Google Cloud Vertex AI

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You are currently on a page documenting the use of Google Vertex text completion models. Many Google models are chat completion models.

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Note: This is separate from the Google Generative AI integration, it exposes Vertex AI Generative API on Google Cloud.

VertexAI exposes all foundational models available in google cloud:

• Gemini (gemini-pro and gemini-pro-vision)
• Palm 2 for Text (text-bison)
• Codey for Code Generation (code-bison)

For a full and updated list of available models visit VertexAI documentation

Setup

By default, Google Cloud does not use customer data to train its foundation models as part of Google Cloud's AI/ML Privacy Commitment. More details about how Google processes data can also be found in Google's Customer Data Processing Addendum (CDPA).

To use Vertex AI Generative AI you must have the langchain-google-vertexai Python package installed and 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:

• https://cloud.google.com/docs/authentication/application-default-credentials#GAC
• https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth

%pip install --upgrade --quiet  langchain-core langchain-google-vertexai

Note: you may need to restart the kernel to use updated packages.

Usage

VertexAI supports all LLM functionality.

from langchain_google_vertexai import VertexAI

# To use model
model = VertexAI(model_name="gemini-pro")

NOTE : You can also specify a Gemini Version

# To specify a particular model version
model = VertexAI(model_name="gemini-1.0-pro-002")

message = "What are some of the pros and cons of Python as a programming language?"
model.invoke(message)

"## Pros of Python:\n\n* **Easy to learn and use:** Python's syntax is simple and straightforward, making it a great choice for beginners. \n* **Extensive library support:** Python has a massive collection of libraries and frameworks for a variety of tasks, from web development to data science. \n* **Open source and free:** Anyone can use and contribute to Python without paying licensing fees.\n* **Large and active community:** There's a vast community of Python users offering help and support.\n* **Versatility:** Python is a general-purpose language, meaning it can be used for a wide variety of tasks.\n* **Portable and cross-platform:** Python code works seamlessly across various operating systems.\n* **High-level language:** Python hides many of the complexities of lower-level languages, allowing developers to focus on problem solving.\n* **Readability:** The clear syntax makes Python programs easier to understand and maintain, especially for collaborative projects.\n\n## Cons of Python:\n\n* **Slower execution:** Compared to compiled languages like C++, Python is generally slower due to its interpreted nature.\n* **Dynamically typed:** Python doesn’t enforce strict data types, which can sometimes lead to errors.\n* **Global Interpreter Lock (GIL):** The GIL limits Python to using a single CPU core at a time, impacting its performance in multi-core environments.\n* **Large memory footprint**: Python programs require more memory than some other languages.\n* **Not ideal for low-level programming:** Python is not suitable for tasks requiring direct hardware interaction.\n\n\n\n## Conclusion:\n\nWhile it has some drawbacks, Python's strengths outweigh them, making it a very versatile and approachable programming language for beginners. Its extensive libraries, large community, ease of use and versatility make it an excellent choice for various projects and applications. However, for tasks requiring extreme performance or low-level access, other languages might offer better solutions.\n"

await model.ainvoke(message)

"## Pros of Python:\n\n* **Easy to learn and read:** Python's syntax is known for its simplicity and readability. Its English-like structure makes it accessible to both beginners and experienced programmers.\n* **Versatile:** Python can be used for a wide range of applications, from web development and data science to machine learning and automation. This versatility makes it a valuable tool for programmers in diverse fields.\n* **Large and active community:** Python has a massive and passionate community of users, developers, and contributors. This translates to extensive resources, libraries, frameworks, and support, making it easier for users to find solutions and collaborate.\n* **Rich libraries and frameworks:** Python boasts an extensive ecosystem of open-source libraries and frameworks for various tasks, including data analysis, web development, machine learning, and scientific computing. This vast choice empowers developers to build powerful and efficient applications.\n* **Cross-platform compatibility:** Python runs on various operating systems like Windows, macOS, Linux, and Unix, making it a portable and adaptable language for development. This allows developers to create applications that can be easily deployed on different platforms.\n* **High-level abstraction:** Python's high-level nature allows developers to focus on the logic of their programs rather than low-level details like memory management. This abstraction contributes to faster development and cleaner code.\n\n## Cons of Python:\n\n* **Slow execution speed:** Compared to languages like C or C++, Python is generally slower due to its interpreted nature. This can be a drawback for computationally intensive tasks or real-time applications.\n* **Dynamic typing:** While dynamic typing offers flexibility, it can lead to runtime errors that might go unnoticed during development. This can be particularly challenging for large and complex projects.\n* **Global interpreter lock (GIL):** Python's GIL limits the performance of multi-threaded applications. It only allows one thread to execute Python bytecode at a time, which can hamper parallel processing capabilities.\n* **Memory management:** Python handles memory management automatically, which can lead to memory leaks in certain cases. Developers need to be aware of memory management practices to avoid potential issues.\n* **Limited hardware control:** Python's design prioritizes ease of use and portability over low-level hardware control. This can be a limitation for applications that require direct hardware interaction.\n\nOverall, Python offers a strong balance between ease of use, versatility, and a rich ecosystem. However, its dynamic typing, execution speed, and GIL limitations are factors to consider when choosing the right language for your project."

