI SDK](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai) or the [OpenAI SDK](https://github.com/openai/openai-node). You can learn more about Azure OpenAI and its difference with the OpenAI API on [this page](https://learn.microsoft.com/azure/ai-services/openai/overview). If you don't have an Azure account, you can [create a free account](https://azure.microsoft.com/free/) to get started. Using Azure OpenAI SDK[](#using-azure-openai-sdk "Direct link to Using Azure OpenAI SDK") ------------------------------------------------------------------------------------------ You'll first need to install the [`@langchain/azure-openai`](https://www.npmjs.com/package/@langchain/azure-openai) package: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install -S @langchain/azure-openai yarn add @langchain/azure-openai pnpm add @langchain/azure-openai tip We're unifying model params across all packages. We now suggest using `model` instead of `modelName`, and `apiKey` for API keys. You'll also need to have an Azure OpenAI instance deployed. You can deploy a version on Azure Portal following [this guide](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource?pivots=web-portal). Once you have your instance running, make sure you have the endpoint and key. You can find them in the Azure Portal, under the "Keys and Endpoint" section of your instance. You can then define the following environment variables to use the service: AZURE_OPENAI_API_ENDPOINT=<YOUR_ENDPOINT>AZURE_OPENAI_API_KEY=<YOUR_KEY>AZURE_OPENAI_API_EMBEDDING_DEPLOYMENT_NAME=<YOUR_EMBEDDING_DEPLOYMENT_NAME> Alternatively, you can pass the values directly to the `AzureOpenAI` constructor: import { AzureOpenAI } from "@langchain/azure-openai";const model = new AzureOpenAI({ azureOpenAIEndpoint: "<your_endpoint>", apiKey: "<your_key>", azureOpenAIApiDeploymentName: "<your_embedding_deployment_name",}); If you're using Azure Managed Identity, you can also pass the credentials directly to the constructor: import { DefaultAzureCredential } from "@azure/identity";import { AzureOpenAI } from "@langchain/azure-openai";const credentials = new DefaultAzureCredential();const model = new AzureOpenAI({ credentials, azureOpenAIEndpoint: "<your_endpoint>", azureOpenAIApiDeploymentName: "<your_embedding_deployment_name",}); ### Usage example[](#usage-example "Direct link to Usage example") import { AzureOpenAIEmbeddings } from "@langchain/azure-openai";const model = new AzureOpenAIEmbeddings();const res = await model.embedQuery( "What would be a good company name for a company that makes colorful socks?");console.log({ res }); #### API Reference: * [AzureOpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_azure_openai.AzureOpenAIEmbeddings.html) from `@langchain/azure-openai` Using OpenAI SDK[](#using-openai-sdk "Direct link to Using OpenAI SDK") ------------------------------------------------------------------------ The `OpenAIEmbeddings` class can also use the OpenAI API on Azure to generate embeddings for a given text. By default it strips new line characters from the text, as recommended by OpenAI, but you can disable this by passing `stripNewLines: false` to the constructor. For example, if your Azure instance is hosted under `https://{MY_INSTANCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}`, you could initialize your instance like this: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai yarn add @langchain/openai pnpm add @langchain/openai import { OpenAIEmbeddings } from "@langchain/openai";const embeddings = new OpenAIEmbeddings({ azureOpenAIApiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.AZURE_OPENAI_API_KEY azureOpenAIApiVersion: "YOUR-API-VERSION", // In Node.js defaults to process.env.AZURE_OPENAI_API_VERSION azureOpenAIApiInstanceName: "{MY_INSTANCE_NAME}", // In Node.js defaults to process.env.AZURE_OPENAI_API_INSTANCE_NAME azureOpenAIApiDeploymentName: "{DEPLOYMENT_NAME}", // In Node.js defaults to process.env.AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME}); If you'd like to initialize using environment variable defaults, the `process.env.AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME` will be used first, then `process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME`. This can be useful if you're using these embeddings with another Azure OpenAI model. If your instance is hosted under a domain other than the default `openai.azure.com`, you'll need to use the alternate `AZURE_OPENAI_BASE_PATH` environment variable. For example, here's how you would connect to the domain `https://westeurope.api.microsoft.com/openai/deployments/{DEPLOYMENT_NAME}`: import { OpenAIEmbeddings } from "@langchain/openai";const embeddings = new OpenAIEmbeddings({ azureOpenAIApiKey: "YOUR-API-KEY", azureOpenAIApiVersion: "YOUR-API-VERSION", azureOpenAIApiDeploymentName: "{DEPLOYMENT_NAME}", azureOpenAIBasePath: "https://westeurope.api.microsoft.com/openai/deployments", // In Node.js defaults to process.env.AZURE_OPENAI_BASE_PATH}); * * * #### Help us out by providing feedback on this documentation page: [ Previous Alibaba Tongyi ](/v0.1/docs/integrations/text_embedding/alibaba_tongyi/)[ Next Baidu Qianfan ](/v0.1/docs/integrations/text_embedding/baidu_qianfan/) * [Using Azure OpenAI SDK](#using-azure-openai-sdk) * [Usage example](#usage-example) * [Using OpenAI SDK](#using-openai-sdk) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/chat/azure/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Chat models](/v0.1/docs/integrations/chat/) * [Alibaba Tongyi](/v0.1/docs/integrations/chat/alibaba_tongyi/) * [Anthropic](/v0.1/docs/integrations/chat/anthropic/) * [Anthropic Tools](/v0.1/docs/integrations/chat/anthropic_tools/) * [Azure OpenAI](/v0.1/docs/integrations/chat/azure/) * [Baidu Wenxin](/v0.1/docs/integrations/chat/baidu_wenxin/) * [Bedrock](/v0.1/docs/integrations/chat/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/chat/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/chat/cohere/) * [Fake LLM](/v0.1/docs/integrations/chat/fake/) * [Fireworks](/v0.1/docs/integrations/chat/fireworks/) * [Friendli](/v0.1/docs/integrations/chat/friendli/) * [Google GenAI](/v0.1/docs/integrations/chat/google_generativeai/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/chat/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/chat/google_vertex_ai/) * [Groq](/v0.1/docs/integrations/chat/groq/) * [Llama CPP](/v0.1/docs/integrations/chat/llama_cpp/) * [Minimax](/v0.1/docs/integrations/chat/minimax/) * [Mistral AI](/v0.1/docs/integrations/chat/mistral/) * [NIBittensorChatModel](/v0.1/docs/integrations/chat/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/chat/ollama/) * [Ollama Functions](/v0.1/docs/integrations/chat/ollama_functions/) * [OpenAI](/v0.1/docs/integrations/chat/openai/) * [PremAI](/v0.1/docs/integrations/chat/premai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/chat/prompt_layer_openai/) * [TogetherAI](/v0.1/docs/integrations/chat/togetherai/) * [WebLLM](/v0.1/docs/integrations/chat/web_llm/) * [YandexGPT](/v0.1/docs/integrations/chat/yandex/) * [ZhipuAI](/v0.1/docs/integrations/chat/zhipuai/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Chat models](/v0.1/docs/integrations/chat/) * Azure OpenAI On this page Azure ChatOpenAI ================ [Azure OpenAI](https://azure.microsoft.com/products/ai-services/openai-service/) is a cloud service to help you quickly develop generative AI experiences with a diverse set of prebuilt and curated models from OpenAI, Meta and beyond. LangChain.js supports integration with [Azure OpenAI](https://azure.microsoft.com/products/ai-services/openai-service/) using either the dedicated [Azure OpenAI SDK](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai) or the [OpenAI SDK](https://github.com/openai/openai-node). You can learn more about Azure OpenAI and its difference with the OpenAI API on [this page](https://learn.microsoft.com/azure/ai-services/openai/overview). If you don't have an Azure account, you can [create a free account](https://azure.microsoft.com/free/) to get started. Using the OpenAI SDK[](#using-the-openai-sdk "Direct link to Using the OpenAI SDK") ------------------------------------------------------------------------------------ You can use the `ChatOpenAI` class to access OpenAI instances hosted on Azure. For example, if your Azure instance is hosted under `https://{MY_INSTANCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}`, you could initialize your instance like this: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai yarn add @langchain/openai pnpm add @langchain/openai tip We're unifying model params across all packages. We now suggest using `model` instead of `modelName`, and `apiKey` for API keys. import { ChatOpenAI } from "@langchain/openai";const model = new ChatOpenAI({ temperature: 0.9, azureOpenAIApiKey: "SOME_SECRET_VALUE", // In Node.js defaults to process.env.AZURE_OPENAI_API_KEY azureOpenAIApiVersion: "YOUR-API-VERSION", // In Node.js defaults to process.env.AZURE_OPENAI_API_VERSION azureOpenAIApiInstanceName: "{MY_INSTANCE_NAME}", // In Node.js defaults to process.env.AZURE_OPENAI_API_INSTANCE_NAME azureOpenAIApiDeploymentName: "{DEPLOYMENT_NAME}", // In Node.js defaults to process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME}); #### API Reference: * [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` If your instance is hosted under a domain other than the default `openai.azure.com`, you'll need to use the alternate `AZURE_OPENAI_BASE_PATH` environment variable. For example, here's how you would connect to the domain `https://westeurope.api.microsoft.com/openai/deployments/{DEPLOYMENT_NAME}`: import { ChatOpenAI } from "@langchain/openai";const model = new ChatOpenAI({ temperature: 0.9, azureOpenAIApiKey: "SOME_SECRET_VALUE", // In Node.js defaults to process.env.AZURE_OPENAI_API_KEY azureOpenAIApiVersion: "YOUR-API-VERSION", // In Node.js defaults to process.env.AZURE_OPENAI_API_VERSION azureOpenAIApiDeploymentName: "{DEPLOYMENT_NAME}", // In Node.js defaults to process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME azureOpenAIBasePath: "https://westeurope.api.microsoft.com/openai/deployments", // In Node.js defaults to process.env.AZURE_OPENAI_BASE_PATH}); #### API Reference: * [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` Using the Azure OpenAI SDK[](#using-the-azure-openai-sdk "Direct link to Using the Azure OpenAI SDK") ------------------------------------------------------------------------------------------------------ You'll first need to install the [`@langchain/azure-openai`](https://www.npmjs.com/package/@langchain/azure-openai) package: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install -S @langchain/azure-openai yarn add @langchain/azure-openai pnpm add @langchain/azure-openai You'll also need to have an Azure OpenAI instance deployed. You can deploy a version on Azure Portal following [this guide](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource?pivots=web-portal). Once you have your instance running, make sure you have the endpoint and key. You can find them in the Azure Portal, under the "Keys and Endpoint" section of your instance. You can then define the following environment variables to use the service: AZURE_OPENAI_API_ENDPOINT=<YOUR_ENDPOINT>AZURE_OPENAI_API_KEY=<YOUR_KEY>AZURE_OPENAI_API_EMBEDDING_DEPLOYMENT_NAME=<YOUR_EMBEDDING_DEPLOYMENT_NAME> Alternatively, you can pass the values directly to the `AzureOpenAI` constructor: import { AzureChatOpenAI } from "@langchain/azure-openai";const model = new AzureChatOpenAI({ azureOpenAIEndpoint: "<your_endpoint>", apiKey: "<your_key>", azureOpenAIApiDeploymentName: "<your_embedding_deployment_name", model: "<your_model>",}); If you're using Azure Managed Identity, you can also pass the credentials directly to the constructor: import { DefaultAzureCredential } from "@azure/identity";import { AzureChatOpenAI } from "@langchain/azure-openai";const credentials = new DefaultAzureCredential();const model = new AzureChatOpenAI({ credentials, azureOpenAIEndpoint: "<your_endpoint>", azureOpenAIApiDeploymentName: "<your_embedding_deployment_name", model: "<your_model>",}); ### Usage example[](#usage-example "Direct link to Usage example") import { AzureChatOpenAI } from "@langchain/azure-openai";const model = new AzureChatOpenAI({ model: "gpt-4", prefixMessages: [ { role: "system", content: "You are a helpful assistant that answers in pirate language", }, ], maxTokens: 50,});const res = await model.invoke( "What would be a good company name for a company that makes colorful socks?");console.log({ res }); #### API Reference: * [AzureChatOpenAI](https://api.js.langchain.com/classes/langchain_azure_openai.AzureChatOpenAI.html) from `@langchain/azure-openai` * * * #### Help us out by providing feedback on this documentation page: [ Previous Anthropic Tools ](/v0.1/docs/integrations/chat/anthropic_tools/)[ Next Baidu Wenxin ](/v0.1/docs/integrations/chat/baidu_wenxin/) * [Using the OpenAI SDK](#using-the-openai-sdk) * [Using the Azure OpenAI SDK](#using-the-azure-openai-sdk) * [Usage example](#usage-example) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/document_loaders/web_loaders/azure_blob_storage_container/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [File Loaders](/v0.1/docs/integrations/document_loaders/file_loaders/) * [Web Loaders](/v0.1/docs/integrations/document_loaders/web_loaders/) * [Cheerio](/v0.1/docs/integrations/document_loaders/web_loaders/web_cheerio/) * [Puppeteer](/v0.1/docs/integrations/document_loaders/web_loaders/web_puppeteer/) * [Playwright](/v0.1/docs/integrations/document_loaders/web_loaders/web_playwright/) * [Apify Dataset](/v0.1/docs/integrations/document_loaders/web_loaders/apify_dataset/) * [AssemblyAI Audio Transcript](/v0.1/docs/integrations/document_loaders/web_loaders/assemblyai_audio_transcription/) * [Azure Blob Storage Container](/v0.1/docs/integrations/document_loaders/web_loaders/azure_blob_storage_container/) * [Azure Blob Storage File](/v0.1/docs/integrations/document_loaders/web_loaders/azure_blob_storage_file/) * [Browserbase Loader](/v0.1/docs/integrations/document_loaders/web_loaders/browserbase/) * [College Confidential](/v0.1/docs/integrations/document_loaders/web_loaders/college_confidential/) * [Confluence](/v0.1/docs/integrations/document_loaders/web_loaders/confluence/) * [Couchbase](/v0.1/docs/integrations/document_loaders/web_loaders/couchbase/) * [Figma](/v0.1/docs/integrations/document_loaders/web_loaders/figma/) * [Firecrawl](/v0.1/docs/integrations/document_loaders/web_loaders/firecrawl/) * [GitBook](/v0.1/docs/integrations/document_loaders/web_loaders/gitbook/) * [GitHub](/v0.1/docs/integrations/document_loaders/web_loaders/github/) * [Hacker News](/v0.1/docs/integrations/document_loaders/web_loaders/hn/) * [IMSDB](/v0.1/docs/integrations/document_loaders/web_loaders/imsdb/) * [Notion API](/v0.1/docs/integrations/document_loaders/web_loaders/notionapi/) * [PDF files](/v0.1/docs/integrations/document_loaders/web_loaders/pdf/) * [Recursive URL Loader](/v0.1/docs/integrations/document_loaders/web_loaders/recursive_url_loader/) * [S3 File](/v0.1/docs/integrations/document_loaders/web_loaders/s3/) * [SearchApi Loader](/v0.1/docs/integrations/document_loaders/web_loaders/searchapi/) * [SerpAPI Loader](/v0.1/docs/integrations/document_loaders/web_loaders/serpapi/) * [Sitemap Loader](/v0.1/docs/integrations/document_loaders/web_loaders/sitemap/) * [Sonix Audio](/v0.1/docs/integrations/document_loaders/web_loaders/sonix_audio_transcription/) * [Blockchain