> ## Documentation Index > Fetch the complete documentation index at: https://langchain.idochub.dev/llms.txt > Use this file to discover all available pages before exploring further. # ChatVertexAI This page provides a quick overview for getting started with VertexAI [chat models](/oss/python/langchain/models). For detailed documentation of all ChatVertexAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html). ChatVertexAI exposes all foundational models available in Google Cloud, like `gemini-2.5-pro`, `gemini-2.5-flash`, etc. For a full and updated list of available models visit [VertexAI documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/models). **Google Cloud VertexAI vs Google PaLM** The Google Cloud VertexAI integration is separate from the [Google PaLM integration](/oss/python/integrations/chat/google_generative_ai/). Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there. ## Overview ### Integration details | Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/google_vertex_ai) | Downloads | Version | | :--------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------- | :---: | :----------: | :----------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------: | | [ChatVertexAI](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html) | [langchain-google-vertexai](https://python.langchain.com/api_reference/google_vertexai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-google-vertexai?style=flat-square\&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-google-vertexai?style=flat-square\&label=%20) | ### Model features | [Tool calling](/oss/python/langchain/tools) | [Structured output](/oss/python/langchain/structured-output) | JSON mode | [Image input](/oss/python/how-to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/oss/python/langchain/streaming/) | Native async | [Token usage](/oss/python/how-to/chat_token_usage_tracking/) | [Logprobs](/oss/python/how-to/logprobs/) | | :-----------------------------------------: | :----------------------------------------------------------: | :-------: | :--------------------------------------------------: | :---------: | :---------: | :-------------------------------------------------------: | :----------: | :----------------------------------------------------------: | :--------------------------------------: | | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ## Setup To access VertexAI models you'll need to create a Google Cloud Platform account, set up credentials, and install the `langchain-google-vertexai` integration package. ### Credentials To use the integration you must either: * Have credentials configured for your environment (gcloud, workload identity, etc...) * Store the path to a service account JSON file as the GOOGLE\_APPLICATION\_CREDENTIALS environment variable This codebase uses the `google.auth` library which first looks for the application credentials variable mentioned above, and then looks for system-level auth. For more information, see: * [cloud.google.com/docs/authentication/application-default-credentials#GAC](https://cloud.google.com/docs/authentication/application-default-credentials#GAC) * [googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth](https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth) To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key: ```python theme={null} # os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ") # os.environ["LANGSMITH_TRACING"] = "true" ``` ### Installation The LangChain VertexAI integration lives in the `langchain-google-vertexai` package: ```python theme={null} %pip install -qU langchain-google-vertexai ``` ```output theme={null} Note: you may need to restart the kernel to use updated packages. ``` ## Instantiation Now we can instantiate our model object and generate chat completions: ```python theme={null} from langchain_google_vertexai import ChatVertexAI llm = ChatVertexAI( model="gemini-2.5-flash", temperature=0, max_tokens=None, max_retries=6, stop=None, # other params... ) ``` ## Invocation ```python theme={null} messages = [ ( "system", "You are a helpful assistant that translates English to French. Translate the user sentence.", ), ("human", "I love programming."), ] ai_msg = llm.invoke(messages) ai_msg ``` ```output theme={null} AIMessage(content="J'adore programmer. \n", response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}], 'usage_metadata': {'prompt_token_count': 20, 'candidates_token_count': 7, 'total_token_count': 27}}, id='run-7032733c-d05c-4f0c-a17a-6c575fdd1ae0-0', usage_metadata={'input_tokens': 20, 'output_tokens': 7, 'total_tokens': 27}) ``` ```python theme={null} print(ai_msg.content) ``` ```output theme={null} J'adore programmer. ``` ## Built-in tools Gemini supports a range of tools that are executed server-side. ### Google search **Requires `langchain-google-vertexai>=2.0.11`** Gemini can execute a Google search and use the results to [ground its responses](https://ai.google.dev/gemini-api/docs/grounding): ```python theme={null} from langchain_google_vertexai import ChatVertexAI llm = ChatVertexAI(model="gemini-2.5-flash").bind_tools([{"google_search": {}}]) response = llm.invoke("What is today's news?") ``` ### Code execution **Requires `langchain-google-vertexai>=2.0.25`** Gemini can [generate and execute Python code](https://ai.google.dev/gemini-api/docs/code-execution): ```python theme={null} from langchain_google_vertexai import ChatVertexAI llm = ChatVertexAI(model="gemini-2.5-flash").bind_tools([{"code_execution": {}}]) response = llm.invoke("What is 3^3?") ``` ## Chaining We can [chain](/oss/python/how-to/sequence/) our model with a prompt template like so: ```python theme={null} from langchain_core.prompts import ChatPromptTemplate prompt = ChatPromptTemplate.from_messages( [ ( "system", "You are a helpful assistant that translates {input_language} to {output_language}.", ), ("human", "{input}"), ] ) chain = prompt | llm chain.invoke( { "input_language": "English", "output_language": "German", "input": "I love programming.", } ) ``` ```output theme={null} AIMessage(content='Ich liebe Programmieren. \n', response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}], 'usage_metadata': {'prompt_token_count': 15, 'candidates_token_count': 8, 'total_token_count': 23}}, id='run-c71955fd-8dc1-422b-88a7-853accf4811b-0', usage_metadata={'input_tokens': 15, 'output_tokens': 8, 'total_tokens': 23}) ``` ## API reference For detailed documentation of all ChatVertexAI features and configurations, like how to send multimodal inputs and configure safety settings, head to the API reference: [python.langchain.com/api\_reference/google\_vertexai/chat\_models/langchain\_google\_vertexai.chat\_models.ChatVertexAI.html](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html)