for chunk in model.stream(message):
    print(chunk, end="", flush=True)

## Pros and Cons of Python

### Pros:

* **Easy to learn and read**: Python's syntax is clear and concise, making it easier to pick up than many other languages. This is especially helpful for beginners.
* **Versatile**: Python can be used for a wide range of applications, from web development and data science to machine learning and scripting.
* **Large and active community**: There's a huge and active community of Python developers, which means there's a wealth of resources and support available online and offline.
* **Open-source and free**: Python is open-source, meaning it's freely available to use and distribute. 
* **Large standard library**: Python comes with a vast standard library that includes modules for many common tasks, reducing the need to write code from scratch.
* **Cross-platform**: Python runs on all major operating systems, including Windows, macOS, and Linux. 
* **Focus on readability**: Python emphasizes code readability with its use of indentation and simple syntax, making it easier to maintain and debug code.

### Cons:

* **Slower execution**: Python is often slower than compiled languages like C++ and Java, especially when working with computationally intensive tasks.
* **Dynamically typed**: Python is a dynamically typed language, which means variables don't have a fixed type. This can lead to runtime errors and can be less efficient for large projects. 
* **Global Interpreter Lock (GIL)**: The GIL restricts Python to using one CPU core at a time, which can limit performance for CPU-bound tasks.
* **Immature frameworks**: While Python has a vast array of libraries and frameworks, some are less mature and stable compared to those in well-established languages.


## Conclusion:

Overall, Python is a great choice for beginners and experienced developers alike. Its versatility, ease of use, and large community make it a popular language for various applications. However, it's important to consider its limitations, like execution speed, when choosing a language for your project.

model.batch([message])

['**Pros:**\n\n* **Easy to learn and use:** Python is known for its simple syntax and readability, making it a great choice for beginners and experienced programmers alike.\n* **Versatile:** Python can be used for a wide variety of tasks, including web development, data science, machine learning, and scripting.\n* **Large community:** Python has a large and active community of developers, which means there is a wealth of resources and support available.\n* **Extensive library support:** Python has a vast collection of libraries and frameworks that can be used to extend its functionality.\n* **Cross-platform:** Python is available for a']

We can use the generate method to get back extra metadata like safety attributes and not just text completions.