Data](/v0.1/docs/integrations/document_loaders/web_loaders/sort_xyz_blockchain/) * [YouTube transcripts](/v0.1/docs/integrations/document_loaders/web_loaders/youtube/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Web Loaders](/v0.1/docs/integrations/document_loaders/web_loaders/) * Azure Blob Storage Container Azure Blob Storage Container ============================ Compatibility Only available on Node.js. This covers how to load a container on Azure Blob Storage into LangChain documents. Setup[](#setup "Direct link to Setup") --------------------------------------- To run this loader, you'll need to have Unstructured already set up and ready to use at an available URL endpoint. It can also be configured to run locally. See the docs [here](https://js.langchain.com/docs/modules/indexes/document_loaders/examples/file_loaders/unstructured) for information on how to do that. You'll also need to install the official Azure Storage Blob client library: * npm * Yarn * pnpm npm install @azure/storage-blob yarn add @azure/storage-blob pnpm add @azure/storage-blob Usage[](#usage "Direct link to Usage") --------------------------------------- Once Unstructured is configured, you can use the Azure Blob Storage Container loader to load files and then convert them into a Document. import { AzureBlobStorageContainerLoader } from "langchain/document_loaders/web/azure_blob_storage_container";const loader = new AzureBlobStorageContainerLoader({ azureConfig: { connectionString: "", container: "container_name", }, unstructuredConfig: { apiUrl: "http://localhost:8000/general/v0/general", apiKey: "", // this will be soon required },});const docs = await loader.load();console.log(docs); #### API Reference: * [AzureBlobStorageContainerLoader](https://api.js.langchain.com/classes/langchain_document_loaders_web_azure_blob_storage_container.AzureBlobStorageContainerLoader.html) from `langchain/document_loaders/web/azure_blob_storage_container` * * * #### Help us out by providing feedback on this documentation page: [ Previous AssemblyAI Audio Transcript ](/v0.1/docs/integrations/document_loaders/web_loaders/assemblyai_audio_transcription/)[ Next Azure Blob Storage File ](/v0.1/docs/integrations/document_loaders/web_loaders/azure_blob_storage_file/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/document_loaders/web_loaders/azure_blob_storage_file/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [File Loaders](/v0.1/docs/integrations/document_loaders/file_loaders/) * [Web Loaders](/v0.1/docs/integrations/document_loaders/web_loaders/) * [Cheerio](/v0.1/docs/integrations/document_loaders/web_loaders/web_cheerio/) * [Puppeteer](/v0.1/docs/integrations/document_loaders/web_loaders/web_puppeteer/) * [Playwright](/v0.1/docs/integrations/document_loaders/web_loaders/web_playwright/) * [Apify Dataset](/v0.1/docs/integrations/document_loaders/web_loaders/apify_dataset/) * [AssemblyAI Audio Transcript](/v0.1/docs/integrations/document_loaders/web_loaders/assemblyai_audio_transcription/) * [Azure Blob Storage Container](/v0.1/docs/integrations/document_loaders/web_loaders/azure_blob_storage_container/) * [Azure Blob Storage File](/v0.1/docs/integrations/document_loaders/web_loaders/azure_blob_storage_file/) * [Browserbase Loader](/v0.1/docs/integrations/document_loaders/web_loaders/browserbase/) * [College Confidential](/v0.1/docs/integrations/document_loaders/web_loaders/college_confidential/) * [Confluence](/v0.1/docs/integrations/document_loaders/web_loaders/confluence/) * [Couchbase](/v0.1/docs/integrations/document_loaders/web_loaders/couchbase/) * [Figma](/v0.1/docs/integrations/document_loaders/web_loaders/figma/) * [Firecrawl](/v0.1/docs/integrations/document_loaders/web_loaders/firecrawl/) * [GitBook](/v0.1/docs/integrations/document_loaders/web_loaders/gitbook/) * [GitHub](/v0.1/docs/integrations/document_loaders/web_loaders/github/) * [Hacker News](/v0.1/docs/integrations/document_loaders/web_loaders/hn/) * [IMSDB](/v0.1/docs/integrations/document_loaders/web_loaders/imsdb/) * [Notion API](/v0.1/docs/integrations/document_loaders/web_loaders/notionapi/) * [PDF files](/v0.1/docs/integrations/document_loaders/web_loaders/pdf/) * [Recursive URL Loader](/v0.1/docs/integrations/document_loaders/web_loaders/recursive_url_loader/) * [S3 File](/v0.1/docs/integrations/document_loaders/web_loaders/s3/) * [SearchApi Loader](/v0.1/docs/integrations/document_loaders/web_loaders/searchapi/) * [SerpAPI Loader](/v0.1/docs/integrations/document_loaders/web_loaders/serpapi/) * [Sitemap Loader](/v0.1/docs/integrations/document_loaders/web_loaders/sitemap/) * [Sonix Audio](/v0.1/docs/integrations/document_loaders/web_loaders/sonix_audio_transcription/) * [Blockchain Data](/v0.1/docs/integrations/document_loaders/web_loaders/sort_xyz_blockchain/) * [YouTube transcripts](/v0.1/docs/integrations/document_loaders/web_loaders/youtube/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Web Loaders](/v0.1/docs/integrations/document_loaders/web_loaders/) * Azure Blob Storage File Azure Blob Storage File ======================= Compatibility Only available on Node.js. This covers how to load an Azure File into LangChain documents. Setup[](#setup "Direct link to Setup") --------------------------------------- To use this loader, you'll need to have Unstructured already set up and ready to use at an available URL endpoint. It can also be configured to run locally. See the docs [here](https://js.langchain.com/docs/modules/indexes/document_loaders/examples/file_loaders/unstructured) for information on how to do that. You'll also need to install the official Azure Storage Blob client library: * npm * Yarn * pnpm npm install @azure/storage-blob yarn add @azure/storage-blob pnpm add @azure/storage-blob Usage[](#usage "Direct link to Usage") --------------------------------------- Once Unstructured is configured, you can use the Azure Blob Storage File loader to load files and then convert them into a Document. import { AzureBlobStorageFileLoader } from "langchain/document_loaders/web/azure_blob_storage_file";const loader = new AzureBlobStorageFileLoader({ azureConfig: { connectionString: "", container: "container_name", blobName: "example.txt", }, unstructuredConfig: { apiUrl: "http://localhost:8000/general/v0/general", apiKey: "", // this will be soon required },});const docs = await loader.load();console.log(docs); #### API Reference: * [AzureBlobStorageFileLoader](https://api.js.langchain.com/classes/langchain_document_loaders_web_azure_blob_storage_file.AzureBlobStorageFileLoader.html) from `langchain/document_loaders/web/azure_blob_storage_file` * * * #### Help us out by providing feedback on this documentation page: [ Previous Azure Blob Storage Container ](/v0.1/docs/integrations/document_loaders/web_loaders/azure_blob_storage_container/)[ Next Browserbase Loader ](/v0.1/docs/integrations/document_loaders/web_loaders/browserbase/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/ai21/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * AI21 AI21 ==== You can get started with AI21Labs' Jurassic family of models, as well as see a full list of available foundational models, by signing up for an API key [on their website](https://www.ai21.com/). Here's an example of initializing an instance in LangChain.js: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/community yarn add @langchain/community pnpm add @langchain/community import { AI21 } from "@langchain/community/llms/ai21";const model = new AI21({ ai21ApiKey: "YOUR_AI21_API_KEY", // Or set as process.env.AI21_API_KEY});const res = await model.invoke(`Translate "I love programming" into German.`);console.log({ res });/* { res: "\nIch liebe das Programmieren." } / #### API Reference: [AI21](https://api.js.langchain.com/classes/langchain_community_llms_ai21.AI21.html) from `@langchain/community/llms/ai21` * * * #### Help us out by providing feedback on this documentation page: [ Previous LLMs ](/v0.1/docs/integrations/llms/)[ Next AlephAlpha ](/v0.1/docs/integrations/llms/aleph_alpha/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/openai/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * OpenAI On this page OpenAI ====== Here's how you can initialize an `OpenAI` LLM instance: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai yarn add @langchain/openai pnpm add @langchain/openai tip We're unifying model params across all packages. We now suggest using `model` instead of `modelName`, and `apiKey` for API keys. import { OpenAI } from "@langchain/openai";const model = new OpenAI({ model: "gpt-3.5-turbo-instruct", // Defaults to "gpt-3.5-turbo-instruct" if no model provided. temperature: 0.9, apiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.OPENAI_API_KEY});const res = await model.invoke( "What would be a good company name a company that makes colorful socks?");console.log({ res }); If you're part of an organization, you can set `process.env.OPENAI_ORGANIZATION` to your OpenAI organization id, or pass it in as `organization` when initializing the model. Custom URLs[](#custom-urls "Direct link to Custom URLs") --------------------------------------------------------- You can customize the base URL the SDK sends requests to by passing a `configuration` parameter like this: const model = new OpenAI({ temperature: 0.9, configuration: { baseURL: "https://your_custom_url.com", },}); You can also pass other `ClientOptions` parameters accepted by the official SDK. If you are hosting on Azure OpenAI, see the [dedicated page instead](/v0.1/docs/integrations/llms/azure/). * * * #### Help us out by providing feedback on this documentation page: [ Previous Ollama ](/v0.1/docs/integrations/llms/ollama/)[ Next PromptLayer OpenAI ](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [Custom URLs](#custom-urls) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/cloudflare_workersai/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * Cloudflare Workers AI On this page Cloudflare Workers AI ===================== info Workers AI is currently in Open Beta and is not recommended for production data and traffic, and limits + access are subject to change Workers AI allows you to run machine learning models, on the Cloudflare network, from your own code. Usage[](#usage "Direct link to Usage") --------------------------------------- You'll first need to install the LangChain Cloudflare integration package: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/cloudflare yarn add @langchain/cloudflare pnpm add @langchain/cloudflare import { CloudflareWorkersAI } from "@langchain/cloudflare";const model = new CloudflareWorkersAI({ model: "@cf/meta/llama-2-7b-chat-int8", // Default value cloudflareAccountId: process.env.CLOUDFLARE_ACCOUNT_ID, cloudflareApiToken: process.env.CLOUDFLARE_API_TOKEN, // Pass a custom base URL to use Cloudflare AI Gateway // baseUrl: `https://gateway.ai.cloudflare.com/v1/{YOUR_ACCOUNT_ID}/{GATEWAY_NAME}/workers-ai/`,});const response = await model.invoke( `Translate "I love programming" into German.`);console.log(response);/* Here are a few options:1. "Ich liebe Programmieren" - This is the most common way to say "I love programming" in German. "Liebe" means "love" in German, and "Programmieren" means "programming".2. "Programmieren macht mir Spaß" - This means "Programming makes me happy". This is a more casual way to express your love for programming in German.3. "Ich bin ein großer Fan von Programmieren" - This means "I'm a big fan of programming". This is a more formal way to express your love for programming in German.4. "Programmieren ist mein Hobby" - This means "Programming is my hobby". This is a more casual way to express your love for programming in German.5. "Ich liebe es, Programme zu schreiben" - This means "I love writing programs". This is a more formal way to express your love for programming in German./const stream = await model.stream( `Translate "I love programming" into German.`);for await (const chunk of stream) { console.log(chunk);}/ Here are a few options : 1 . " I ch lie be Program .../ #### API Reference: [CloudflareWorkersAI](https://api.js.langchain.com/classes/langchain_cloudflare.CloudflareWorkersAI.html) from `@langchain/cloudflare` * * * #### Help us out by providing feedback on this documentation page: [ Previous Bedrock ](/v0.1/docs/integrations/llms/bedrock/)[ Next Cohere ](/v0.1/docs/integrations/llms/cohere/) * [Usage](#usage) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/aleph_alpha/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * AlephAlpha AlephAlpha ========== LangChain.js supports AlephAlpha's Luminous family of models. You'll need to sign up for an API key [on their website](https://www.aleph-alpha.com/). Here's an example: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/community yarn add @langchain/community pnpm add @langchain/community import { AlephAlpha } from "@langchain/community/llms/aleph_alpha";const model = new AlephAlpha({ aleph_alpha_api_key: "YOUR_ALEPH_ALPHA_API_KEY", // Or set as process.env.ALEPH_ALPHA_API_KEY});const res = await model.invoke(`Is cereal soup?`);console.log({ res });/* { res: "\nIs soup a cereal? I don’t think so, but it is delicious." } / #### API Reference: [AlephAlpha](https://api.js.langchain.com/classes/langchain_community_llms_aleph_alpha.AlephAlpha.html) from `@langchain/community/llms/aleph_alpha` * * * #### Help us out by providing feedback on this documentation page: [ Previous AI21 ](/v0.1/docs/integrations/llms/ai21/)[ Next AWS SageMakerEndpoint ](/v0.1/docs/integrations/llms/aws_sagemaker/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/cohere/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * Cohere Cohere ====== LangChain.js supports Cohere LLMs. Here's an example: You'll first need to install the [`@langchain/cohere`](https://www.npmjs.com/package/@langchain/cohere) package. tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/cohere yarn add @langchain/cohere pnpm add @langchain/cohere import { Cohere } from "@langchain/cohere";const model = new Cohere({ maxTokens: 20, apiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.COHERE_API_KEY});const res = await model.invoke( "What would be a good company name a company that makes colorful socks?");console.log({ res }); #### API Reference: * [Cohere](https://api.js.langchain.com/classes/langchain_cohere.Cohere.html) from `@langchain/cohere` * * * #### Help us out by providing feedback on this documentation page: [ Previous Cloudflare Workers AI ](/v0.1/docs/integrations/llms/cloudflare_workersai/)[ Next Fake LLM ](/v0.1/docs/integrations/llms/fake/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/fake/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * Fake LLM On this page Fake LLM ======== LangChain provides a fake LLM for testing purposes. This allows you to mock out calls to the LLM and and simulate what would happen if the LLM responded in a certain way. Usage[](#usage "Direct link to Usage") --------------------------------------- import { FakeListLLM } from "langchain/llms/fake";/** * The FakeListLLM can be used to simulate ordered predefined responses. /const llm = new FakeListLLM({ responses: ["I'll callback later.", "You 'console' them!"],});const firstResponse = await llm.invoke("You want to hear a JavasSript joke?");const secondResponse = await llm.invoke( "How do you cheer up a JavaScript developer?");console.log({ firstResponse });console.log({ secondResponse });/* * The FakeListLLM can also be used to simulate streamed responses. /const stream = await llm.stream("You want to hear a