> ## Documentation Index > Fetch the complete documentation index at: https://docs.langchain.com/llms.txt > Use this file to discover all available pages before exploring further. # ChatVertexAI integration > Integrate with the ChatVertexAI chat model using LangChain JavaScript. [Google Vertex](https://cloud.google.com/vertex-ai) is a service that exposes all foundation models available in Google Cloud, like `gemini-2.5-pro`, `gemini-2.5-flash`, etc. It also provides some non-Google models such as [Anthropic's Claude](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/use-claude). This will help you getting started with `ChatVertexAI` [chat models](/oss/javascript/langchain/models). For detailed documentation of all `ChatVertexAI` features and configurations head to the [API reference](https://reference.langchain.com/javascript/langchain-google-vertexai/index/ChatVertexAI). **This library will be deprecated** This library will be replaced by the [ChatGoogle](/oss/javascript/integrations/chat/google) library. New implementations should use the [ChatGoogle](/oss/javascript/integrations/chat/google) library instead and existing implementations should consider migrating. ## Overview ### Integration details | Class | Package | Serializable | PY support | Downloads | Version | | :-------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------- | :----------: | :--------: | :--------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------: | | [`ChatVertexAI`](https://reference.langchain.com/javascript/langchain-google-vertexai/index/ChatVertexAI) | [`@langchain/google-vertexai`](https://www.npmjs.com/package/@langchain/google-vertexai) | ✅ | ✅ | ![NPM - Downloads](https://img.shields.io/npm/dm/@langchain/google-vertexai?style=flat-square\&label=%20&) | ![NPM - Version](https://img.shields.io/npm/v/@langchain/google-vertexai?style=flat-square\&label=%20&) | ### Model features See the links in the table headers below for guides on how to use specific features. | [Tool calling](/oss/javascript/langchain/tools) | [Structured output](/oss/javascript/langchain/structured-output) | [Image input](/oss/javascript/langchain/messages#multimodal) | Audio input | Video input | [Token-level streaming](/oss/javascript/langchain/streaming/) | [Token usage](/oss/javascript/langchain/models#token-usage) | [Logprobs](/oss/javascript/langchain/models#log-probabilities) | | :---------------------------------------------: | :--------------------------------------------------------------: | :----------------------------------------------------------: | :---------: | :---------: | :-----------------------------------------------------------: | :---------------------------------------------------------: | :------------------------------------------------------------: | | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | Note that while logprobs are supported, Gemini has fairly restricted usage of them. ## Setup LangChain.js supports two different authentication methods based on whether you're running in a Node.js environment or a web environment. It also supports the authentication method used by Vertex AI Express Mode using either package. To access `ChatVertexAI` models you'll need to setup Google VertexAI in your Google Cloud Platform (GCP) account, save the credentials file, and install the `@langchain/google-vertexai` integration package. ### Credentials Head to your [GCP account](https://console.cloud.google.com/) and generate a credentials file. Once you've done this set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable: ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} export GOOGLE_APPLICATION_CREDENTIALS="path/to/your/credentials.json" ``` If running in a web environment, you should set the `GOOGLE_VERTEX_AI_WEB_CREDENTIALS` environment variable as a JSON stringified object, and install the `@langchain/google-vertexai-web` package: ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} GOOGLE_VERTEX_AI_WEB_CREDENTIALS={"type":"service_account","project_id":"YOUR_PROJECT-12345",...} ``` If you are using Vertex AI Express Mode, you can install either the `@langchain/google-vertexai` or `@langchain/google-vertexai-web` package. You can then go to the [Express Mode](https://console.cloud.google.com/vertex-ai/studio) API Key page and set your API Key in the `GOOGLE_API_KEY` environment variable: ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} export GOOGLE_API_KEY="api_key_value" ``` If you want to get automated tracing of your model calls you can also set your [LangSmith](/langsmith/home) API key by uncommenting below: ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # export LANGSMITH_TRACING="true" # export LANGSMITH_API_KEY="your-api-key" ``` ### Installation The LangChain `ChatVertexAI` integration lives in the `@langchain/google-vertexai` package: ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} npm install @langchain/google-vertexai @langchain/core ``` ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} yarn add @langchain/google-vertexai @langchain/core ``` ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pnpm add @langchain/google-vertexai @langchain/core ``` Or if using in a web environment like a [Vercel Edge function](https://vercel.com/blog/edge-functions-generally-available): ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} npm install @langchain/google-vertexai-web @langchain/core ``` ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} yarn add @langchain/google-vertexai-web @langchain/core ``` ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pnpm add @langchain/google-vertexai-web @langchain/core ``` ## Instantiation Now we can instantiate our model object and generate chat completions: ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { ChatVertexAI } from "@langchain/google-vertexai" // Uncomment