result = model.generate([message])
result.generations

[[GenerationChunk(text='## Python: Pros and Cons\n\n### Pros:\n\n* **Easy to learn:** Python is often cited as one of the easiest programming languages to learn, making it a popular choice for beginners. Its syntax is simple and straightforward, resembling natural language in many ways. This ease of learning makes it a great option for those new to programming or looking to pick up a new language quickly.\n* **Versatile:**  Python is a versatile language, suitable for a wide range of applications. From web development and data science to scripting and machine learning, Python offers a diverse set of libraries and frameworks, making it adaptable to various needs. This versatility makes it a valuable tool for developers with varied interests and projects.\n* **Cross-platform:** Python can be used on various operating systems, including Windows, macOS, Linux, and Unix. This cross-platform capability allows developers to work on their projects regardless of their preferred platform, ensuring better portability and collaboration.\n* **Large community:** Python boasts a vast and active community, providing ample resources for support, learning, and collaboration. This large community offers numerous tutorials, libraries, frameworks, and forums, creating a valuable ecosystem for Python developers.\n* **Open-source:** Python is an open-source language, meaning its source code is freely available for anyone to use, modify, and distribute. This openness fosters collaboration and innovation, leading to a continuously evolving and improving language. \n* **Extensive libraries:** Python offers a vast collection of libraries and frameworks, covering diverse areas like data science (NumPy, Pandas, Scikit-learn), web development (Django, Flask), machine learning (TensorFlow, PyTorch), and more. This extensive ecosystem enhances Python\'s capabilities and makes it adaptable to various tasks.\n\n### Cons:\n\n* **Dynamically typed:** Python uses dynamic typing, where variable types are determined at runtime. While this can be convenient for beginners, it can also lead to runtime errors and inconsistencies, especially in larger projects. Static typing languages offer more rigorous type checking, which can help prevent these issues.\n* **Slow execution speed:** Compared to compiled languages like C++ or Java, Python is generally slower due to its interpreted nature. This difference in execution speed may be significant when dealing with performance-critical tasks or large datasets.\n* **"Not invented here" syndrome:** Python\'s popularity has sometimes led to the "not invented here" syndrome, where developers might reject external libraries or frameworks in favor of creating their own solutions. This can lead to redundant efforts and reinventing the wheel, potentially hindering progress.\n* **Global Interpreter Lock (GIL):** Python\'s GIL limits the use of multiple CPU cores effectively, as only one thread can execute Python bytecode at a time. This can be a bottleneck for CPU-bound tasks, although alternative implementations like Jython and IronPython offer workarounds.\n\nOverall, Python\'s strengths lie in its ease of learning, versatility, and large community, making it a popular choice for various applications. However, it\'s essential to be aware of its limitations, such as slower execution speed and the GIL, when deciding if it\'s the right tool for your specific needs.', generation_info={'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}], 'citation_metadata': None, 'usage_metadata': {'prompt_token_count': 15, 'candidates_token_count': 647, 'total_token_count': 662}})]]

OPTIONAL : Managing Safety Attributes

• If your use case requires your to manage thresholds for saftey attributes, you can do so using below snippets

  NOTE : We recommend exercising extreme caution when adjusting Safety Attributes thresholds

from langchain_google_vertexai import HarmBlockThreshold, HarmCategory

safety_settings = {
    HarmCategory.HARM_CATEGORY_UNSPECIFIED: HarmBlockThreshold.BLOCK_NONE,
    HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
    HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
    HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
    HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
}

llm = VertexAI(model_name="gemini-1.0-pro-001", safety_settings=safety_settings)

# invoke a model response
output = llm.invoke(["How to make a molotov cocktail?"])
output

"I'm so sorry, but I can't answer that question. Molotov cocktails are illegal and dangerous, and I would never do anything that could put someone at risk. If you are interested in learning more about the dangers of molotov cocktails, I can provide you with some resources."

# You may also pass safety_settings to generate method
llm = VertexAI(model_name="gemini-1.0-pro-001")

# invoke a model response
output = llm.invoke(
    ["How to make a molotov cocktail?"], safety_settings=safety_settings
)
output

"I'm sorry, I can't answer that question. Molotov cocktails are illegal and dangerous."

result = await model.ainvoke([message])
result

"## Pros of Python\n\n* **Easy to learn:** Python's clear syntax and simple structure make it easy for beginners to pick up, even if they have no prior programming experience.\n* **Versatile:** Python is a general-purpose language, meaning it can be used for a wide range of tasks, including web development, data analysis, machine learning, and scripting.\n* **Large community:** Python has a large and active community of developers, which means there are plenty of resources available to help you learn and use the language.\n* **Libraries and frameworks:** Python has a vast ecosystem of libraries and frameworks that can be used for various tasks, making it easy to \nbuild complex applications.\n* **Open-source:** Python is an open-source language, which means it is free to use and distribute. This also means that the code is constantly being improved and updated by the community.\n\n## Cons of Python\n\n* **Slow execution:** Python is an interpreted language, which means that the code is executed line by line. This can make Python slower than compiled languages like C++ or Java.\n* **Dynamic typing:** Python's dynamic typing can be a disadvantage for large projects, as it can lead to errors that are not caught until runtime.\n* **Global interpreter lock (GIL):** The GIL can limit the performance of Python code on multi-core processors, as only one thread can execute Python code at a time.\n* **Large memory footprint:** Python programs tend to use more memory than programs written in other languages.\n\n\nOverall, Python is a great choice for beginners and experienced programmers alike. Its ease of use, versatility, and large community make it a popular choice for many different types of projects. However, it is important to be aware of its limitations, such as its slow execution speed and dynamic typing."

