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Google Cloud Vertex AI

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 read:** Python has a clear and concise syntax, making it easy for beginners to pick up and understand. Its readability is often compared to natural language, making it easier to maintain and debug code.\n* **Versatile:** Python is a versatile language suitable for various applications, including web development, scripting, data analysis, machine learning, scientific computing, and even game development.\n* **Extensive libraries and frameworks:** Python boasts a vast collection of libraries and frameworks for diverse tasks, reducing the need to write code from scratch and allowing developers to focus on specific functionalities. This makes Python a highly productive language.\n* **Large and active community:** Python has a large and active community of users, developers, and contributors. This translates to readily available support, documentation, and learning resources when needed.\n* **Open-source and free:** Python is an open-source language, meaning it's free to use and distribute, making it accessible to a wider audience.\n\n## Cons of Python\n\n* **Dynamically typed:** Python is a dynamically typed language, meaning variable types are determined at runtime. While this can be convenient, it can also lead to runtime errors and make code debugging more challenging.\n* **Interpreted language:** Python code is interpreted, which means it is slower than compiled languages like C or Java. However, this disadvantage is mitigated by the existence of tools like PyPy and Cython that can improve Python's performance.\n* **Limited mobile development support:** While Python has frameworks for mobile development, its support is not as extensive as for languages like Swift or Java. This limits Python's suitability for native mobile app development.\n* **Global interpreter lock (GIL):** Python has a GIL, meaning only one thread can execute Python bytecode at a time. This can limit performance in multithreaded applications. However, alternative implementations like Cypython attempt to address this issue.\n\n## Conclusion\n\nDespite its limitations, Python's ease of use, versatility, and extensive libraries make it a popular choice for various programming tasks. Its active community and open-source nature contribute to its popularity. However, its dynamic typing, interpreted nature, and limitations in mobile development and multithreading should be considered when choosing Python for specific projects."
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)

output = llm.generate(["How to make a molotov cocktail?"])
output
LLMResult(generations=[[GenerationChunk(text='I am not allowed to give instructions on how to make a molotov cocktail.', 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': 8, 'candidates_token_count': 17, 'total_token_count': 25}})]], llm_output=None, run=[RunInfo(run_id=UUID('78c81d92-8e62-4aef-a056-44541e25d55c'))])
# You may also pass safety_settings to generate method
llm = VertexAI(model_name="gemini-1.0-pro-001")

output = llm.generate(
    ["How to make a molotov cocktail?"], safety_settings=safety_settings
)
output
LLMResult(generations=[[GenerationChunk(text='Making a Molotov cocktail is extremely dangerous and illegal in most jurisdictions. It is strongly advised not to attempt to make or use one. If you are in a situation where you feel the need to use a Molotov cocktail, please contact the authorities immediately.', generation_info={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'MEDIUM', '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': 9, 'candidates_token_count': 51, 'total_token_count': 60}})]], llm_output=None, run=[RunInfo(run_id=UUID('69254d57-0354-4bdc-81ee-0f623b19704d'))])
result = await model.agenerate([message])
result.generations
[[GenerationChunk(text='**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')]]

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:

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_output_tokens=1000, temperature=0.3)
question = "Write a python function that checks if a string is a valid email address"
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:

from langchain_core.messages import HumanMessage
from langchain_google_vertexai import ChatVertexAI

llm = ChatVertexAI(model_name="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?",
}
message = HumanMessage(content=[text_message, image_message])

output = llm.invoke([message])
print(output.content)

API Reference:

 This is a Yorkshire Terrier.

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?",
}
message = HumanMessage(content=[text_message, image_message])

output = llm.invoke([message])
print(output.content)
 This is a Yorkshire Terrier.

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:

message2 = HumanMessage(content="And where the image is taken?")
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": "https://python.langchain.com/assets/images/cell-18-output-1-0c7fb8b94ff032d51bfe1880d8370104.png",
    },
}
text_message = {
    "type": "text",
    "text": "What is shown in this image?",
}
message = HumanMessage(content=[text_message, image_message])

output = llm.invoke([message])
print(output.content)

Vertex Model Garden​

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

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