JavasSript joke?");const chunks = [];for await (const chunk of stream) { chunks.push(chunk);}console.log(chunks.join(""));/* * The FakeListLLM can also be used to simulate delays in either either synchronous or streamed responses. /const slowLLM = new FakeListLLM({ responses: ["Because Oct 31 equals Dec 25", "You 'console' them!"], sleep: 1000,});const slowResponse = await slowLLM.invoke( "Why do programmers always mix up Halloween and Christmas?");console.log({ slowResponse });const slowStream = await slowLLM.stream( "How do you cheer up a JavaScript developer?");const slowChunks = [];for await (const chunk of slowStream) { slowChunks.push(chunk);}console.log(slowChunks.join("")); #### API Reference: [FakeListLLM](https://api.js.langchain.com/classes/langchain_llms_fake.FakeListLLM.html) from `langchain/llms/fake` * * * #### Help us out by providing feedback on this documentation page: [ Previous Cohere ](/v0.1/docs/integrations/llms/cohere/)[ Next Fireworks ](/v0.1/docs/integrations/llms/fireworks/) * [Usage](#usage) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/fireworks/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * Fireworks Fireworks ========= tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/community yarn add @langchain/community pnpm add @langchain/community You can use models provided by Fireworks AI as follows: import { Fireworks } from "@langchain/community/llms/fireworks";const model = new Fireworks({ temperature: 0.9, // In Node.js defaults to process.env.FIREWORKS_API_KEY apiKey: "YOUR-API-KEY",}); #### API Reference: * [Fireworks](https://api.js.langchain.com/classes/langchain_community_llms_fireworks.Fireworks.html) from `@langchain/community/llms/fireworks` Behind the scenes, Fireworks AI uses the OpenAI SDK and OpenAI compatible API, with some caveats: * Certain properties are not supported by the Fireworks API, see [here](https://readme.fireworks.ai/docs/openai-compatibility#api-compatibility). * Generation using multiple prompts is not supported. * * * #### Help us out by providing feedback on this documentation page: [ Previous Fake LLM ](/v0.1/docs/integrations/llms/fake/)[ Next Friendli ](/v0.1/docs/integrations/llms/friendli/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/friendli/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * Friendli On this page Friendli ======== > [Friendli](https://friendli.ai/) enhances AI application performance and optimizes cost savings with scalable, efficient deployment options, tailored for high-demand AI workloads. This tutorial guides you through integrating `Friendli` with LangChain. Setup[](#setup "Direct link to Setup") --------------------------------------- Ensure the `@langchain/community` is installed. tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/community yarn add @langchain/community pnpm add @langchain/community Sign in to [Friendli Suite](https://suite.friendli.ai/) to create a Personal Access Token, and set it as the `FRIENDLI_TOKEN` environment. You can set team id as `FRIENDLI_TEAM` environment. You can initialize a Friendli chat model with selecting the model you want to use. The default model is `mixtral-8x7b-instruct-v0-1`. You can check the available models at [docs.friendli.ai](https://docs.friendli.ai/guides/serverless_endpoints/pricing#text-generation-models). Usage[](#usage "Direct link to Usage") --------------------------------------- import { Friendli } from "@langchain/community/llms/friendli";const model = new Friendli({ model: "mixtral-8x7b-instruct-v0-1", // Default value friendliToken: process.env.FRIENDLI_TOKEN, friendliTeam: process.env.FRIENDLI_TEAM, maxTokens: 18, temperature: 0.75, topP: 0.25, frequencyPenalty: 0, stop: [],});const response = await model.invoke( "Check the Grammar: She dont like to eat vegetables, but she loves fruits.");console.log(response);/Correct: She doesn't like to eat vegetables, but she loves fruits/const stream = await model.stream( "Check the Grammar: She dont like to eat vegetables, but she loves fruits.");for await (const chunk of stream) { console.log(chunk);}/Correct: She doesn...she loves fruits/ #### API Reference: * [Friendli](https://api.js.langchain.com/classes/langchain_community_llms_friendli.Friendli.html) from `@langchain/community/llms/friendli` * * * #### Help us out by providing feedback on this documentation page: [ Previous Fireworks ](/v0.1/docs/integrations/llms/fireworks/)[ Next (Legacy) Google PaLM/VertexAI ](/v0.1/docs/integrations/llms/google_palm/) * [Setup](#setup) * [Usage](#usage) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/google_vertex_ai/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * Google Vertex AI On this page Google Vertex AI ================ Langchain.js supports two different authentication methods based on whether you're running in a Node.js environment or a web environment. Setup[](#setup "Direct link to Setup") --------------------------------------- ### Node.js[](#nodejs "Direct link to Node.js") To call Vertex AI models in Node, you'll need to install the `@langchain/google-vertexai` package: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/google-vertexai yarn add @langchain/google-vertexai pnpm add @langchain/google-vertexai You should make sure the Vertex AI API is enabled for the relevant project and that you've authenticated to Google Cloud using one of these methods: * You are logged into an account (using `gcloud auth application-default login`) permitted to that project. * You are running on a machine using a service account that is permitted to the project. * You have downloaded the credentials for a service account that is permitted to the project and set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to the path of this file. or * You set the `GOOGLE_API_KEY` environment variable to the API key for the project. ### Web[](#web "Direct link to Web") To call Vertex AI models in web environments (like Edge functions), you'll need to install the `@langchain/google-vertexai-web` package: * npm * Yarn * pnpm npm install @langchain/google-vertexai-web yarn add @langchain/google-vertexai-web pnpm add @langchain/google-vertexai-web Then, you'll need to add your service account credentials directly as a `GOOGLE_VERTEX_AI_WEB_CREDENTIALS` environment variable: GOOGLE_VERTEX_AI_WEB_CREDENTIALS={"type":"service_account","project_id":"YOUR_PROJECT-12345",...} You can also pass your credentials directly in code like this: import { VertexAI } from "@langchain/google-vertexai";// Or uncomment this line if you're using the web version:// import { VertexAI } from "@langchain/google-vertexai-web";const model = new VertexAI({ authOptions: { credentials: {"type":"service_account","project_id":"YOUR_PROJECT-12345",...}, },}); Usage[](#usage "Direct link to Usage") --------------------------------------- The entire family of `gemini` models are available by specifying the `modelName` parameter. import { VertexAI } from "@langchain/google-vertexai";// Or, if using the web entrypoint:// import { VertexAI } from "@langchain/google-vertexai-web";const model = new VertexAI({ temperature: 0.7,});const res = await model.invoke( "What would be a good company name for a company that makes colorful socks?");console.log({ res });/{ res: ' Hue Hues\n' + '* Sock Spectrum\n' + '* Kaleidosocks\n' + '* Threads of Joy\n' + '* Vibrant Threads\n' + '* Rainbow Soles\n' + '* Colorful Canvases\n' + '* Prismatic Pedals\n' + '* Sock Canvas\n' + '* Color Collective'} / #### API Reference: [VertexAI](https://api.js.langchain.com/classes/langchain_google_vertexai.VertexAI.html) from `@langchain/google-vertexai` ### Streaming[](#streaming "Direct link to Streaming") Streaming in multiple chunks is supported for faster responses: import { VertexAI } from "@langchain/google-vertexai";// Or, if using the web entrypoint:// import { VertexAI } from "@langchain/google-vertexai-web";const model = new VertexAI({ temperature: 0.7,});const stream = await model.stream( "What would be a good company name for a company that makes colorful socks?");for await (const chunk of stream) { console.log("\n---------\nChunk:\n---------\n", chunk);}/---------Chunk:--------- Kaleidoscope Toes* Huephoria* Soleful Spectrum---------Chunk:--------- Colorwave Hosiery Chromatic Threads* Rainbow Rhapsody* Vibrant Soles* Toe-tally Colorful* Socktacular Hues---------Chunk:--------- Threads of Joy---------Chunk:---------/ #### API Reference: * [VertexAI](https://api.js.langchain.com/classes/langchain_google_vertexai.VertexAI.html) from `@langchain/google-vertexai` * * * #### Help us out by providing feedback on this documentation page: [ Previous (Legacy) Google PaLM/VertexAI ](/v0.1/docs/integrations/llms/google_palm/)[ Next Gradient AI ](/v0.1/docs/integrations/llms/gradient_ai/) * [Setup](#setup) * [Node.js](#nodejs) * [Web](#web) * [Usage](#usage) * [Streaming](#streaming) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/huggingface_inference/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * HuggingFaceInference HuggingFaceInference ==================== Here's an example of calling a HugggingFaceInference model as an LLM: * npm * Yarn * pnpm npm install @huggingface/inference@2 yarn add @huggingface/inference@2 pnpm add @huggingface/inference@2 tip We're unifying model params across all packages. We now suggest using `model` instead of `modelName`, and `apiKey` for API keys. import { HuggingFaceInference } from "langchain/llms/hf";const model = new HuggingFaceInference({ model: "gpt2", apiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.HUGGINGFACEHUB_API_KEY});const res = await model.invoke("1 + 1 =");console.log({ res }); * * * #### Help us out by providing feedback on this documentation page: [ Previous Gradient AI ](/v0.1/docs/integrations/llms/gradient_ai/)[ Next Llama CPP ](/v0.1/docs/integrations/llms/llama_cpp/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/gradient_ai/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * Gradient AI On this page Gradient AI =========== LangChain.js supports integration with Gradient AI. Check out [Gradient AI](https://docs.gradient.ai/docs) for a list of available models. Setup[](#setup "Direct link to Setup") --------------------------------------- You'll need to install the official Gradient Node SDK as a peer dependency: * npm * Yarn * pnpm npm i @gradientai/nodejs-sdk yarn add @gradientai/nodejs-sdk pnpm add @gradientai/nodejs-sdk You will need to set the following environment variables for using the Gradient AI API. 1. `GRADIENT_ACCESS_TOKEN` 2. `GRADIENT_WORKSPACE_ID` Alternatively, these can be set during the GradientAI Class instantiation as `gradientAccessKey` and `workspaceId` respectively. For example: const model = new GradientLLM({ gradientAccessKey: "My secret Access Token" workspaceId: "My secret workspace id"}); Usage[](#usage "Direct link to Usage") --------------------------------------- tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/community yarn add @langchain/community pnpm add @langchain/community ### Using Gradient's Base Models[](#using-gradients-base-models "Direct link to Using Gradient's Base Models") import { GradientLLM } from "@langchain/community/llms/gradient_ai";// Note that inferenceParameters are optionalconst model = new GradientLLM({ modelSlug: "llama2-7b-chat", inferenceParameters: { maxGeneratedTokenCount: 20, temperature: 0, },});const res = await model.invoke( "What would be a good company name for a company that makes colorful socks?");console.log({ res }); #### API Reference: * [GradientLLM](https://api.js.langchain.com/classes/langchain_community_llms_gradient_ai.GradientLLM.html) from `@langchain/community/llms/gradient_ai` ### Using your own fine-tuned Adapters[](#using-your-own-fine-tuned-adapters "Direct link to Using your own fine-tuned Adapters") The use your own custom adapter simply set `adapterId` during setup. import { GradientLLM } from "@langchain/community/llms/gradient_ai";// Note that inferenceParameters are optionalconst model = new GradientLLM({ adapterId: process.env.GRADIENT_ADAPTER_ID, inferenceParameters: { maxGeneratedTokenCount: 20, temperature: 0, },});const res = await model.invoke( "What would be a good company name for a company that makes colorful socks?");console.log({ res }); #### API Reference: * [GradientLLM](https://api.js.langchain.com/classes/langchain_community_llms_gradient_ai.GradientLLM.html) from `@langchain/community/llms/gradient_ai` * * * #### Help us out by providing feedback on this documentation page: [ Previous Google Vertex AI ](/v0.1/docs/integrations/llms/google_vertex_ai/)[ Next HuggingFaceInference ](/v0.1/docs/integrations/llms/huggingface_inference/) * [Setup](#setup) * [Usage](#usage) * [Using Gradient's Base Models](#using-gradients-base-models) * [Using your own fine-tuned Adapters](#using-your-own-fine-tuned-adapters) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/llama_cpp/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * Llama CPP On this page Llama CPP ========= Compatibility Only available on Node.js. This module is based on the [node-llama-cpp](https://github.com/withcatai/node-llama-cpp) Node.js bindings for [llama.cpp](https://github.com/ggerganov/llama.cpp), allowing you to work with a locally running LLM. This allows you to work with a much smaller quantized model capable of running on a laptop environment, ideal for testing and scratch padding ideas without running up a bill! Setup[](#setup "Direct link to Setup") --------------------------------------- You'll need to install the [node-llama-cpp](https://github.com/withcatai/node-llama-cpp) module to communicate with your local model. * npm * Yarn * pnpm npm install -S node-llama-cpp yarn add node-llama-cpp pnpm add node-llama-cpp tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/community yarn add @langchain/community pnpm add @langchain/community You will also need a local Llama 2 model (or a model supported by [node-llama-cpp](https://github.com/withcatai/node-llama-cpp)). You will need to pass the path to this model to the LlamaCpp module as a part of the parameters (see example). Out-of-the-box `node-llama-cpp` is tuned for running on a MacOS platform with support for the Metal GPU of Apple M-series of processors. If you need to turn this off or need support for the CUDA architecture then refer to the documentation at [node-llama-cpp](https://withcatai.github.io/node-llama-cpp/). A note to LangChain.js contributors: if you want to run the tests associated with this module you will need to put the path to your local model in the environment variable `LLAMA_PATH`. Guide to installing Llama2[](#guide-to-installing-llama2 "Direct link to Guide to installing Llama2") ------------------------------------------------------------------------------------------------------ Getting a local Llama2 model running on your machine is a pre-req so this is a quick guide to getting and building Llama 7B (the smallest) and then quantizing it so that it will run comfortably on a laptop. To do this you will need `python3` on your machine (3.11 is recommended), also `gcc` and `make` so that `llama.cpp` can be built. ### Getting the Llama2 models[](#getting-the-llama2-models "Direct link to Getting the Llama2 models") To get a copy of Llama2 you need to visit [Meta AI](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and request access to their models. Once Meta AI grant you access, you will receive an email containing a unique URL to access the files, this will be needed in the next steps. Now create a directory to work in, for example: mkdir llama2cd llama2 Now we need to get the Meta AI `llama` repo in place so we can download the model. git clone https://github.com/facebookresearch/llama.git Once we have this in place we can change into this directory and run the downloader script to get the model we will be working with. Note: From here on its assumed that the model in use is `llama-2–7b`, if you select a different model don't forget to change the references to the model accordingly. cd llama/bin/bash ./download.sh This script will ask you for the URL that Meta AI sent to you (see above), you will also select the model to download, in this case we used `llama-2–7b`. Once this step has completed successfully (this can take some time, the `llama-2–7b` model is around 13.5Gb) there should be a new `llama-2–7b` directory containing the model and other files. ### Converting and quantizing the model[](#converting-and-quantizing-the-model "Direct link to Converting and quantizing the model") In this step we need to use `llama.cpp` so we need to download that repo. cd ..git clone https://github.com/ggerganov/llama.cpp.gitcd llama.cpp Now we need to build the `llama.cpp` tools and set up our `python` environment. In these steps it's assumed that your install of python can be run using `python3` and that the virtual environment can be called `llama2`, adjust accordingly for your own situation. makepython3 -m venv llama2source llama2/bin/activate After activating your llama2 environment you should see `(llama2)` prefixing your command prompt to let you know this is the active environment. Note: if you need to come back to build another model or re-quantize the model don't forget to activate the environment again also if you update `llama.cpp` you will need to rebuild the tools and possibly install new or updated dependencies! Now that we have an active python environment, we need to install the python dependencies. python3 -m pip install -r requirements.txt Having done this, we can start converting and quantizing the Llama2 model ready for use locally via `llama.cpp`. First, we need to convert the model, prior to the conversion let's create a directory to store it in. mkdir models/7Bpython3 convert.py --outfile models/7B/gguf-llama2-f16.bin --outtype f16 ../../llama2/llama/llama-2-7b --vocab-dir ../../llama2/llama/llama-2-7b This should create a converted model called `gguf-llama2-f16.bin` in the directory we just created. Note that this is just a converted model so it is also around 13.5Gb in size, in the next step we will quantize it down to around 4Gb. ./quantize ./models/7B/gguf-llama2-f16.bin ./models/7B/gguf-llama2-q4_0.bin q4_0 Running this should result in a new model being created in the `models\7B` directory, this one called `gguf-llama2-q4_0.bin`, this is the model we can use with langchain. You can validate this model is working by testing it using the `llama.cpp` tools. ./main -m ./models/7B/gguf-llama2-q4_0.bin -n 1024 --repeat_penalty 1.0 --color -i -r "User:" -f ./prompts/chat-with-bob.txt Running this command fires up the model for a chat session. BTW if you are running out of disk space this small model is the only one we need, so you can backup and/or delete the original and converted 13.5Gb models. Usage[](#usage "Direct link to Usage") --------------------------------------- import { LlamaCpp } from "@langchain/community/llms/llama_cpp";const llamaPath = "/Replace/with/path/to/your/model/gguf-llama2-q4_0.bin";const question = "Where do Llamas come from?";const model = new LlamaCpp({ modelPath: llamaPath });console.log(`You: ${question}`);const response = await model.invoke(question);console.log(`AI : ${response}`); #### API Reference: * [LlamaCpp](https://api.js.langchain.com/classes/langchain_community_llms_llama_cpp.LlamaCpp.html) from `@langchain/community/llms/llama_cpp` Streaming[](#streaming "Direct link to Streaming") --------------------------------------------------- import { LlamaCpp } from "@langchain/community/llms/llama_cpp";const llamaPath = "/Replace/with/path/to/your/model/gguf-llama2-q4_0.bin";const model = new LlamaCpp({ modelPath: llamaPath, temperature: 0.7 });const prompt = "Tell me a short story about a happy Llama.";const stream = await model.stream(prompt);for await (const chunk of stream) { console.log(chunk);}/* Once upon a time , in the rolling hills of Peru ... / #### API Reference: [LlamaCpp](https://api.js.langchain.com/classes/langchain_community_llms_llama_cpp.LlamaCpp.html) from `@langchain/community/llms/llama_cpp` ; * * * #### Help us out by providing feedback on this documentation page: [ Previous HuggingFaceInference ](/v0.1/docs/integrations/llms/huggingface_inference/)[ Next NIBittensor ](/v0.1/docs/integrations/llms/ni_bittensor/) * [Setup](#setup) * [Guide to installing Llama2](#guide-to-installing-llama2) * [Getting the Llama2 models](#getting-the-llama2-models) * [Converting and quantizing the model](#converting-and-quantizing-the-model) * [Usage](#usage) * [Streaming](#streaming) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/ni_bittensor/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * NIBittensor NIBittensor =========== LangChain.js offers experimental support for Neural Internet's Bittensor LLM models. Here's an example: import { NIBittensorLLM } from "langchain/experimental/llms/bittensor";const model = new NIBittensorLLM();const res = await model.invoke(`What is Bittensor?`);console.log({ res });/* { res: "\nBittensor is opensource protocol..." } / #### API Reference: [NIBittensorLLM](https://api.js.langchain.com/classes/langchain_experimental_llms_bittensor.NIBittensorLLM.html) from `langchain/experimental/llms/bittensor` * * * #### Help us out by providing feedback on this documentation page: [ Previous Llama CPP ](/v0.1/docs/integrations/llms/llama_cpp/)[ Next Ollama ](/v0.1/docs/integrations/llms/ollama/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/ollama/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * Ollama On this page Ollama ====== [Ollama](https://ollama.ai/) allows you to run open-source large language models, such as Llama 2, locally. Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. It optimizes setup and configuration details, including GPU usage. This example goes over how to use LangChain to interact with an Ollama-run Llama 2 7b instance. For a complete list of supported models and model variants, see the [Ollama model library](https://github.com/jmorganca/ollama#model-library). Setup[](#setup "Direct link to Setup") --------------------------------------- Follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance. Usage[](#usage "Direct link to Usage") --------------------------------------- tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/community yarn add @langchain/community pnpm add @langchain/community import { Ollama } from "@langchain/community/llms/ollama";const ollama = new Ollama({ baseUrl: "http://localhost:11434", // Default value model: "llama2", // Default value});const stream = await ollama.stream( `Translate "I love programming" into German.`);const chunks = [];for await (const chunk of stream) { chunks.push(chunk);}console.log(chunks.join(""));/* I'm glad to help! "I love programming" can be translated to German as "Ich liebe Programmieren." It's important to note that the translation of "I love" in German is "ich liebe," which is a more formal and polite way of saying "I love." In informal situations, people might use "mag ich" or "möchte ich" instead. Additionally, the word "Programmieren" is the correct term for "programming" in German. It's a combination of two words: "Programm" and "-ieren," which means "to do something." So, the full translation of "I love programming" would be "Ich liebe Programmieren./ #### API Reference: [Ollama](https://api.js.langchain.com/classes/langchain_community_llms_ollama.Ollama.html) from `@langchain/community/llms/ollama` Multimodal models[](#multimodal-models "Direct link to Multimodal models") --------------------------------------------------------------------------- Ollama supports open source multimodal models like [LLaVA](https://ollama.ai/library/llava) in versions 0.1.15 and up. You can bind base64 encoded image data to multimodal-capable models to use as context like this: import { Ollama } from "@langchain/community/llms/ollama";import * as fs from "node:fs/promises";const imageData = await fs.readFile("./hotdog.jpg");const model = new Ollama({ model: "llava", baseUrl: "http://127.0.0.1:11434",}).bind({ images: [imageData.toString("base64")],});const res = await model.invoke("What's in this image?");console.log({ res });/* { res: ' The image displays a hot dog sitting on top of a bun, which is placed directly on the table. The hot dog has a striped pattern on it and looks ready to be eaten.' }/ #### API Reference: [Ollama](https://api.js.langchain.com/classes/langchain_community_llms_ollama.Ollama.html) from `@langchain/community/llms/ollama` * * * #### Help us out by providing feedback on this documentation page: [ Previous NIBittensor ](/v0.1/docs/integrations/llms/ni_bittensor/)[ Next OpenAI ](/v0.1/docs/integrations/llms/openai/) * [Setup](#setup) * [Usage](#usage) * [Multimodal models](#multimodal-models) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/prompt_layer_openai/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * PromptLayer OpenAI PromptLayer OpenAI ================== LangChain integrates with PromptLayer for logging and debugging prompts and responses. To add support for PromptLayer: 1. Create a PromptLayer account here: [https://promptlayer.com](https://promptlayer.com). 2. Create an API token and pass it either as `promptLayerApiKey` argument in the `PromptLayerOpenAI` constructor or in the `PROMPTLAYER_API_KEY` environment variable. import { PromptLayerOpenAI } from "langchain/llms/openai";const model = new PromptLayerOpenAI({ temperature: 0.9, apiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.OPENAI_API_KEY promptLayerApiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.PROMPTLAYER_API_KEY});const res = await model.invoke( "What would be a good company name a company that makes colorful socks?"); Azure PromptLayerOpenAI ======================= LangChain also integrates with PromptLayer for Azure-hosted OpenAI instances: import { PromptLayerOpenAI } from "langchain/llms/openai";const model = new PromptLayerOpenAI({ temperature: 0.9, azureOpenAIApiKey: "YOUR-AOAI-API-KEY", // In Node.js defaults to process.env.AZURE_OPENAI_API_KEY azureOpenAIApiInstanceName: "YOUR-AOAI-INSTANCE-NAME", // In Node.js defaults to process.env.AZURE_OPENAI_API_INSTANCE_NAME azureOpenAIApiDeploymentName: "YOUR-AOAI-DEPLOYMENT-NAME", // In Node.js defaults to process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME azureOpenAIApiCompletionsDeploymentName: "YOUR-AOAI-COMPLETIONS-DEPLOYMENT-NAME", // In Node.js defaults to process.env.AZURE_OPENAI_API_COMPLETIONS_DEPLOYMENT_NAME azureOpenAIApiEmbeddingsDeploymentName: "YOUR-AOAI-EMBEDDINGS-DEPLOYMENT-NAME", // In Node.js defaults to process.env.AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME azureOpenAIApiVersion: "YOUR-AOAI-API-VERSION", // In Node.js defaults to process.env.AZURE_OPENAI_API_VERSION azureOpenAIBasePath: "YOUR-AZURE-OPENAI-BASE-PATH", // In Node.js defaults to process.env.AZURE_OPENAI_BASE_PATH promptLayerApiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.PROMPTLAYER_API_KEY});const res = await model.invoke( "What would be a good company name a company that makes colorful socks?"); The request and the response will be logged in the [PromptLayer dashboard](https://promptlayer.com/home). > _Note:_ In streaming mode PromptLayer will not log the response. * * * #### Help us out by providing feedback on this documentation page: [ Previous OpenAI ](/v0.1/docs/integrations/llms/openai/)[ Next RaycastAI ](/v0.1/docs/integrations/llms/raycast/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/raycast/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * RaycastAI RaycastAI ========= > Note: This is a community-built integration and is not officially supported by Raycast. You can utilize the LangChain's RaycastAI class within the [Raycast Environment](https://developers.raycast.com/api-reference/ai) to enhance your Raycast extension with Langchain's capabilities. * The RaycastAI class is only available in the Raycast environment and only to [Raycast Pro](https://www.raycast.com/pro) users as of August 2023. You may check how to create an extension for Raycast [here](https://developers.raycast.com/). * There is a rate limit of approx 10 requests per minute for each Raycast Pro user. If you exceed this limit, you will receive an error. You can set your desired rpm limit by passing `rateLimitPerMinute` to the `RaycastAI` constructor as shown in the example, as this rate limit may change in the future. tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/community yarn add @langchain/community pnpm add @langchain/community import { RaycastAI } from "@langchain/community/llms/raycast";import { showHUD } from "@raycast/api";import { initializeAgentExecutorWithOptions } from "langchain/agents";import { Tool } from "@langchain/core/tools";const model = new RaycastAI({ rateLimitPerMinute: 10, // It is 10 by default so you can omit this line model: "gpt-3.5-turbo", creativity: 0, // `creativity` is a term used by Raycast which is equivalent to `temperature` in some other LLMs});const tools: Tool[] = [ // Add your tools here];export default async function main() { // Initialize the agent executor with RaycastAI model const executor = await initializeAgentExecutorWithOptions(tools, model, { agentType: "chat-conversational-react-description", }); const input = `Describe my today's schedule as Gabriel Garcia Marquez would describe it`; const answer = await executor.invoke({ input }); await showHUD(answer.output);} #### API Reference: * [RaycastAI](https://api.js.langchain.com/classes/langchain_community_llms_raycast.RaycastAI.html) from `@langchain/community/llms/raycast` * [initializeAgentExecutorWithOptions](https://api.js.langchain.com/functions/langchain_agents.initializeAgentExecutorWithOptions.html) from `langchain/agents` * [Tool](https://api.js.langchain.com/classes/langchain_core_tools.Tool.html) from `@langchain/core/tools` * * * #### Help us out by providing feedback on this documentation page: [ Previous PromptLayer OpenAI ](/v0.1/docs/integrations/llms/prompt_layer_openai/)[ Next Replicate ](/v0.1/docs/integrations/llms/replicate/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/togetherai/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * Together AI On this page Together AI =========== Here's an example of calling a Together AI model as an LLM: import { TogetherAI } from "@langchain/community/llms/togetherai";import { PromptTemplate } from "@langchain/core/prompts";const model = new TogetherAI({ model: "mistralai/Mixtral-8x7B-Instruct-v0.1",});const prompt = PromptTemplate.fromTemplate(`System: You are a helpful assistant.User: {input}.Assistant:`);const chain = prompt.pipe(model);const response = await chain.invoke({ input: `Tell me a joke about bears`,});console.log("response", response);/*response Sure, here's a bear joke for you: Why do bears hate shoes so much? Because they like to run around in their bear feet! / #### API Reference: * [TogetherAI](https://api.js.langchain.com/classes/langchain_community_llms_togetherai.TogetherAI.html) from `@langchain/community/llms/togetherai` * [PromptTemplate](https://api.js.langchain.com/classes/langchain_core_prompts.PromptTemplate.html) from `@langchain/core/prompts` info You can see a LangSmith trace of this example [here](https://smith.langchain.com/public/f49160bd-a6cd-4234-96de-b8106a9e08a7/r) You can run other models through Together by changing the `modelName` parameter. You can find a full list of models on [Together's website](https://api.together.xyz/playground). ### Streaming[](#streaming "Direct link to Streaming") Together AI also supports streaming, this example demonstrates how to use this feature. import { TogetherAI } from "@langchain/community/llms/togetherai";import { ChatPromptTemplate } from "@langchain/core/prompts";const model = new TogetherAI({ model: "mistralai/Mixtral-8x7B-Instruct-v0.1", streaming: true,});const prompt = ChatPromptTemplate.fromMessages([ ["ai", "You are a helpful assistant."], [ "human", `Tell me a joke about bears.Assistant:`, ],]);const chain = prompt.pipe(model);const result = await chain.stream({});let fullText = "";for await (const item of result) { console.log("stream item:", item); fullText += item;}console.log(fullText);/*stream item: Surestream item: ,stream item: herestream item: 'stream item: sstream item: astream item: lightstream item: -stream item: heartstream item: edstream item: bearstream item: jokestream item: forstream item: youstream item: :stream item:stream item:stream item: Whystream item: dostream item: bearsstream item: hatestream item: shoesstream item: sostream item: muchstream item: ?stream item:stream item:stream item: Becausestream item: theystream item: likestream item: tostream item: runstream item: aroundstream item: instream item: theirstream item: bearstream item: feetstream item: !stream item: </s> Sure, here's a light-hearted bear joke for you:Why do bears hate shoes so much?Because they like to run around in their bear feet!