You can also easily combine with a prompt template for easy structuring of user input. We can do this using LCEL

from langchain_core.prompts import PromptTemplate

template = """Question: {question}

Answer: Let's think step by step."""
prompt = PromptTemplate.from_template(template)

chain = prompt | model

question = """
I have five apples. I throw two away. I eat one. How many apples do I have left?
"""
print(chain.invoke({"question": question}))

API Reference:PromptTemplate

1. You start with 5 apples.
2. You throw away 2 apples, so you have 5 - 2 = 3 apples left.
3. You eat 1 apple, so you have 3 - 1 = 2 apples left.

Therefore, you have 2 apples left.

You can use different foundational models for specialized in different tasks. For an updated list of available models visit VertexAI documentation

llm = VertexAI(model_name="code-bison", max_tokens=1000, temperature=0.3)
question = "Write a python function that checks if a string is a valid email address"

# invoke a model response
print(model.invoke(question))

```python
import re

def is_valid_email(email):
  """
  Checks if a string is a valid email address.

  Args:
    email: The string to check.

  Returns:
    True if the string is a valid email address, False otherwise.
  """

  # Compile the regular expression for an email address.
  regex = re.compile(r"[^@]+@[^@]+\.[^@]+")

  # Check if the string matches the regular expression.
  return regex.match(email) is not None

## Multimodality

With Gemini, you can use LLM in a multimodal mode:


```python
from langchain_core.messages import HumanMessage
from langchain_google_vertexai import ChatVertexAI

llm = ChatVertexAI(model="gemini-pro-vision")

image_message = {
    "type": "image_url",
    "image_url": {"url": "image_example.jpg"},
}
text_message = {
    "type": "text",
    "text": "What is shown in this image?",
}

# Prepare input for model consumption
message = HumanMessage(content=[text_message, image_message])

# invoke a model response
output = llm.invoke([message])
print(output.content)

API Reference:HumanMessage

 The image shows a dog with a long coat. The dog is sitting on a wooden floor and looking at the camera.

Let's double-check it's a cat :)

from vertexai.preview.generative_models import Image

i = Image.load_from_file("image_example.jpg")
i

You can also pass images as bytes:

import base64

with open("image_example.jpg", "rb") as image_file:
    image_bytes = image_file.read()

image_message = {
    "type": "image_url",
    "image_url": {
        "url": f"data:image/jpeg;base64,{base64.b64encode(image_bytes).decode('utf-8')}"
    },
}
text_message = {
    "type": "text",
    "text": "What is shown in this image?",
}

# Prepare input for model consumption
message = HumanMessage(content=[text_message, image_message])

# invoke a model response
output = llm.invoke([message])
print(output.content)

 The image shows a dog sitting on a wooden floor. The dog is a small breed, with a long, shaggy coat that is brown and gray in color. The dog has a white patch of fur on its chest and white paws. The dog is looking at the camera with a curious expression.

Please, note that you can also use the image stored in GCS (just point the url to the full GCS path, starting with gs:// instead of a local one).

And you can also pass a history of a previous chat to the LLM:

# Prepare input for model consumption
message2 = HumanMessage(content="And where the image is taken?")

# invoke a model response
output2 = llm.invoke([message, output, message2])
print(output2.content)

You can also use the public image URL:

image_message = {
    "type": "image_url",
    "image_url": {
        "url": "gs://github-repo/img/vision/google-cloud-next.jpeg",
    },
}
text_message = {
    "type": "text",
    "text": "What is shown in this image?",
}

# Prepare input for model consumption
message = HumanMessage(content=[text_message, image_message])

# invoke a model response
output = llm.invoke([message])
print(output.content)

 This image shows a Google Cloud Next event. Google Cloud Next is an annual conference held by Google Cloud, a division of Google that offers cloud computing services. The conference brings together customers, partners, and industry experts to learn about the latest cloud technologies and trends.

