LangChain Google Vertex AI Integration

Common errors

Warnings

Install compatibility verified last tested: 2026-05-12

Imports

• ChatVertexAI
  wrong

  from langchain.llms import VertexAI or from langchain.chat_models import ChatVertexAI (for older LangChain versions)

  correct

  from langchain_google_vertexai import ChatVertexAI

  The primary chat model class for Vertex AI. As of v3.2.0, this is deprecated in favor of `ChatGoogleGenerativeAI` from `langchain-google-genai` for unified Gemini API access, but remains for Vertex AI platform-specific features. [9, 22]

• VertexAI
  wrong

  from langchain.llms import VertexAI (for older LangChain versions)

  correct

  from langchain_google_vertexai import VertexAI

  The primary LLM class for Vertex AI completion models. As of v3.2.0, this is deprecated in favor of `GoogleGenerativeAI` from `langchain-google-genai` for unified Gemini API access, but remains for Vertex AI platform-specific features. [9, 22]

• VertexAIEmbeddings
  wrong

  from langchain.embeddings import VertexAIEmbeddings (for older LangChain versions)

  correct

  from langchain_google_vertexai import VertexAIEmbeddings

  The embeddings class for Vertex AI. As of v3.2.0, this is deprecated in favor of `GoogleGenerativeAIEmbeddings` from `langchain-google-genai` for unified Gemini API access, but remains for Vertex AI platform-specific features. [9, 20, 22]

• ChatAnthropicVertex

  from langchain_google_vertexai import ChatAnthropicVertex

  For integrating with Anthropic's Claude models hosted on Vertex AI. [9]

Quickstart last tested: 2026-04-24

This quickstart demonstrates how to instantiate and use the `ChatVertexAI` model for text generation. It also shows an example of multimodal input (commented out) using a vision-capable model. Ensure your Google Cloud project and location are configured, and that you are authenticated to Google Cloud, typically via Application Default Credentials (e.g., `gcloud auth application-default login`) or by setting `GOOGLE_APPLICATION_CREDENTIALS`.

import os
from langchain_core.messages import HumanMessage
from langchain_google_vertexai import ChatVertexAI

# Ensure your Google Cloud Project ID and location are set
# or use GOOGLE_APPLICATION_CREDENTIALS for authentication.
# For example, by running 'gcloud auth application-default login'
# os.environ["GOOGLE_CLOUD_PROJECT"] = os.environ.get("GOOGLE_CLOUD_PROJECT", "your-gcp-project-id")
# os.environ["GOOGLE_CLOUD_LOCATION"] = os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")

# Initialize the chat model
# Note: ChatVertexAI is deprecated in favor of ChatGoogleGenerativeAI from langchain_google_genai
# for most Gemini models, but can still be used for Vertex AI specific deployments.
llm = ChatVertexAI(model="gemini-pro") # or "gemini-2.5-flash", "chat-bison", etc.

# Invoke the model with a message
response = llm.invoke("What is the capital of France?")
print(response.content)

# Example with multimodal input (requires a vision model like "gemini-pro-vision")
# from langchain_core.messages import HumanMessage
# llm_vision = ChatVertexAI(model="gemini-pro-vision")
# message_with_image = HumanMessage(
#     content=[
#         {"type": "text", "text": "What's in this image?"},
#         {"type": "image_url", "image_url": {"url": "https://picsum.photos/seed/picsum/200/300"}},
#     ]
# )
# response_vision = llm_vision.invoke([message_with_image])
# print(response_vision.content)