</s> / #### API Reference: * [TogetherAI](https://api.js.langchain.com/classes/langchain_community_llms_togetherai.TogetherAI.html) from `@langchain/community/llms/togetherai` * [ChatPromptTemplate](https://api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` info You can see a LangSmith trace of this example [here](https://smith.langchain.com/public/26b5716e-6f00-47c1-aa71-1838a1eddbd1/r) * * * #### Help us out by providing feedback on this documentation page: [ Previous Replicate ](/v0.1/docs/integrations/llms/replicate/)[ Next WatsonX AI ](/v0.1/docs/integrations/llms/watsonx_ai/) * [Streaming](#streaming) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/replicate/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * Replicate Replicate ========= Here's an example of calling a Replicate model as an LLM: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install replicate @langchain/community yarn add replicate @langchain/community pnpm add replicate @langchain/community import { Replicate } from "@langchain/community/llms/replicate";const model = new Replicate({ model: "a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5",});const prompt = `User: How much wood would a woodchuck chuck if a wood chuck could chuck wood?Assistant:`;const res = await model.invoke(prompt);console.log({ res });/* { res: "I'm happy to help! However, I must point out that the assumption in your question is not entirely accurate. " + + "Woodchucks, also known as groundhogs, do not actually chuck wood. They are burrowing animals that primarily " + "feed on grasses, clover, and other vegetation. They do not have the physical ability to chuck wood.\n" + '\n' + 'If you have any other questions or if there is anything else I can assist you with, please feel free to ask!' }/ #### API Reference: [Replicate](https://api.js.langchain.com/classes/langchain_community_llms_replicate.Replicate.html) from `@langchain/community/llms/replicate` You can run other models through Replicate by changing the `model` parameter. You can find a full list of models on [Replicate's website](https://replicate.com/explore). * * * #### Help us out by providing feedback on this documentation page: [ Previous RaycastAI ](/v0.1/docs/integrations/llms/raycast/)[ Next Together AI ](/v0.1/docs/integrations/llms/togetherai/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/watsonx_ai/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * WatsonX AI On this page WatsonX AI ========== LangChain.js supports integration with IBM WatsonX AI. Checkout [WatsonX AI](https://www.ibm.com/products/watsonx-ai) for a list of available models. Setup[](#setup "Direct link to Setup") --------------------------------------- You will need to set the following environment variables for using the WatsonX AI API. 1. `IBM_CLOUD_API_KEY` which can be generated via [IBM Cloud](https://cloud.ibm.com/iam/apikeys) 2. `WATSONX_PROJECT_ID` which can be found in your [project's manage tab](https://dataplatform.cloud.ibm.com/projects/?context=wx) Alternatively, these can be set during the WatsonxAI Class instantiation as `ibmCloudApiKey` and `projectId` respectively. For example: const model = new WatsonxAI({ ibmCloudApiKey: "My secret IBM Cloud API Key" projectId: "My secret WatsonX AI Project id"}); Usage[](#usage "Direct link to Usage") --------------------------------------- tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/community yarn add @langchain/community pnpm add @langchain/community import { WatsonxAI } from "@langchain/community/llms/watsonx_ai";// Note that modelParameters are optionalconst model = new WatsonxAI({ modelId: "meta-llama/llama-2-70b-chat", modelParameters: { max_new_tokens: 100, min_new_tokens: 0, stop_sequences: [], repetition_penalty: 1, },});const res = await model.invoke( "What would be a good company name for a company that makes colorful socks?");console.log({ res }); #### API Reference: * [WatsonxAI](https://api.js.langchain.com/classes/langchain_community_llms_watsonx_ai.WatsonxAI.html) from `@langchain/community/llms/watsonx_ai` * * * #### Help us out by providing feedback on this documentation page: [ Previous Together AI ](/v0.1/docs/integrations/llms/togetherai/)[ Next Writer ](/v0.1/docs/integrations/llms/writer/) * [Setup](#setup) * [Usage](#usage) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/yandex/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * YandexGPT On this page YandexGPT ========= LangChain.js supports calling [YandexGPT](https://cloud.yandex.com/en/services/yandexgpt) LLMs. Setup[](#setup "Direct link to Setup") --------------------------------------- First, you should [create service account](https://cloud.yandex.com/en/docs/iam/operations/sa/create) with the `ai.languageModels.user` role. Next, you have two authentication options: * [IAM token](https://cloud.yandex.com/en/docs/iam/operations/iam-token/create-for-sa). You can specify the token in a constructor parameter `iam_token` or in an environment variable `YC_IAM_TOKEN`. * [API key](https://cloud.yandex.com/en/docs/iam/operations/api-key/create) You can specify the key in a constructor parameter `api_key` or in an environment variable `YC_API_KEY`. Usage[](#usage "Direct link to Usage") --------------------------------------- tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/yandex yarn add @langchain/yandex pnpm add @langchain/yandex import { YandexGPT } from "@langchain/yandex/llms";const model = new YandexGPT();const res = await model.invoke(['Translate "I love programming" into French.']);console.log({ res }); #### API Reference: * [YandexGPT](https://api.js.langchain.com/classes/langchain_yandex_llms.YandexGPT.html) from `@langchain/yandex/llms` * * * #### Help us out by providing feedback on this documentation page: [ Previous Writer ](/v0.1/docs/integrations/llms/writer/)[ Next Chat models ](/v0.1/docs/integrations/chat/) * [Setup](#setup) * [Usage](#usage) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/document_loaders/file_loaders/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [File Loaders](/v0.1/docs/integrations/document_loaders/file_loaders/) * [Folders with multiple files](/v0.1/docs/integrations/document_loaders/file_loaders/directory/) * [ChatGPT files](/v0.1/docs/integrations/document_loaders/file_loaders/chatgpt/) * [CSV files](/v0.1/docs/integrations/document_loaders/file_loaders/csv/) * [Docx files](/v0.1/docs/integrations/document_loaders/file_loaders/docx/) * [EPUB files](/v0.1/docs/integrations/document_loaders/file_loaders/epub/) * [JSON files](/v0.1/docs/integrations/document_loaders/file_loaders/json/) * [JSONLines files](/v0.1/docs/integrations/document_loaders/file_loaders/jsonlines/) * [Notion markdown export](/v0.1/docs/integrations/document_loaders/file_loaders/notion_markdown/) * [Open AI Whisper Audio](/v0.1/docs/integrations/document_loaders/file_loaders/openai_whisper_audio/) * [PDF files](/v0.1/docs/integrations/document_loaders/file_loaders/pdf/) * [PPTX files](/v0.1/docs/integrations/document_loaders/file_loaders/pptx/) * [Subtitles](/v0.1/docs/integrations/document_loaders/file_loaders/subtitles/) * [Text files](/v0.1/docs/integrations/document_loaders/file_loaders/text/) * [Unstructured](/v0.1/docs/integrations/document_loaders/file_loaders/unstructured/) * [Web Loaders](/v0.1/docs/integrations/document_loaders/web_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * File Loaders File Loaders ============ Compatibility Only available on Node.js. These loaders are used to load files given a filesystem path or a Blob object. [ 📄️ Folders with multiple files ------------------------------- This example goes over how to load data from folders with multiple files. The second argument is a map of file extensions to loader factories. Each file will be passed to the matching loader, and the resulting documents will be concatenated together. ](/v0.1/docs/integrations/document_loaders/file_loaders/directory/) [ 📄️ ChatGPT files ----------------- This example goes over how to load conversations.json from your ChatGPT data export folder. You can get your data export by email by going to: ChatGPT -> (Profile) - Settings -> Export data -> Confirm export -> Check email. ](/v0.1/docs/integrations/document_loaders/file_loaders/chatgpt/) [ 📄️ CSV files ------------- This example goes over how to load data from CSV files. The second argument is the column name to extract from the CSV file. One document will be created for each row in the CSV file. When column is not specified, each row is converted into a key/value pair with each key/value pair outputted to a new line in the document's pageContent. When column is specified, one document is created for each row, and the value of the specified column is used as the document's pageContent. ](/v0.1/docs/integrations/document_loaders/file_loaders/csv/) [ 📄️ Docx files -------------- This example goes over how to load data from docx files. ](/v0.1/docs/integrations/document_loaders/file_loaders/docx/) [ 📄️ EPUB files -------------- This example goes over how to load data from EPUB files. By default, one document will be created for each chapter in the EPUB file, you can change this behavior by setting the splitChapters option to false. ](/v0.1/docs/integrations/document_loaders/file_loaders/epub/) [ 📄️ JSON files -------------- The JSON loader use JSON pointer to target keys in your JSON files you want to target. ](/v0.1/docs/integrations/document_loaders/file_loaders/json/) [ 📄️ JSONLines files ------------------- This example goes over how to load data from JSONLines or JSONL files. The second argument is a JSONPointer to the property to extract from each JSON object in the file. One document will be created for each JSON object in the file. ](/v0.1/docs/integrations/document_loaders/file_loaders/jsonlines/) [ 📄️ Notion markdown export -------------------------- This example goes over how to load data from your Notion pages exported from the notion dashboard. ](/v0.1/docs/integrations/document_loaders/file_loaders/notion_markdown/) [ 📄️ Open AI Whisper Audio ------------------------- Only available on Node.js. ](/v0.1/docs/integrations/document_loaders/file_loaders/openai_whisper_audio/) [ 📄️ PDF files ------------- This example goes over how to load data from PDF files. By default, one document will be created for each page in the PDF file, you can change this behavior by setting the splitPages option to false. ](/v0.1/docs/integrations/document_loaders/file_loaders/pdf/) [ 📄️ PPTX files -------------- This example goes over how to load data from PPTX files. By default, one document will be created for all pages in the PPTX file. ](/v0.1/docs/integrations/document_loaders/file_loaders/pptx/) [ 📄️ Subtitles ------------- This example goes over how to load data from subtitle files. One document will be created for each subtitles file. ](/v0.1/docs/integrations/document_loaders/file_loaders/subtitles/) [ 📄️ Text files -------------- This example goes over how to load data from text files. ](/v0.1/docs/integrations/document_loaders/file_loaders/text/) [ 📄️ Unstructured ---------------- This example covers how to use Unstructured to load files of many types. Unstructured currently supports loading of text files, powerpoints, html, pdfs, images, and more. ](/v0.1/docs/integrations/document_loaders/file_loaders/unstructured/) * * * #### Help us out by providing feedback on this documentation page: [ Previous Document loaders ](/v0.1/docs/integrations/document_loaders/)[ Next Folders with multiple files ](/v0.1/docs/integrations/document_loaders/file_loaders/directory/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/llms/writer/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [LLMs](/v0.1/docs/integrations/llms/) * [AI21](/v0.1/docs/integrations/llms/ai21/) * [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/) * [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/) * [Azure OpenAI](/v0.1/docs/integrations/llms/azure/) * [Bedrock](/v0.1/docs/integrations/llms/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/llms/cohere/) * [Fake LLM](/v0.1/docs/integrations/llms/fake/) * [Fireworks](/v0.1/docs/integrations/llms/fireworks/) * [Friendli](/v0.1/docs/integrations/llms/friendli/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/) * [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/) * [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/) * [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/) * [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/llms/ollama/) * [OpenAI](/v0.1/docs/integrations/llms/openai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/) * [RaycastAI](/v0.1/docs/integrations/llms/raycast/) * [Replicate](/v0.1/docs/integrations/llms/replicate/) * [Together AI](/v0.1/docs/integrations/llms/togetherai/) * [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/) * [Writer](/v0.1/docs/integrations/llms/writer/) * [YandexGPT](/v0.1/docs/integrations/llms/yandex/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * Writer On this page Writer ====== LangChain.js supports calling [Writer](https://writer.com/) LLMs. Setup[](#setup "Direct link to Setup") --------------------------------------- First, you'll need to sign up for an account at [https://writer.com/](https://writer.com/). Create a service account and note your API key. Next, you'll need to install the official package as a peer dependency: * npm * Yarn * pnpm yarn add @writerai/writer-sdk yarn add @writerai/writer-sdk yarn add @writerai/writer-sdk tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/community yarn add @langchain/community pnpm add @langchain/community Usage[](#usage "Direct link to Usage") --------------------------------------- import { Writer } from "@langchain/community/llms/writer";const model = new Writer({ maxTokens: 20, apiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.WRITER_API_KEY orgId: "YOUR-ORGANIZATION-ID", // In Node.js defaults to process.env.WRITER_ORG_ID});const res = await model.invoke( "What would be a good company name a company that makes colorful socks?");console.log({ res }); #### API Reference: * [Writer](https://api.js.langchain.com/classes/langchain_community_llms_writer.Writer.html) from `@langchain/community/llms/writer` * * * #### Help us out by providing feedback on this documentation page: [ Previous WatsonX AI ](/v0.1/docs/integrations/llms/watsonx_ai/)[ Next YandexGPT ](/v0.1/docs/integrations/llms/yandex/) * [Setup](#setup) * [Usage](#usage) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/chat/zhipuai/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Chat models](/v0.1/docs/integrations/chat/) * [Alibaba Tongyi](/v0.1/docs/integrations/chat/alibaba_tongyi/) * [Anthropic](/v0.1/docs/integrations/chat/anthropic/) * [Anthropic Tools](/v0.1/docs/integrations/chat/anthropic_tools/) * [Azure OpenAI](/v0.1/docs/integrations/chat/azure/) * [Baidu Wenxin](/v0.1/docs/integrations/chat/baidu_wenxin/) * [Bedrock](/v0.1/docs/integrations/chat/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/chat/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/chat/cohere/) * [Fake LLM](/v0.1/docs/integrations/chat/fake/) * [Fireworks](/v0.1/docs/integrations/chat/fireworks/) * [Friendli](/v0.1/docs/integrations/chat/friendli/) * [Google GenAI](/v0.1/docs/integrations/chat/google_generativeai/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/chat/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/chat/google_vertex_ai/) * [Groq](/v0.1/docs/integrations/chat/groq/) * [Llama CPP](/v0.1/docs/integrations/chat/llama_cpp/) * [Minimax](/v0.1/docs/integrations/chat/minimax/) * [Mistral AI](/v0.1/docs/integrations/chat/mistral/) * [NIBittensorChatModel](/v0.1/docs/integrations/chat/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/chat/ollama/) * [Ollama Functions](/v0.1/docs/integrations/chat/ollama_functions/) * [OpenAI](/v0.1/docs/integrations/chat/openai/) * [PremAI](/v0.1/docs/integrations/chat/premai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/chat/prompt_layer_openai/) * [TogetherAI](/v0.1/docs/integrations/chat/togetherai/) * [WebLLM](/v0.1/docs/integrations/chat/web_llm/) * [YandexGPT](/v0.1/docs/integrations/chat/yandex/) * [ZhipuAI](/v0.1/docs/integrations/chat/zhipuai/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Chat models](/v0.1/docs/integrations/chat/) * ZhipuAI On this page ChatZhipuAI =========== LangChain.js supports the Zhipu AI family of models. [https://open.bigmodel.cn/dev/howuse/model](https://open.bigmodel.cn/dev/howuse/model) Setup[](#setup "Direct link to Setup") --------------------------------------- You'll need to sign up for an Zhipu API key and set it as an environment variable named `ZHIPUAI_API_KEY` [https://open.bigmodel.cn](https://open.bigmodel.cn) You'll also need to install the following dependencies: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/community jsonwebtoken yarn add @langchain/community jsonwebtoken pnpm add @langchain/community jsonwebtoken Usage[](#usage "Direct link to Usage") --------------------------------------- Here's an example: import { ChatZhipuAI } from "@langchain/community/chat_models/zhipuai";import { HumanMessage } from "@langchain/core/messages";// Default model is glm-3-turboconst glm3turbo = new ChatZhipuAI({ zhipuAIApiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.ZHIPUAI_API_KEY});// Use glm-4const glm4 = new ChatZhipuAI({ model: "glm-4", // Available models: temperature: 1, zhipuAIApiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.ZHIPUAI_API_KEY});const messages = [new HumanMessage("Hello")];const res = await glm3turbo.invoke(messages);/AIMessage { content: "Hello! How can I help you today? Is there something you would like to talk about or ask about? I'm here to assist you with any questions you may have.",}/const res2 = await glm4.invoke(messages);/AIMessage { text: "Hello! How can I help you today? Is there something you would like to talk about or ask about? I'm here to assist you with any questions you may have.",}/ #### API Reference: * [ChatZhipuAI](https://api.js.langchain.com/classes/langchain_community_chat_models_zhipuai.ChatZhipuAI.html) from `@langchain/community/chat_models/zhipuai` * [HumanMessage](https://api.js.langchain.com/classes/langchain_core_messages.HumanMessage.html) from `@langchain/core/messages` * * * #### Help us out by providing feedback on this documentation page: [ Previous YandexGPT ](/v0.1/docs/integrations/chat/yandex/)[ Next Document loaders ](/v0.1/docs/integrations/document_loaders/) * [Setup](#setup) * [Usage](#usage) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/document_loaders/web_loaders/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [File Loaders](/v0.1/docs/integrations/document_loaders/file_loaders/) * [Web Loaders](/v0.1/docs/integrations/document_loaders/web_loaders/) * [Cheerio](/v0.1/docs/integrations/document_loaders/web_loaders/web_cheerio/) * [Puppeteer](/v0.1/docs/integrations/document_loaders/web_loaders/web_puppeteer/) * [Playwright](/v0.1/docs/integrations/document_loaders/web_loaders/web_playwright/) * [Apify Dataset](/v0.1/docs/integrations/document_loaders/web_loaders/apify_dataset/) * [AssemblyAI Audio Transcript](/v0.1/docs/integrations/document_loaders/web_loaders/assemblyai_audio_transcription/) * [Azure Blob Storage Container](/v0.1/docs/integrations/document_loaders/web_loaders/azure_blob_storage_container/) * [Azure Blob Storage File](/v0.1/docs/integrations/document_loaders/web_loaders/azure_blob_storage_file/) * [Browserbase Loader](/v0.1/docs/integrations/document_loaders/web_loaders/browserbase/) * [College Confidential](/v0.1/docs/integrations/document_loaders/web_loaders/college_confidential/) * [Confluence](/v0.1/docs/integrations/document_loaders/web_loaders/confluence/) * [Couchbase](/v0.1/docs/integrations/document_loaders/web_loaders/couchbase/) * [Figma](/v0.1/docs/integrations/document_loaders/web_loaders/figma/) * [Firecrawl](/v0.1/docs/integrations/document_loaders/web_loaders/firecrawl/) * [GitBook](/v0.1/docs/integrations/document_loaders/web_loaders/gitbook/) * [GitHub](/v0.1/docs/integrations/document_loaders/web_loaders/github/) * [Hacker News](/v0.1/docs/integrations/document_loaders/web_loaders/hn/) * [IMSDB](/v0.1/docs/integrations/document_loaders/web_loaders/imsdb/) * [Notion API](/v0.1/docs/integrations/document_loaders/web_loaders/notionapi/) * [PDF files](/v0.1/docs/integrations/document_loaders/web_loaders/pdf/) * [Recursive URL Loader](/v0.1/docs/integrations/document_loaders/web_loaders/recursive_url_loader/) * [S3 File](/v0.1/docs/integrations/document_loaders/web_loaders/s3/) * [SearchApi Loader](/v0.1/docs/integrations/document_loaders/web_loaders/searchapi/) * [SerpAPI Loader](/v0.1/docs/integrations/document_loaders/web_loaders/serpapi/) * [Sitemap Loader](/v0.1/docs/integrations/document_loaders/web_loaders/sitemap/) * [Sonix Audio](/v0.1/docs/integrations/document_loaders/web_loaders/sonix_audio_transcription/) * [Blockchain Data](/v0.1/docs/integrations/document_loaders/web_loaders/sort_xyz_blockchain/) * [YouTube transcripts](/v0.1/docs/integrations/document_loaders/web_loaders/youtube/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * Web Loaders Web Loaders =========== These loaders are used to load web resources. [ 📄️ Cheerio ----------- This example goes over how to load data from webpages using Cheerio. One document will be created for each webpage. ](/v0.1/docs/integrations/document_loaders/web_loaders/web_cheerio/) [ 📄️ Puppeteer ------------- Only available on Node.js. ](/v0.1/docs/integrations/document_loaders/web_loaders/web_puppeteer/) [ 📄️ Playwright -------------- Only available on Node.js. ](/v0.1/docs/integrations/document_loaders/web_loaders/web_playwright/) [ 📄️ Apify Dataset ----------------- This guide shows how to use Apify with LangChain to load documents from an Apify Dataset. ](/v0.1/docs/integrations/document_loaders/web_loaders/apify_dataset/) [ 📄️ AssemblyAI Audio Transcript ------------------------------- This covers how to load audio (and video) transcripts as document objects from a file using the AssemblyAI API. ](/v0.1/docs/integrations/document_loaders/web_loaders/assemblyai_audio_transcription/) [ 📄️ Azure Blob Storage Container -------------------------------- Only available on Node.js. ](/v0.1/docs/integrations/document_loaders/web_loaders/azure_blob_storage_container/) [ 📄️ Azure Blob Storage File --------------------------- Only available on Node.js. ](/v0.1/docs/integrations/document_loaders/web_loaders/azure_blob_storage_file/) [ 📄️ Browserbase Loader ---------------------- Description ](/v0.1/docs/integrations/document_loaders/web_loaders/browserbase/) [ 📄️ College Confidential ------------------------ This example goes over how to load data from the college confidential website, using Cheerio. One document will be created for each page. ](/v0.1/docs/integrations/document_loaders/web_loaders/college_confidential/) [ 📄️ Confluence -------------- Only available on Node.js. ](/v0.1/docs/integrations/document_loaders/web_loaders/confluence/) [ 📄️ Couchbase ------------- Couchbase is an award-winning distributed NoSQL cloud database that delivers unmatched versatility, performance, scalability, and financial value for all of your cloud, mobile, AI, and edge computing applications. ](/v0.1/docs/integrations/document_loaders/web_loaders/couchbase/) [ 📄️ Figma --------- This example goes over how to load data from a Figma file. ](/v0.1/docs/integrations/document_loaders/web_loaders/figma/) [ 📄️ Firecrawl ------------- This guide shows how to use Firecrawl with LangChain to load web data into an LLM-ready format using Firecrawl. ](/v0.1/docs/integrations/document_loaders/web_loaders/firecrawl/) [ 📄️ GitBook ----------- This example goes over how to load data from any GitBook, using Cheerio. One document will be created for each page. ](/v0.1/docs/integrations/document_loaders/web_loaders/gitbook/) [ 📄️ GitHub ---------- This example goes over how to load data from a GitHub repository. ](/v0.1/docs/integrations/document_loaders/web_loaders/github/) [ 📄️ Hacker News --------------- This example goes over how to load data from the hacker news website, using Cheerio. One document will be created for each page. ](/v0.1/docs/integrations/document_loaders/web_loaders/hn/) [ 📄️ IMSDB --------- This example goes over how to load data from the internet movie script database website, using Cheerio. One document will be created for each page. ](/v0.1/docs/integrations/document_loaders/web_loaders/imsdb/) [ 📄️ Notion API -------------- This guide will take you through the steps required to load documents from Notion pages and databases using the Notion API. ](/v0.1/docs/integrations/document_loaders/web_loaders/notionapi/) [ 📄️ PDF files ------------- You can use this version of the popular PDFLoader in web environments. ](/v0.1/docs/integrations/document_loaders/web_loaders/pdf/) [ 📄️ Recursive URL Loader ------------------------ When loading content from a website, we may want to process load all URLs on a page. ](/v0.1/docs/integrations/document_loaders/web_loaders/recursive_url_loader/) [ 📄️ S3 File ----------- Only available on Node.js. ](/v0.1/docs/integrations/document_loaders/web_loaders/s3/) [ 📄️ SearchApi Loader -------------------- This guide shows how to use SearchApi with LangChain to load web search results. ](/v0.1/docs/integrations/document_loaders/web_loaders/searchapi/) [ 📄️ SerpAPI Loader ------------------ This guide shows how to use SerpAPI with LangChain to load web search results. ](/v0.1/docs/integrations/document_loaders/web_loaders/serpapi/) [ 📄️ Sitemap Loader ------------------ This notebook goes over how to use the SitemapLoader class to load sitemaps into Documents. ](/v0.1/docs/integrations/document_loaders/web_loaders/sitemap/) [ 📄️ Sonix Audio --------------- Only available on Node.js. ](/v0.1/docs/integrations/document_loaders/web_loaders/sonix_audio_transcription/) [ 📄️ Blockchain Data ------------------- This example shows how to load blockchain data, including NFT metadata and transactions for a contract address, via the sort.xyz SQL API. ](/v0.1/docs/integrations/document_loaders/web_loaders/sort_xyz_blockchain/) [ 📄️ YouTube transcripts ----------------------- This covers how to load youtube transcript into LangChain documents. ](/v0.1/docs/integrations/document_loaders/web_loaders/youtube/) * * * #### Help us out by providing feedback on this documentation page: [ Previous Unstructured ](/v0.1/docs/integrations/document_loaders/file_loaders/unstructured/)[ Next Cheerio ](/v0.1/docs/integrations/document_loaders/web_loaders/web_cheerio/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/chat/alibaba_tongyi/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Chat models](/v0.1/docs/integrations/chat/) * [Alibaba Tongyi](/v0.1/docs/integrations/chat/alibaba_tongyi/) * [Anthropic](/v0.1/docs/integrations/chat/anthropic/) * [Anthropic Tools](/v0.1/docs/integrations/chat/anthropic_tools/) * [Azure OpenAI](/v0.1/docs/integrations/chat/azure/) * [Baidu Wenxin](/v0.1/docs/integrations/chat/baidu_wenxin/) * [Bedrock](/v0.1/docs/integrations/chat/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/chat/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/chat/cohere/) * [Fake LLM](/v0.1/docs/integrations/chat/fake/) * [Fireworks](/v0.1/docs/integrations/chat/fireworks/) * [Friendli](/v0.1/docs/integrations/chat/friendli/) * [Google GenAI](/v0.1/docs/integrations/chat/google_generativeai/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/chat/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/chat/google_vertex_ai/) * [Groq](/v0.1/docs/integrations/chat/groq/) * [Llama CPP](/v0.1/docs/integrations/chat/llama_cpp/) * [Minimax](/v0.1/docs/integrations/chat/minimax/) * [Mistral AI](/v0.1/docs/integrations/chat/mistral/) * [NIBittensorChatModel](/v0.1/docs/integrations/chat/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/chat/ollama/) * [Ollama Functions](/v0.1/docs/integrations/chat/ollama_functions/) * [OpenAI](/v0.1/docs/integrations/chat/openai/) * [PremAI](/v0.1/docs/integrations/chat/premai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/chat/prompt_layer_openai/) * [TogetherAI](/v0.1/docs/integrations/chat/togetherai/) * [WebLLM](/v0.1/docs/integrations/chat/web_llm/) * [YandexGPT](/v0.1/docs/integrations/chat/yandex/) * [ZhipuAI](/v0.1/docs/integrations/chat/zhipuai/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Chat models](/v0.1/docs/integrations/chat/) * Alibaba Tongyi On this page ChatAlibabaTongyi ================= LangChain.js supports the Alibaba qwen family of models. Setup[](#setup "Direct link to Setup") --------------------------------------- You'll need to sign up for an Alibaba API key and set it as an environment variable named `ALIBABA_API_KEY`. Then, you'll need to install the [`@langchain/community`](https://www.npmjs.com/package/@langchain/community) package: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/community yarn add @langchain/community pnpm add @langchain/community Usage[](#usage "Direct link to Usage") --------------------------------------- Here's an example: import { ChatAlibabaTongyi } from "@langchain/community/chat_models/alibaba_tongyi";import { HumanMessage } from "@langchain/core/messages";// Default model