ADVANCED : You can use Pdfs with Gemini Models

from langchain_core.messages import HumanMessage
from langchain_google_vertexai import ChatVertexAI

# Use Gemini 1.5 Pro
llm = ChatVertexAI(model="gemini-1.5-pro-preview-0514")

API Reference:HumanMessage

# Prepare input for model consumption
pdf_message = {
    "type": "image_url",
    "image_url": {"url": "gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf"},
}

text_message = {
    "type": "text",
    "text": "Summarize the provided document.",
}

# Prepare input for model consumption
message = HumanMessage(content=[text_message, pdf_message])

# invoke a model response
llm.invoke([message])

AIMessage(content='The document introduces Gemini 1.5 Pro, a multimodal AI model developed by Google. It\'s a "mixture-of-experts" model capable of understanding and reasoning over very long contexts, up to millions of tokens, across text, audio, and video data. \n\n**Key Features:**\n\n* **Unprecedented Long Context:** Handles context lengths of up to 10 million tokens, enabling it to process entire books, hours of video, and days of audio.\n* **Multimodal Understanding:** Seamlessly integrates text, audio, and video data for comprehensive understanding.\n* **Enhanced Performance:** Achieves near-perfect recall in retrieval tasks and surpasses previous models in various benchmarks.\n* **Novel Capabilities:** Demonstrates surprising abilities like learning to translate a new language from a single grammar book in context.\n\n**Evaluations:**\n\nThe document presents extensive evaluations highlighting Gemini 1.5 Pro\'s capabilities. It excels in both diagnostic tests (perplexity, needle-in-a-haystack) and realistic tasks (long-document QA, language translation, video understanding). It also outperforms its predecessors and state-of-the-art models like GPT-4 Turbo and Claude 2.1 in various core benchmarks (coding, multilingual tasks, math and science reasoning).\n\n**Responsible Deployment:**\n\nGoogle emphasizes a structured approach to responsible deployment, outlining their model mitigation efforts, impact assessments, and ongoing safety evaluations to address potential risks associated with long-context understanding and multimodal capabilities.\n\n**Call-to-action:**\n\nThe document highlights the need for innovative evaluation methodologies to effectively assess long-context models. They encourage researchers to develop challenging benchmarks that go beyond simple retrieval and require complex reasoning over extended inputs.\n\n**Overall:**\n\nGemini 1.5 Pro represents a significant advancement in AI, pushing the boundaries of multimodal long-context understanding. Its impressive performance and unique capabilities open new possibilities for research and application, while Google\'s commitment to responsible deployment ensures the safe and ethical use of this powerful technology. \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': 19872, 'candidates_token_count': 415, 'total_token_count': 20287}}, id='run-99072700-55be-49d4-acca-205a52256bcd-0')

Vertex Model Garden

Vertex Model Garden exposes open-sourced models that can be deployed and served on Vertex AI.

Hundreds popular open-sourced models like Llama, Falcon and are available for One Click Deployment

If you have successfully deployed a model from Vertex Model Garden, you can find a corresponding Vertex AI endpoint in the console or via API.

from langchain_google_vertexai import VertexAIModelGarden

llm = VertexAIModelGarden(project="YOUR PROJECT", endpoint_id="YOUR ENDPOINT_ID")

# invoke a model response
llm.invoke("What is the meaning of life?")

Like all LLMs, we can then compose it with other components:

prompt = PromptTemplate.from_template("What is the meaning of {thing}?")

chain = prompt | llm
print(chain.invoke({"thing": "life"}))

Llama on Vertex Model Garden

Llama is a family of open weight models developed by Meta that you can fine-tune and deploy on Vertex AI. Llama models are pre-trained and fine-tuned generative text models. You can deploy Llama 2 and Llama 3 models on Vertex AI. Official documentation for more information about Llama on Vertex Model Garden

To use Llama on Vertex Model Garden you must first deploy it to Vertex AI Endpoint

from langchain_google_vertexai import VertexAIModelGarden

# TODO : Add "YOUR PROJECT" and "YOUR ENDPOINT_ID"
llm = VertexAIModelGarden(project="YOUR PROJECT", endpoint_id="YOUR ENDPOINT_ID")

# invoke a model response
llm.invoke("What is the meaning of life?")

'Prompt:\nWhat is the meaning of life?\nOutput:\n is a classic problem for Humanity. There is one vital characteristic of Life in'

Like all LLMs, we can then compose it with other components:

from langchain_core.prompts import PromptTemplate

prompt = PromptTemplate.from_template("What is the meaning of {thing}?")

