ChatGoogleGenerativeAI

%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: ")

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

from langchain_google_genai import ChatGoogleGenerativeAI

llm = ChatGoogleGenerativeAI(
    model="gemini-2.5-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

AIMessage(content="J'adore la programmation.", additional_kwargs={}, response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'model_name': 'gemini-2.0-flash', 'safety_ratings': []}, id='run-3b28d4b8-8a62-4e6c-ad4e-b53e6e825749-0', usage_metadata={'input_tokens': 20, 'output_tokens': 7, 'total_tokens': 27, 'input_token_details': {'cache_read': 0}})

print(ai_msg.content)

J'adore la programmation.

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.",
    }
)

AIMessage(content='Ich liebe Programmieren.', additional_kwargs={}, response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'model_name': 'gemini-2.0-flash', 'safety_ratings': []}, id='run-e5561c6b-2beb-4411-9210-4796b576a7cd-0', usage_metadata={'input_tokens': 15, 'output_tokens': 7, 'total_tokens': 22, 'input_token_details': {'cache_read': 0}})

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}")

import base64

from langchain_core.messages import HumanMessage

# Ensure you have an audio file named 'example_audio.mp3' or provide the correct path.
audio_file_path = "example_audio.mp3"
audio_mime_type = "audio/mpeg"


with open(audio_file_path, "rb") as audio_file:
    encoded_audio = base64.b64encode(audio_file.read()).decode("utf-8")

message = HumanMessage(
    content=[
        {"type": "text", "text": "Transcribe the audio."},
        {
            "type": "media",
            "data": encoded_audio,  # Use base64 string directly
            "mime_type": audio_mime_type,
        },
    ]
)
response = llm.invoke([message])  # Uncomment to run
print(f"Response for audio: {response.content}")

import base64

from langchain_core.messages import HumanMessage
from langchain_google_genai import ChatGoogleGenerativeAI

# Ensure you have a video file named 'example_video.mp4' or provide the correct path.
video_file_path = "example_video.mp4"
video_mime_type = "video/mp4"


with open(video_file_path, "rb") as video_file:
    encoded_video = base64.b64encode(video_file.read()).decode("utf-8")

message = HumanMessage(
    content=[
        {"type": "text", "text": "Describe the first few frames of the video."},
        {
            "type": "media",
            "data": encoded_video,  # Use base64 string directly
            "mime_type": video_mime_type,
        },
    ]
)
response = llm.invoke([message])  # Uncomment to run
print(f"Response for video: {response.content}")

import base64

from IPython.display import Image, display
from langchain_core.messages import AIMessage
from langchain_google_genai import ChatGoogleGenerativeAI

llm = ChatGoogleGenerativeAI(model="models/gemini-2.0-flash-preview-image-generation")

message = {
    "role": "user",
    "content": "Generate a photorealistic image of a cuddly cat wearing a hat.",
}

response = llm.invoke(
    [message],
    generation_config=dict(response_modalities=["TEXT", "IMAGE"]),
)


def _get_image_base64(response: AIMessage) -> None:
    image_block = next(
        block
        for block in response.content
        if isinstance(block, dict) and block.get("image_url")
    )
    return image_block["image_url"].get("url").split(",")[-1]


image_base64 = _get_image_base64(response)
display(Image(data=base64.b64decode(image_base64), width=300))

from langchain_core.tools import tool
from langchain_google_genai import ChatGoogleGenerativeAI


# Define the tool
@tool(description="Get the current weather in a given location")
def get_weather(location: str) -> str:
    return "It's sunny."


# Initialize the model and bind the tool
llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash")
llm_with_tools = llm.bind_tools([get_weather])

# Invoke the model with a query that should trigger the tool
query = "What's the weather in San Francisco?"
ai_msg = llm_with_tools.invoke(query)

# Check the tool calls in the response
print(ai_msg.tool_calls)

# Example tool call message would be needed here if you were actually running the tool
from langchain_core.messages import ToolMessage

tool_message = ToolMessage(
    content=get_weather(*ai_msg.tool_calls[0]["args"]),
    tool_call_id=ai_msg.tool_calls[0]["id"],
)
llm_with_tools.invoke([ai_msg, tool_message])  # Example of passing tool result back

[{'name': 'get_weather', 'args': {'location': 'San Francisco'}, 'id': 'a6248087-74c5-4b7c-9250-f335e642927c', 'type': 'tool_call'}]