is qwen-turboconst qwenTurbo = new ChatAlibabaTongyi({ alibabaApiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.ALIBABA_API_KEY});// Use qwen-plusconst qwenPlus = new ChatAlibabaTongyi({ model: "qwen-plus", // Available models: qwen-turbo, qwen-plus, qwen-max temperature: 1, alibabaApiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.ALIBABA_API_KEY});const messages = [new HumanMessage("Hello")];const res = await qwenTurbo.invoke(messages);/AIMessage { content: "Hello! How can I help you today? Is there something you would like to talk about or ask about? I'm here to assist you with any questions you may have.",}/const res2 = await qwenPlus.invoke(messages);/AIMessage { text: "Hello! How can I help you today? Is there something you would like to talk about or ask about? I'm here to assist you with any questions you may have.",}/ #### API Reference: * [ChatAlibabaTongyi](https://api.js.langchain.com/classes/langchain_community_chat_models_alibaba_tongyi.ChatAlibabaTongyi.html) from `@langchain/community/chat_models/alibaba_tongyi` * [HumanMessage](https://api.js.langchain.com/classes/langchain_core_messages.HumanMessage.html) from `@langchain/core/messages` * * * #### Help us out by providing feedback on this documentation page: [ Previous Chat models ](/v0.1/docs/integrations/chat/)[ Next Anthropic ](/v0.1/docs/integrations/chat/anthropic/) * [Setup](#setup) * [Usage](#usage) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/chat/baidu_wenxin/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Chat models](/v0.1/docs/integrations/chat/) * [Alibaba Tongyi](/v0.1/docs/integrations/chat/alibaba_tongyi/) * [Anthropic](/v0.1/docs/integrations/chat/anthropic/) * [Anthropic Tools](/v0.1/docs/integrations/chat/anthropic_tools/) * [Azure OpenAI](/v0.1/docs/integrations/chat/azure/) * [Baidu Wenxin](/v0.1/docs/integrations/chat/baidu_wenxin/) * [Bedrock](/v0.1/docs/integrations/chat/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/chat/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/chat/cohere/) * [Fake LLM](/v0.1/docs/integrations/chat/fake/) * [Fireworks](/v0.1/docs/integrations/chat/fireworks/) * [Friendli](/v0.1/docs/integrations/chat/friendli/) * [Google GenAI](/v0.1/docs/integrations/chat/google_generativeai/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/chat/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/chat/google_vertex_ai/) * [Groq](/v0.1/docs/integrations/chat/groq/) * [Llama CPP](/v0.1/docs/integrations/chat/llama_cpp/) * [Minimax](/v0.1/docs/integrations/chat/minimax/) * [Mistral AI](/v0.1/docs/integrations/chat/mistral/) * [NIBittensorChatModel](/v0.1/docs/integrations/chat/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/chat/ollama/) * [Ollama Functions](/v0.1/docs/integrations/chat/ollama_functions/) * [OpenAI](/v0.1/docs/integrations/chat/openai/) * [PremAI](/v0.1/docs/integrations/chat/premai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/chat/prompt_layer_openai/) * [TogetherAI](/v0.1/docs/integrations/chat/togetherai/) * [WebLLM](/v0.1/docs/integrations/chat/web_llm/) * [YandexGPT](/v0.1/docs/integrations/chat/yandex/) * [ZhipuAI](/v0.1/docs/integrations/chat/zhipuai/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Chat models](/v0.1/docs/integrations/chat/) * Baidu Wenxin ChatBaiduWenxin =============== LangChain.js supports Baidu's ERNIE-bot family of models. Here's an example: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/community yarn add @langchain/community pnpm add @langchain/community Available models: `ERNIE-Bot`,`ERNIE-Bot-turbo`,`ERNIE-Bot-4`,`ERNIE-Speed-8K`,`ERNIE-Speed-128K`,`ERNIE-4.0-8K`, `ERNIE-4.0-8K-Preview`,`ERNIE-3.5-8K`,`ERNIE-3.5-8K-Preview`,`ERNIE-Lite-8K`,`ERNIE-Tiny-8K`,`ERNIE-Character-8K`, `ERNIE Speed-AppBuilder` import { ChatBaiduWenxin } from "@langchain/community/chat_models/baiduwenxin";import { HumanMessage } from "@langchain/core/messages";// Default model is ERNIE-Bot-turboconst ernieTurbo = new ChatBaiduWenxin({ baiduApiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.BAIDU_API_KEY baiduSecretKey: "YOUR-SECRET-KEY", // In Node.js defaults to process.env.BAIDU_SECRET_KEY});// Use ERNIE-Botconst ernie = new ChatBaiduWenxin({ model: "ERNIE-Bot", // Available models are shown above temperature: 1, baiduApiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.BAIDU_API_KEY baiduSecretKey: "YOUR-SECRET-KEY", // In Node.js defaults to process.env.BAIDU_SECRET_KEY});const messages = [new HumanMessage("Hello")];let res = await ernieTurbo.invoke(messages);/AIChatMessage { text: 'Hello! How may I assist you today?', name: undefined, additional_kwargs: {} }}/res = await ernie.invoke(messages);/AIChatMessage { text: 'Hello! How may I assist you today?', name: undefined, additional_kwargs: {} }}/ #### API Reference: * [ChatBaiduWenxin](https://api.js.langchain.com/classes/langchain_community_chat_models_baiduwenxin.ChatBaiduWenxin.html) from `@langchain/community/chat_models/baiduwenxin` * [HumanMessage](https://api.js.langchain.com/classes/langchain_core_messages.HumanMessage.html) from `@langchain/core/messages` * * * #### Help us out by providing feedback on this documentation page: [ Previous Azure OpenAI ](/v0.1/docs/integrations/chat/azure/)[ Next Bedrock ](/v0.1/docs/integrations/chat/bedrock/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/chat/bedrock/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Chat models](/v0.1/docs/integrations/chat/) * [Alibaba Tongyi](/v0.1/docs/integrations/chat/alibaba_tongyi/) * [Anthropic](/v0.1/docs/integrations/chat/anthropic/) * [Anthropic Tools](/v0.1/docs/integrations/chat/anthropic_tools/) * [Azure OpenAI](/v0.1/docs/integrations/chat/azure/) * [Baidu Wenxin](/v0.1/docs/integrations/chat/baidu_wenxin/) * [Bedrock](/v0.1/docs/integrations/chat/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/chat/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/chat/cohere/) * [Fake LLM](/v0.1/docs/integrations/chat/fake/) * [Fireworks](/v0.1/docs/integrations/chat/fireworks/) * [Friendli](/v0.1/docs/integrations/chat/friendli/) * [Google GenAI](/v0.1/docs/integrations/chat/google_generativeai/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/chat/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/chat/google_vertex_ai/) * [Groq](/v0.1/docs/integrations/chat/groq/) * [Llama CPP](/v0.1/docs/integrations/chat/llama_cpp/) * [Minimax](/v0.1/docs/integrations/chat/minimax/) * [Mistral AI](/v0.1/docs/integrations/chat/mistral/) * [NIBittensorChatModel](/v0.1/docs/integrations/chat/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/chat/ollama/) * [Ollama Functions](/v0.1/docs/integrations/chat/ollama_functions/) * [OpenAI](/v0.1/docs/integrations/chat/openai/) * [PremAI](/v0.1/docs/integrations/chat/premai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/chat/prompt_layer_openai/) * [TogetherAI](/v0.1/docs/integrations/chat/togetherai/) * [WebLLM](/v0.1/docs/integrations/chat/web_llm/) * [YandexGPT](/v0.1/docs/integrations/chat/yandex/) * [ZhipuAI](/v0.1/docs/integrations/chat/zhipuai/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Chat models](/v0.1/docs/integrations/chat/) * Bedrock On this page BedrockChat =========== > [Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes Foundation Models (FMs) from leading AI startups and Amazon available via an API. You can choose from a wide range of FMs to find the model that is best suited for your use case. Setup[](#setup "Direct link to Setup") --------------------------------------- You'll need to install the `@langchain/community` package: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/community yarn add @langchain/community pnpm add @langchain/community Then, you'll need to install a few official AWS packages as peer dependencies: * npm * Yarn * pnpm npm install @aws-crypto/sha256-js @aws-sdk/credential-provider-node @smithy/protocol-http @smithy/signature-v4 @smithy/eventstream-codec @smithy/util-utf8 @aws-sdk/types yarn add @aws-crypto/sha256-js @aws-sdk/credential-provider-node @smithy/protocol-http @smithy/signature-v4 @smithy/eventstream-codec @smithy/util-utf8 @aws-sdk/types pnpm add @aws-crypto/sha256-js @aws-sdk/credential-provider-node @smithy/protocol-http @smithy/signature-v4 @smithy/eventstream-codec @smithy/util-utf8 @aws-sdk/types You can also use BedrockChat in web environments such as Edge functions or Cloudflare Workers by omitting the `@aws-sdk/credential-provider-node` dependency and using the `web` entrypoint: * npm * Yarn * pnpm npm install @aws-crypto/sha256-js @smithy/protocol-http @smithy/signature-v4 @smithy/eventstream-codec @smithy/util-utf8 @aws-sdk/types yarn add @aws-crypto/sha256-js @smithy/protocol-http @smithy/signature-v4 @smithy/eventstream-codec @smithy/util-utf8 @aws-sdk/types pnpm add @aws-crypto/sha256-js @smithy/protocol-http @smithy/signature-v4 @smithy/eventstream-codec @smithy/util-utf8 @aws-sdk/types Usage[](#usage "Direct link to Usage") --------------------------------------- tip We're unifying model params across all packages. We now suggest using `model` instead of `modelName`, and `apiKey` for API keys. Currently, only Anthropic, Cohere, and Mistral models are supported with the chat model integration. For foundation models from AI21 or Amazon, see [the text generation Bedrock variant](/v0.1/docs/integrations/llms/bedrock/). import { BedrockChat } from "@langchain/community/chat_models/bedrock";// Or, from web environments:// import { BedrockChat } from "@langchain/community/chat_models/bedrock/web";import { HumanMessage } from "@langchain/core/messages";// If no credentials are provided, the default credentials from// @aws-sdk/credential-provider-node will be used.// modelKwargs are additional parameters passed to the model when it// is invoked.const model = new BedrockChat({ model: "anthropic.claude-3-sonnet-20240229-v1:0", region: "us-east-1", // endpointUrl: "custom.amazonaws.com", // credentials: { // accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID!, // secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY!, // }, // modelKwargs: { // anthropic_version: "bedrock-2023-05-31", // },});// Other model names include:// "mistral.mistral-7b-instruct-v0:2"// "mistral.mixtral-8x7b-instruct-v0:1"//// For a full list, see the Bedrock page in AWS.const res = await model.invoke([ new HumanMessage({ content: "Tell me a joke" }),]);console.log(res);/* AIMessage { content: "Here's a silly joke for you:\n" + '\n' + "Why can't a bicycle stand up by itself?\n" + "Because it's two-tired!", name: undefined, additional_kwargs: { id: 'msg_01NYN7Rf39k4cgurqpZWYyDh' } }/const stream = await model.stream([ new HumanMessage({ content: "Tell me a joke" }),]);for await (const chunk of stream) { console.log(chunk.content);}/ Here 's a silly joke for you : Why can 't a bicycle stand up by itself ? Because it 's two - tired !/ #### API Reference: [BedrockChat](https://api.js.langchain.com/classes/langchain_community_chat_models_bedrock.BedrockChat.html) from `@langchain/community/chat_models/bedrock` * [HumanMessage](https://api.js.langchain.com/classes/langchain_core_messages.HumanMessage.html) from `@langchain/core/messages` Multimodal inputs[](#multimodal-inputs "Direct link to Multimodal inputs") --------------------------------------------------------------------------- tip Multimodal inputs are currently only supported by Anthropic Claude-3 models. Anthropic Claude-3 models hosted on Bedrock have multimodal capabilities and can reason about images. Here's an example: import * as fs from "node:fs/promises";import { BedrockChat } from "@langchain/community/chat_models/bedrock";// Or, from web environments:// import { BedrockChat } from "@langchain/community/chat_models/bedrock/web";import { HumanMessage } from "@langchain/core/messages";// If no credentials are provided, the default credentials from// @aws-sdk/credential-provider-node will be used.// modelKwargs are additional parameters passed to the model when it// is invoked.const model = new BedrockChat({ model: "anthropic.claude-3-sonnet-20240229-v1:0", region: "us-east-1", // endpointUrl: "custom.amazonaws.com", // credentials: { // accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID!, // secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY!, // }, // modelKwargs: { // anthropic_version: "bedrock-2023-05-31", // },});const imageData = await fs.readFile("./hotdog.jpg");const res = await model.invoke([ new HumanMessage({ content: [ { type: "text", text: "What's in this image?", }, { type: "image_url", image_url: { url: `data:image/jpeg;base64,${imageData.toString("base64")}`, }, }, ], }),]);console.log(res);/* AIMessage { content: 'The image shows a hot dog or frankfurter. It has a reddish-pink sausage filling encased in a light brown bread-like bun. The hot dog bun is split open, revealing the sausage inside. This classic fast food item is a popular snack or meal, often served at events like baseball games or cookouts. The hot dog appears to be against a plain white background, allowing the details and textures of the food item to be clearly visible.', name: undefined, additional_kwargs: { id: 'msg_01XrLPL9vCb82U3Wrrpza18p' } }/ #### API Reference: [BedrockChat](https://api.js.langchain.com/classes/langchain_community_chat_models_bedrock.BedrockChat.html) from `@langchain/community/chat_models/bedrock` * [HumanMessage](https://api.js.langchain.com/classes/langchain_core_messages.HumanMessage.html) from `@langchain/core/messages` * * * #### Help us out by providing feedback on this documentation page: [ Previous Baidu Wenxin ](/v0.1/docs/integrations/chat/baidu_wenxin/)[ Next Cloudflare Workers AI ](/v0.1/docs/integrations/chat/cloudflare_workersai/) * [Setup](#setup) * [Usage](#usage) * [Multimodal inputs](#multimodal-inputs) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/chat/cohere/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Chat models](/v0.1/docs/integrations/chat/) * [Alibaba Tongyi](/v0.1/docs/integrations/chat/alibaba_tongyi/) * [Anthropic](/v0.1/docs/integrations/chat/anthropic/) * [Anthropic Tools](/v0.1/docs/integrations/chat/anthropic_tools/) * [Azure OpenAI](/v0.1/docs/integrations/chat/azure/) * [Baidu Wenxin](/v0.1/docs/integrations/chat/baidu_wenxin/) * [Bedrock](/v0.1/docs/integrations/chat/bedrock/) * [Cloudflare Workers AI](/v0.1/docs/integrations/chat/cloudflare_workersai/) * [Cohere](/v0.1/docs/integrations/chat/cohere/) * [Fake LLM](/v0.1/docs/integrations/chat/fake/) * [Fireworks](/v0.1/docs/integrations/chat/fireworks/) * [Friendli](/v0.1/docs/integrations/chat/friendli/) * [Google GenAI](/v0.1/docs/integrations/chat/google_generativeai/) * [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/chat/google_palm/) * [Google Vertex AI](/v0.1/docs/integrations/chat/google_vertex_ai/) * [Groq](/v0.1/docs/integrations/chat/groq/) * [Llama CPP](/v0.1/docs/integrations/chat/llama_cpp/) * [Minimax](/v0.1/docs/integrations/chat/minimax/) * [Mistral AI](/v0.1/docs/integrations/chat/mistral/) * [NIBittensorChatModel](/v0.1/docs/integrations/chat/ni_bittensor/) * [Ollama](/v0.1/docs/integrations/chat/ollama/) * [Ollama Functions](/v0.1/docs/integrations/chat/ollama_functions/) * [OpenAI](/v0.1/docs/integrations/chat/openai/) * [PremAI](/v0.1/docs/integrations/chat/premai/) * [PromptLayer OpenAI](/v0.1/docs/integrations/chat/prompt_layer_openai/) * [TogetherAI](/v0.1/docs/integrations/chat/togetherai/) * [WebLLM](/v0.1/docs/integrations/chat/web_llm/) * [YandexGPT](/v0.1/docs/integrations/chat/yandex/) * [ZhipuAI](/v0.1/docs/integrations/chat/zhipuai/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Chat