the following line if you're running in a web environment: // import { ChatVertexAI } from "@langchain/google-vertexai-web" const llm = new ChatVertexAI({ model: "gemini-2.5-flash", temperature: 0, maxRetries: 2, // For web, authOptions.credentials // authOptions: { ... } // other params... }) ``` ## Invocation ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} const aiMsg = await llm.invoke([ [ "system", "You are a helpful assistant that translates English to French. Translate the user sentence.", ], ["human", "I love programming."], ]) aiMsg ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} AIMessageChunk { "content": "J'adore programmer. \n", "additional_kwargs": {}, "response_metadata": {}, "tool_calls": [], "tool_call_chunks": [], "invalid_tool_calls": [], "usage_metadata": { "input_tokens": 20, "output_tokens": 7, "total_tokens": 27 } } ``` ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} console.log(aiMsg.content) ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} J'adore programmer. ``` ## Tool calling with Google search retrieval It is possible to call the model with a Google search tool which you can use to [ground](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/grounding) content generation with real-world information and reduce hallucinations. Grounding is currently not supported by `gemini-2.0-flash-exp`. You can choose to either ground using Google Search or by using a custom data store. Here are examples of both: ### Google search retrieval Grounding example that uses Google Search: ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { ChatVertexAI } from "@langchain/google-vertexai" const searchRetrievalTool = { googleSearchRetrieval: { dynamicRetrievalConfig: { mode: "MODE_DYNAMIC", // Use Dynamic Retrieval dynamicThreshold: 0.7, // Default for Dynamic Retrieval threshold }, }, }; const searchRetrievalModel = new ChatVertexAI({ model: "gemini-2.5-pro", temperature: 0, maxRetries: 0, }).bindTools([searchRetrievalTool]); const searchRetrievalResult = await searchRetrievalModel.invoke("Who won the 2024 NBA Finals?"); console.log(searchRetrievalResult.content); ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} The Boston Celtics won the 2024 NBA Finals, defeating the Dallas Mavericks 4-1 in the series to claim their 18th NBA championship. This victory marked their first title since 2008 and established them as the team with the most NBA championships, surpassing the Los Angeles Lakers' 17 titles. ``` ### Google search retrieval with data store First, set up your data store (this is a schema of an example data store): | ID | Date | Team 1 | Score | Team 2 | | :--: | :--------: | :-------: | :---: | :------: | | 3001 | 2023-09-07 | Argentina | 1 - 0 | Ecuador | | 3002 | 2023-09-12 | Venezuela | 1 - 0 | Paraguay | | 3003 | 2023-09-12 | Chile | 0 - 0 | Colombia | | 3004 | 2023-09-12 | Peru | 0 - 1 | Brazil | | 3005 | 2024-10-15 | Argentina | 6 - 0 | Bolivia | Then, use this data store in the example provided below: (Note that you have to use your own variables for `projectId` and `datastoreId`) ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { ChatVertexAI } from "@langchain/google-vertexai"; const projectId = "YOUR_PROJECT_ID"; const datastoreId = "YOUR_DATASTORE_ID"; const searchRetrievalToolWithDataset = { retrieval: { vertexAiSearch: { datastore: `projects/${projectId}/locations/global/collections/default_collection/dataStores/${datastoreId}`, }, disableAttribution: false, }, }; const searchRetrievalModelWithDataset = new ChatVertexAI({ model: "gemini-2.5-pro", temperature: 0, maxRetries: 0, }).bindTools([searchRetrievalToolWithDataset]); const searchRetrievalModelResult = await searchRetrievalModelWithDataset.invoke( "What is the score of Argentina vs Bolivia football game?" ); console.log(searchRetrievalModelResult.content); ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} Argentina won against Bolivia with a score of 6-0 on October 15, 2024. ``` You should now get results that are grounded in the data from your provided data store. ## Context caching Vertex AI offers context caching functionality, which helps optimize costs by storing and reusing long blocks of message content across multiple API requests. This is particularly useful when you have lengthy conversation histories or message segments that appear frequently in your interactions. To use this feature, first create a context cache by following [this official guide](https://cloud.google.com/vertex-ai/generative-ai/docs/context-cache/context-cache-create). Once you've created a cache, you can pass its id in as a runtime param as follows: ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { ChatVertexAI } from "@langchain/google-vertexai"; const modelWithCachedContent = new ChatVertexAI({ model: "gemini-2.5-pro-002", location: "us-east5", }); await modelWithCachedContent.invoke("What is in the content?", { cachedContent: "projects/PROJECT_NUMBER/locations/LOCATION/cachedContents/CACHE_ID", }); ``` You can also bind this field directly onto the model instance: ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} const modelWithBoundCachedContent = new ChatVertexAI({ model: "gemini-2.5-pro-002", location: "us-east5", }).bind({ cachedContent: "projects/PROJECT_NUMBER/locations/LOCATION/cachedContents/CACHE_ID", }); ``` Note that not all models currently support context caching. *** ## API reference For detailed documentation of all `ChatVertexAI` features and configurations head to the [API reference](https://reference.langchain.com/javascript/langchain-google-vertexai/index/ChatVertexAI). ***
[Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers. [Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/javascript/integrations/chat/google_vertex_ai.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).