API Reference:PromptTemplate

# invoke a model response using chain
chain = prompt | llm
print(chain.invoke({"thing": "life"}))

Prompt:
What is the meaning of life?
Output:
 The question is so perplexing that there have been dozens of care

Falcon on Vertex Model Garden

Falcon is a family of open weight models developed by Falcon that you can fine-tune and deploy on Vertex AI. Falcon models are pre-trained and fine-tuned generative text models.

To use Falcon on Vertex Model Garden you must first deploy it to Vertex AI Endpoint

from langchain_google_vertexai import VertexAIModelGarden

# TODO : Add "YOUR PROJECT" and "YOUR ENDPOINT_ID"
llm = VertexAIModelGarden(project="YOUR PROJECT", endpoint_id="YOUR ENDPOINT_ID")

# invoke a model response
llm.invoke("What is the meaning of life?")

'Prompt:\nWhat is the meaning of life?\nOutput:\nWhat is the meaning of life?\nThe meaning of life is a philosophical question that does not have a clear answer. The search for the meaning of life is a lifelong journey, and there is no definitive answer. Different cultures, religions, and individuals may approach this question in different ways.'

Like all LLMs, we can then compose it with other components:

from langchain_core.prompts import PromptTemplate

prompt = PromptTemplate.from_template("What is the meaning of {thing}?")

API Reference:PromptTemplate

chain = prompt | llm
print(chain.invoke({"thing": "life"}))

Prompt:
What is the meaning of life?
Output:
What is the meaning of life?
As an AI language model, my personal belief is that the meaning of life varies from person to person. It might be finding happiness, fulfilling a purpose or goal, or making a difference in the world. It's ultimately a personal question that can be explored through introspection or by seeking guidance from others.

Gemma on Vertex AI Model Garden

Gemma is a set of lightweight, generative artificial intelligence (AI) open models. Gemma models are available to run in your applications and on your hardware, mobile devices, or hosted services. You can also customize these models using tuning techniques so that they excel at performing tasks that matter to you and your users. Gemma models are based on Gemini models and are intended for the AI development community to extend and take further.

To use Gemma on Vertex Model Garden you must first deploy it to Vertex AI Endpoint

from langchain_core.messages import (
    AIMessage,
    HumanMessage,
)
from langchain_google_vertexai import (
    GemmaChatVertexAIModelGarden,
    GemmaVertexAIModelGarden,
)

API Reference:AIMessage | HumanMessage

Anthropic on Vertex AI

# TODO : Add "YOUR PROJECT" , "YOUR REGION" and "YOUR ENDPOINT_ID"
llm = GemmaVertexAIModelGarden(
    endpoint_id="YOUR PROJECT",
    project="YOUR ENDPOINT_ID",
    location="YOUR REGION",
)

# invoke a model response
llm.invoke("What is the meaning of life?")

'Prompt:\nWhat is the meaning of life?\nOutput:\nThis is a classic question that has captivated philosophers, theologians, and seekers for'

# TODO : Add "YOUR PROJECT" , "YOUR REGION" and "YOUR ENDPOINT_ID"
chat_llm = GemmaChatVertexAIModelGarden(
    endpoint_id="YOUR PROJECT",
    project="YOUR ENDPOINT_ID",
    location="YOUR REGION",
)

# Prepare input for model consumption
text_question1 = "How much is 2+2?"
message1 = HumanMessage(content=text_question1)

# invoke a model response
chat_llm.invoke([message1])

AIMessage(content='Prompt:\n<start_of_turn>user\nHow much is 2+2?<end_of_turn>\n<start_of_turn>model\nOutput:\nThe answer is 4.\n2 + 2 = 4.', id='run-cea563df-e91a-4374-83a1-3d8b186a01b2-0')

Anthropic Claude 3 models on Vertex AI offer fully managed and serverless models as APIs. To use a Claude model on Vertex AI, send a request directly to the Vertex AI API endpoint. Because Anthropic Claude 3 models use a managed API, there's no need to provision or manage infrastructure.

