langchain-google-vertexai

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)