AIMessage(content="OK. It's sunny in San Francisco.", additional_kwargs={}, response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'model_name': 'gemini-2.0-flash', 'safety_ratings': []}, id='run-ac5bb52c-e244-4c72-9fbc-fb2a9cd7a72e-0', usage_metadata={'input_tokens': 29, 'output_tokens': 11, 'total_tokens': 40, 'input_token_details': {'cache_read': 0}})

from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_google_genai import ChatGoogleGenerativeAI


# Define the desired structure
class Person(BaseModel):
    """Information about a person."""

    name: str = Field(..., description="The person's name")
    height_m: float = Field(..., description="The person's height in meters")


# Initialize the model
llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)

# Method 1: Default function calling approach
structured_llm_default = llm.with_structured_output(Person)

# Method 2: Native JSON schema for better reliability (recommended)
structured_llm_json = llm.with_structured_output(Person, method="json_schema")

# Invoke the model with a query asking for structured information
result = structured_llm_json.invoke(
    "Who was the 16th president of the USA, and how tall was he in meters?"
)
print(result)

name='Abraham Lincoln' height_m=1.93

from langchain_google_genai import ChatGoogleGenerativeAI

llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash")

result = llm.invoke("Explain the concept of prompt engineering in one sentence.")

print(result.content)
print("\nUsage Metadata:")
print(result.usage_metadata)

Prompt engineering is the art and science of crafting effective text prompts to elicit desired and accurate responses from large language models.

Usage Metadata:
{'input_tokens': 10, 'output_tokens': 24, 'total_tokens': 34, 'input_token_details': {'cache_read': 0}}

from google.ai.generativelanguage_v1beta.types import Tool as GenAITool

resp = llm.invoke(
    "When is the next total solar eclipse in US?",
    tools=[GenAITool(google_search={})],
)

print(resp.content)

The next total solar eclipse visible in the United States will occur on August 23, 2044. However, the path of totality will only pass through Montana, North Dakota, and South Dakota.

For a total solar eclipse that crosses a significant portion of the continental U.S., you'll have to wait until August 12, 2045. This eclipse will start in California and end in Florida.

from google.ai.generativelanguage_v1beta.types import Tool as GenAITool

resp = llm.invoke(
    "What is 2*2, use python",
    tools=[GenAITool(code_execution={})],
)

for c in resp.content:
    if isinstance(c, dict):
        if c["type"] == "code_execution_result":
            print(f"Code execution result: {c['code_execution_result']}")
        elif c["type"] == "executable_code":
            print(f"Executable code: {c['executable_code']}")
    else:
        print(c)

Executable code: print(2*2)

Code execution result: 4

2*2 is 4.

/Users/philschmid/projects/google-gemini/langchain/.venv/lib/python3.9/site-packages/langchain_google_genai/chat_models.py:580: UserWarning:
        ⚠️ Warning: Output may vary each run.
        - 'executable_code': Always present.
        - 'execution_result' & 'image_url': May be absent for some queries.

        Validate before using in production.

  warnings.warn(

from langchain_google_genai import ChatGoogleGenerativeAI

llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash")


async def run_async_calls():
    # Async invoke
        result_ainvoke = await llm.ainvoke("Why is the sky blue?")
    print("Async Invoke Result:", result_ainvoke.content[:50] + "...")

    # Async stream
    print("\nAsync Stream Result:")
    async for chunk in llm.astream(
        "Write a short poem about asynchronous programming."
    ):
                print(chunk.content, end="", flush=True)
    print("\n")

    # Async batch
        results_abatch = await llm.abatch(["What is 1+1?", "What is 2+2?"])
    print("Async Batch Results:", [res.content for res in results_abatch])


await run_async_calls()

Async Invoke Result: The sky is blue due to a phenomenon called **Rayle...

Async Stream Result:
The thread is free, it does not wait,
For answers slow, or tasks of fate.
A promise made, a future bright,
It moves ahead, with all its might.

A callback waits, a signal sent,
When data's read, or job is spent.
Non-blocking code, a graceful dance,
Responsive apps, a fleeting glance.

Async Batch Results: ['1 + 1 = 2', '2 + 2 = 4']

from langchain_google_genai import (
    ChatGoogleGenerativeAI,
    HarmBlockThreshold,
    HarmCategory,
)

llm = ChatGoogleGenerativeAI(
        model="gemini-1.5-pro",
        safety_settings={
        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
    },
)