NOTE : Anthropic Models on Vertex are implemented as Chat Model through class ChatAnthropicVertex

!pip install -U langchain-google-vertexai anthropic[vertex]

from langchain_core.messages import (
    AIMessage,
    AIMessageChunk,
    HumanMessage,
    SystemMessage,
)
from langchain_core.outputs import LLMResult
from langchain_google_vertexai.model_garden import ChatAnthropicVertex

API Reference:AIMessage | AIMessageChunk | HumanMessage | SystemMessage | LLMResult

NOTE : Specify the correct Claude 3 Model Versions

• For Claude 3 Opus (Preview), use claude-3-opus@20240229.
• For Claude 3 Sonnet, use claude-3-sonnet@20240229.
• For Claude 3 Haiku, use claude-3-haiku@20240307.

We don't recommend using the Anthropic Claude 3 model versions that don't include a suffix that starts with an @ symbol (claude-3-opus, claude-3-sonnet, or claude-3-haiku).

# TODO : Replace below with your project id and region
project = "<project_id>"
location = "<region>"

# Initialise the Model
model = ChatAnthropicVertex(
    model_name="claude-3-haiku@20240307",
    project=project,
    location=location,
)

# prepare input data for the model
raw_context = (
    "My name is Peter. You are my personal assistant. My favorite movies "
    "are Lord of the Rings and Hobbit."
)
question = (
    "Hello, could you recommend a good movie for me to watch this evening, please?"
)
context = SystemMessage(content=raw_context)
message = HumanMessage(content=question)

# Invoke the model
response = model.invoke([context, message])
print(response.content)

Since your favorite movies are the Lord of the Rings and Hobbit trilogies, I would recommend checking out some other epic fantasy films that have a similar feel:

1. The Chronicles of Narnia series - These films are based on the beloved fantasy novels by C.S. Lewis and have a great blend of adventure, magic, and memorable characters.

2. Stardust - This 2007 fantasy film, based on the Neil Gaiman novel, has an excellent cast and a charming, whimsical tone.

3. The Golden Compass - The first film adaptation of Philip Pullman's His Dark Materials series, with stunning visuals and a compelling story.

4. Pan's Labyrinth - Guillermo del Toro's dark, fairy tale-inspired masterpiece set against the backdrop of the Spanish Civil War.

5. The Princess Bride - A classic fantasy adventure film with humor, romance, and unforgettable characters.

Let me know if any of those appeal to you or if you'd like me to suggest something else! I'm happy to provide more personalized recommendations.

# You can choose to initialize/ override the model name on Invoke method as well
response = model.invoke([context, message], model_name="claude-3-sonnet@20240229")
print(response.content)

Sure, I'd be happy to recommend a movie for you! Since you mentioned that The Lord of the Rings and The Hobbit are among your favorite movies, I'll suggest some other epic fantasy/adventure films you might enjoy:

1. The Princess Bride (1987) - A classic fairy tale with adventure, romance, and a lot of wit and humor. It has an all-star cast and very quotable lines.

2. Willow (1988) - A fun fantasy film produced by George Lucas with fairies, dwarves, and brownies going on an epic quest. Has a similar tone to the Lord of the Rings movies.

3. Stardust (2007) - An underrated fantasy adventure based on the Neil Gaiman novel about a young man entering a magical kingdom to retrieve a fallen star. Great cast and visuals.

4. The Chronicles of Narnia series - The Lion, The Witch and The Wardrobe is the best known, but the other Narnia films are also very well done fantasy epics.

5. The Golden Compass (2007) - First installment of the His Dark Materials trilogy, set in a parallel universe with armored polar bears and truth-seeking devices.

Let me know if you'd like any other suggestions or have a particular style of movie in mind! I aimed for entertaining fantasy/adventure flicks similar to Lord of the Rings.

# Use streaming responses
sync_response = model.stream([context, message], model_name="claude-3-haiku@20240307")
for chunk in sync_response:
    print(chunk.content)