models](/v0.1/docs/integrations/chat/) * Cohere On this page ChatCohere ========== info The Cohere Chat API is still in beta. This means Cohere may make breaking changes at any time. Setup[](#setup "Direct link to Setup") --------------------------------------- In order to use the LangChain.js Cohere integration you'll need an API key. You can sign up for a Cohere account and create an API key [here](https://dashboard.cohere.com/welcome/register). You'll first need to install the [`@langchain/cohere`](https://www.npmjs.com/package/@langchain/cohere) package: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/cohere yarn add @langchain/cohere pnpm add @langchain/cohere Usage[](#usage "Direct link to Usage") --------------------------------------- import { ChatCohere } from "@langchain/cohere";import { ChatPromptTemplate } from "@langchain/core/prompts";const model = new ChatCohere({ apiKey: process.env.COHERE_API_KEY, // Default model: "command", // Default});const prompt = ChatPromptTemplate.fromMessages([ ["ai", "You are a helpful assistant"], ["human", "{input}"],]);const chain = prompt.pipe(model);const response = await chain.invoke({ input: "Hello there friend!",});console.log("response", response);/*response AIMessage { lc_serializable: true, lc_namespace: [ 'langchain_core', 'messages' ], content: "Hi there! I'm not your friend, but I'm happy to help you in whatever way I can today. How are you doing? Is there anything I can assist you with? I am an AI chatbot capable of generating thorough responses, and I'm designed to have helpful, inclusive conversations with users. \n" + '\n' + "If you have any questions, feel free to ask away, and I'll do my best to provide you with helpful responses. \n" + '\n' + 'Would you like me to help you with anything in particular right now?', additional_kwargs: { response_id: 'c6baa057-ef94-4bb0-9c25-3a424963a074', generationId: 'd824fcdc-b922-4ae6-8d45-7b65a21cdd6a', token_count: { prompt_tokens: 66, response_tokens: 104, total_tokens: 170, billed_tokens: 159 }, meta: { api_version: [Object], billed_units: [Object] }, tool_inputs: null }} / #### API Reference: * [ChatCohere](https://api.js.langchain.com/classes/langchain_cohere.ChatCohere.html) from `@langchain/cohere` * [ChatPromptTemplate](https://api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` info You can see a LangSmith trace of this example [here](https://smith.langchain.com/public/69ccd2aa-b651-4f07-9223-ecc0b77e645e/r) ### Streaming[](#streaming "Direct link to Streaming") Cohere's API also supports streaming token responses. The example below demonstrates how to use this feature. import { ChatCohere } from "@langchain/cohere";import { ChatPromptTemplate } from "@langchain/core/prompts";import { StringOutputParser } from "@langchain/core/output_parsers";const model = new ChatCohere({ apiKey: process.env.COHERE_API_KEY, // Default model: "command", // Default});const prompt = ChatPromptTemplate.fromMessages([ ["ai", "You are a helpful assistant"], ["human", "{input}"],]);const outputParser = new StringOutputParser();const chain = prompt.pipe(model).pipe(outputParser);const response = await chain.stream({ input: "Why is the sky blue? Be concise with your answer.",});let streamTokens = "";let streamIters = 0;for await (const item of response) { streamTokens += item; streamIters += 1;}console.log("stream tokens:", streamTokens);console.log("stream iters:", streamIters);/*stream item:stream item: Hello! I'm here to help answer any questions youstream item: might have or assist you with any task you'd like tostream item: accomplish. I can provide informationstream item: on a wide range of topicsstream item: , from math and science to history and literature. I canstream item: also help you manage your schedule, set reminders, andstream item: much more. Is there something specific you need help with? Letstream item: me know!stream item: / #### API Reference: * [ChatCohere](https://api.js.langchain.com/classes/langchain_cohere.ChatCohere.html) from `@langchain/cohere` * [ChatPromptTemplate](https://api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [StringOutputParser](https://api.js.langchain.com/classes/langchain_core_output_parsers.StringOutputParser.html) from `@langchain/core/output_parsers` info You can see a LangSmith trace of this example [here](https://smith.langchain.com/public/36ae0564-b096-4ec1-9318-1f82fe705fe8/r) ### Stateful conversation API[](#stateful-conversation-api "Direct link to Stateful conversation API") Cohere's chat API supports stateful conversations. This means the API stores previous chat messages which can be accessed by passing in a `conversation_id` field. The example below demonstrates how to use this feature. import { ChatCohere } from "@langchain/cohere";import { HumanMessage } from "@langchain/core/messages";const model = new ChatCohere({ apiKey: process.env.COHERE_API_KEY, // Default model: "command", // Default});const conversationId = `demo_test_id-${Math.random()}`;const response = await model.invoke( [new HumanMessage("Tell me a joke about bears.")], { conversationId, });console.log("response: ", response.content);/*response: Why did the bear go to the dentist?Because she had bear teeth!Hope you found that joke about bears to be a little bit tooth-arious!Would you like me to tell you another one? I could also provide you with a list of jokes about bears if you prefer.Just let me know if you have any other jokes or topics you'd like to hear about! /const response2 = await model.invoke( [new HumanMessage("What was the subject of my last question?")], { conversationId, });console.log("response2: ", response2.content);/*response2: Your last question was about bears. You asked me to tell you a joke about bears, which I am programmed to assist with.Would you like me to assist you with anything else bear-related? I can provide you with facts about bears, stories about bears, or even list other topics that might be of interest to you.Please let me know if you have any other questions and I will do my best to provide you with a response. / #### API Reference: * [ChatCohere](https://api.js.langchain.com/classes/langchain_cohere.ChatCohere.html) from `@langchain/cohere` * [HumanMessage](https://api.js.langchain.com/classes/langchain_core_messages.HumanMessage.html) from `@langchain/core/messages` info You can see the LangSmith traces from this example [here](https://smith.langchain.com/public/8e67b05a-4e63-414e-ac91-a91acf21b262/r) and [here](https://smith.langchain.com/public/50fabc25-46fe-4727-a59c-7e4eb0de8e70/r) ### RAG[](#rag "Direct link to RAG") Cohere also comes out of the box with RAG support. You can pass in documents as context to the API request and Cohere's models will use them when generating responses. import { ChatCohere } from "@langchain/cohere";import { HumanMessage } from "@langchain/core/messages";const model = new ChatCohere({ apiKey: process.env.COHERE_API_KEY, // Default model: "command", // Default});const documents = [ { title: "Harrison's work", snippet: "Harrison worked at Kensho as an engineer.", }, { title: "Harrison's work duration", snippet: "Harrison worked at Kensho for 3 years.", }, { title: "Polar berars in the Appalachian Mountains", snippet: "Polar bears have surprisingly adapted to the Appalachian Mountains, thriving in the diverse, forested terrain despite their traditional arctic habitat. This unique situation has sparked significant interest and study in climate adaptability and wildlife behavior.", },];const response = await model.invoke( [new HumanMessage("Where did Harrison work and for how long?")], { documents, });console.log("response: ", response.content);/*response: Harrison worked as an engineer at Kensho for about 3 years. / #### API Reference: * [ChatCohere](https://api.js.langchain.com/classes/langchain_cohere.ChatCohere.html) from `@langchain/cohere` * [HumanMessage](https://api.js.langchain.com/classes/langchain_core_messages.HumanMessage.html) from `@langchain/core/messages` info You can see a LangSmith trace of this example [here](https://smith.langchain.com/public/de71fffe-6f01-4c36-9b49-40d1bc87dea3/r) ### Connectors[](#connectors "Direct link to Connectors") The API also allows for other connections which are not static documents. An example of this is their `web-search` connector which allows you to pass in a query and the API will search the web for relevant documents. The example below demonstrates how to use this feature. import { ChatCohere } from "@langchain/cohere";import { HumanMessage } from "@langchain/core/messages";const model = new ChatCohere({ apiKey: process.env.COHERE_API_KEY, // Default model: "command", // Default});const response = await model.invoke( [new HumanMessage("How tall are the largest pengiuns?")], { connectors: [{ id: "web-search" }], });console.log("response: ", JSON.stringify(response, null, 2));/*response: { "lc": 1, "type": "constructor", "id": [ "langchain_core", "messages", "AIMessage" ], "kwargs": { "content": "The tallest penguin species currently in existence is the Emperor Penguin, with a height of 110cm to the top of their head or 115cm to the tip of their beak. This is equivalent to being approximately 3 feet and 7 inches tall.\n\nA fossil of an Anthropornis penguin was found in New Zealand and is suspected to have been even taller at 1.7 metres, though this is uncertain as the fossil is only known from preserved arm and leg bones. The height of a closely related species, Kumimanu biceae, has been estimated at 1.77 metres.\n\nDid you know that because larger-bodied penguins can hold their breath for longer, the colossus penguin could have stayed underwater for 40 minutes or more?", "additional_kwargs": { "response_id": "a3567a59-2377-439d-894f-0309f7fea1de", "generationId": "65dc5b1b-6099-44c4-8338-50eed0d427c5", "token_count": { "prompt_tokens": 1394, "response_tokens": 149, "total_tokens": 1543, "billed_tokens": 159 }, "meta": { "api_version": { "version": "1" }, "billed_units": { "input_tokens": 10, "output_tokens": 149 } }, "citations": [ { "start": 58, "end": 73, "text": "Emperor Penguin", "documentIds": [ "web-search_3:2", "web-search_4:10" ] }, { "start": 92, "end": 157, "text": "110cm to the top of their head or 115cm to the tip of their beak.", "documentIds": [ "web-search_4:10" ] }, { "start": 200, "end": 225, "text": "3 feet and 7 inches tall.", "documentIds": [ "web-search_3:2", "web-search_4:10" ] }, { "start": 242, "end": 262, "text": "Anthropornis penguin", "documentIds": [ "web-search_9:4" ] }, { "start": 276, "end": 287, "text": "New Zealand", "documentIds": [ "web-search_9:4" ] }, { "start": 333, "end": 343, "text": "1.7 metres", "documentIds": [ "web-search_9:4" ] }, { "start": 403, "end": 431, "text": "preserved arm and leg bones.", "documentIds": [ "web-search_9:4" ] }, { "start": 473, "end": 488, "text": "Kumimanu biceae", "documentIds": [ "web-search_9:4" ] }, { "start": 512, "end": 524, "text": "1.77 metres.", "documentIds": [ "web-search_9:4" ] }, { "start": 613, "end": 629, "text": "colossus penguin", "documentIds": [ "web-search_3:2" ] }, { "start": 663, "end": 681, "text": "40 minutes or more", "documentIds": [ "web-search_3:2" ] } ], "documents": [ { "id": "web-search_3:2", "snippet": " By comparison, the largest species of penguin alive today, the emperor penguin, is \"only\" about 4 feet tall and can weigh as much as 100 pounds.\n\nInterestingly, because larger bodied penguins can hold their breath for longer, the colossus penguin probably could have stayed underwater for 40 minutes or more. It boggles the mind to imagine the kinds of huge, deep sea fish this mammoth bird might have been capable of hunting.\n\nThe fossil was found at the La Meseta formation on Seymour Island, an island in a chain of 16 major islands around the tip of the Graham Land on the Antarctic Peninsula.", "title": "Giant 6-Foot-8 Penguin Discovered in Antarctica", "url": "https://www.treehugger.com/giant-foot-penguin-discovered-in-antarctica-4864169" }, { "id": "web-search_4:10", "snippet": "\n\nWhat is the Tallest Penguin?\n\nThe tallest penguin is the Emperor Penguin which is 110cm to the top of their head or 115cm to the tip of their beak.\n\nHow Tall Are Emperor Penguins in Feet?\n\nAn Emperor Penguin is about 3 feet and 7 inches to the top of its head. They are the largest penguin species currently in existence.\n\nHow Much Do Penguins Weigh in Pounds?\n\nPenguins weigh between 2.5lbs for the smallest species, the Little Penguin, up to 82lbs for the largest species, the Emperor Penguin.\n\nDr. Jackie Symmons is a professional ecologist with a Ph.D. in Ecology and Wildlife Management from Bangor University and over 25 years of experience delivering conservation projects.", "title": "How Big Are Penguins? [Height & Weight of Every Species] - Polar Guidebook", "url": "https://polarguidebook.com/how-big-are-penguins/" }, { "id": "web-search_9:4", "snippet": "\n\nA fossil of an Anthropornis penguin found on the island may have been even taller, but this is likely to be an exception. The majority of these penguins were only 1.7 metres tall and weighed around 80 kilogrammes.\n\nWhile Palaeeudyptes klekowskii remains the tallest ever penguin, it is no longer the heaviest. At an estimated 150 kilogrammes, Kumimanu fordycei would have been around three times heavier than any living penguin.\n\nWhile it's uncertain how tall the species was, the height of a closely related species, Kumimanu biceae, has been estimated at 1.77 metres.\n\nThese measurements, however, are all open for debate. Many fossil penguins are only known from preserved arm and leg bones, rather than complete skeletons.", "title": "The largest ever penguin species has been discovered in New Zealand \| Natural History Museum", "url": "https://www.nhm.ac.uk/discover/news/2023/february/largest-ever-penguin-species-discovered-new-zealand.html" } ], "searchResults": [ { "searchQuery": { "text": "largest penguin species height", "generationId": "908fe321-5d27-48c4-bdb6-493be5687344" }, "documentIds": [ "web-search_3:2", "web-search_4:10", "web-search_9:4" ], "connector": { "id": "web-search" } } ], "tool_inputs": null, "searchQueries": [ { "text": "largest penguin species height", "generationId": "908fe321-5d27-48c4-bdb6-493be5687344" } ] } }} / #### API Reference: * [ChatCohere](https://api.js.langchain.com/classes/langchain_cohere.ChatCohere.html) from `@langchain/cohere` * [HumanMessage](https://api.js.langchain.com/classes/langchain_core_messages.HumanMessage.html) from `@langchain/core/messages` info You can see a LangSmith trace of this example [here](https://smith.langchain.com/public/9a6f996b-cff2-4f3f-916a-640469a5a963/r) We can see in the `kwargs` object that the API request did a few things: * Performed a search query, storing the result data in the `searchQueries` and `searchResults` fields. In the `searchQueries` field we see they rephrased our query to `largest penguin species height` for better results. * Generated three documents from the search query. * Generated a list of citations * Generated a final response based on the above actions & content. * * * #### Help us out by providing feedback on this documentation page: [ Previous Cloudflare Workers AI ](/v0.1/docs/integrations/chat/cloudflare_workersai/)[ Next Fake LLM ](/v0.1/docs/integrations/chat/fake/) * [Setup](#setup) * [Usage](#usage) * [Streaming](#streaming) * [Stateful conversation API](#stateful-conversation-api) * [RAG](#rag) * [Connectors](#connectors) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.