OpenAI

pip install -qU "langchain[openai]"

import getpassimport osif not os.environ.get("OPENAI_API_KEY"):  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")from langchain.chat_models import init_chat_modelmodel = init_chat_model("gpt-4o-mini", model_provider="openai")

model.invoke("Hello, world!")

Instantiation​

Now we can instantiate our model object and generate chat completions:

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-4o",
    temperature=0,
    max_tokens=None,
    timeout=None,
    max_retries=2,
    # api_key="...",  # if you prefer to pass api key in directly instaed of using env vars
    # base_url="...",
    # organization="...",
    # other params...
)

Invocation

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

Chaining

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.",
    }
)

Google

pip install -U langchain-google-genai

import getpass  
import os  
  
if "GOOGLE_API_KEY" not in os.environ:  
os.environ["GOOGLE_API_KEY"] = getpass.getpass("Enter your Google AI API key: ")

from langchain_google_genai import ChatGoogleGenerativeAI  
  
llm = ChatGoogleGenerativeAI(  
model="gemini-2.0-flash",  
temperature=0,  
max_tokens=None,  
timeout=None,  
max_retries=2,  
# other params...  
)

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

We can chain our model with a prompt template like so:

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.",  
}  
)

Image Input

import base64

from langchain_core.messages import HumanMessage
from langchain_google_genai import ChatGoogleGenerativeAI

# Example using a public URL (remains the same)
message_url = HumanMessage(
    content=[
        {
            "type": "text",
            "text": "Describe the image at the URL.",
        },
        {"type": "image_url", "image_url": "https://picsum.photos/seed/picsum/200/300"},
    ]
)
result_url = llm.invoke([message_url])
print(f"Response for URL image: {result_url.content}")

# Example using a local image file encoded in base64
image_file_path = "/Users/philschmid/projects/google-gemini/langchain/docs/static/img/agents_vs_chains.png"

with open(image_file_path, "rb") as image_file:
    encoded_image = base64.b64encode(image_file.read()).decode("utf-8")

message_local = HumanMessage(
    content=[
        {"type": "text", "text": "Describe the local image."},
        {"type": "image_url", "image_url": f"data:image/png;base64,{encoded_image}"},
    ]
)
result_local = llm.invoke([message_local])
print(f"Response for local image: {result_local.content}")