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Merge pull request #287 from langchain-ai/bagatur/vertexai_docstring
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docs: ChatVertexAI rich docstring
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@baskaryan
baskaryan authored Jun 11, 2024
2 parents f11446a + ddec437 commit dd223b2
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2 changes: 1 addition & 1 deletion libs/vertexai/langchain_google_vertexai/_base.py
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Expand Up @@ -72,7 +72,7 @@ class _VertexAIBase(BaseModel):
client_options: Optional["ClientOptions"] = Field(
default=None, exclude=True
) #: :meta private:
api_endpoint: Optional[str] = None
api_endpoint: Optional[str] = Field(None, alias="base_url")
"Desired API endpoint, e.g., us-central1-aiplatform.googleapis.com"
api_transport: Optional[str] = None
"""The desired API transport method, can be either 'grpc' or 'rest'"""
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348 changes: 347 additions & 1 deletion libs/vertexai/langchain_google_vertexai/chat_models.py
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Expand Up @@ -610,7 +610,353 @@ async def _completion_with_retry_inner(


class ChatVertexAI(_VertexAICommon, BaseChatModel):
"""`Vertex AI` Chat large language models API."""
"""Google Cloud Vertex AI chat model integration.
Setup:
You must have the langchain-google-vertexai Python package installed
.. code-block:: bash
pip install -U langchain-google-vertexai
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
and https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth.
Key init args — completion params:
model: str
Name of ChatVertexAI model to use. e.g. "gemini-1.5-flash-001",
"gemini-1.5-pro-001", etc.
temperature: Optional[float]
Sampling temperature.
max_tokens: Optional[int]
Max number of tokens to generate.
stop: Optional[List[str]]
Default stop sequences.
safety_settings: Optional[Dict[vertexai.generative_models.HarmCategory, vertexai.generative_models.HarmBlockThreshold]]
The default safety settings to use for all generations.
Key init args — client params:
max_retries: int
Max number of retries.
credentials: Optional[google.auth.credentials.Credentials]
The default custom credentials to use when making API calls. If not
provided, credentials will be ascertained from the environment.
project: Optional[str]
The default GCP project to use when making Vertex API calls.
location: str = "us-central1"
The default location to use when making API calls.
request_parallelism: int = 5
The amount of parallelism allowed for requests issued to VertexAI models.
Default is 5.
base_url: Optional[str]
Base URL for API requests.
See full list of supported init args and their descriptions in the params section.
Instantiate:
.. code-block:: python
from langchain_google_vertexai import ChatVertexAI
llm = ChatVertexAI(
model="gemini-1.5-flash-001",
temperature=0,
max_tokens=None,
max_retries=6,
stop=None,
# other params...
)
Invoke:
.. code-block:: python
messages = [
("system", "You are a helpful translator. Translate the user sentence to French."),
("human", "I love programming."),
]
llm.invoke(messages)
.. code-block:: python
AIMessage(content="J'adore programmer. \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}], 'citation_metadata': None, 'usage_metadata': {'prompt_token_count': 17, 'candidates_token_count': 7, 'total_token_count': 24}}, id='run-925ce305-2268-44c4-875f-dde9128520ad-0')
Stream:
.. code-block:: python
for chunk in llm.stream(messages):
print(chunk)
.. code-block:: python
AIMessageChunk(content='J', response_metadata={'is_blocked': False, 'safety_ratings': [], 'citation_metadata': None}, id='run-9df01d73-84d9-42db-9d6b-b1466a019e89')
AIMessageChunk(content="'adore programmer. \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}], 'citation_metadata': None}, id='run-9df01d73-84d9-42db-9d6b-b1466a019e89')
AIMessageChunk(content='', response_metadata={'is_blocked': False, 'safety_ratings': [], 'citation_metadata': None, 'usage_metadata': {'prompt_token_count': 17, 'candidates_token_count': 7, 'total_token_count': 24}}, id='run-9df01d73-84d9-42db-9d6b-b1466a019e89')
.. code-block:: python
stream = llm.stream(messages)
full = next(stream)
for chunk in stream:
full += chunk
full
.. code-block:: python
AIMessageChunk(content="J'adore programmer. \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}], 'citation_metadata': None, 'usage_metadata': {'prompt_token_count': 17, 'candidates_token_count': 7, 'total_token_count': 24}}, id='run-b7f7492c-4cb5-42d0-8fc3-dce9b293b0fb')
Async:
.. code-block:: python
await llm.ainvoke(messages)
# stream:
# async for chunk in (await llm.astream(messages))
# batch:
# await llm.abatch([messages])
.. code-block:: python
AIMessage(content="J'adore programmer. \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}], 'citation_metadata': None, 'usage_metadata': {'prompt_token_count': 17, 'candidates_token_count': 7, 'total_token_count': 24}}, id='run-925ce305-2268-44c4-875f-dde9128520ad-0')
Tool calling:
.. code-block:: python
from langchain_core.pydantic_v1 import BaseModel, Field
class GetWeather(BaseModel):
'''Get the current weather in a given location'''
location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
class GetPopulation(BaseModel):
'''Get the current population in a given location'''
location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
ai_msg = llm_with_tools.invoke("Which city is hotter today and which is bigger: LA or NY?")
ai_msg.tool_calls
.. code-block:: python
[{'name': 'GetWeather',
'args': {'location': 'Los Angeles, CA'},
'id': '2a2401fa-40db-470d-83ce-4e52de910d9e'},
{'name': 'GetWeather',
'args': {'location': 'New York City, NY'},
'id': '96761deb-ab7f-4ef9-b4b4-6d44562fc46e'},
{'name': 'GetPopulation',
'args': {'location': 'Los Angeles, CA'},
'id': '9147d532-abee-43a2-adb5-12f164300484'},
{'name': 'GetPopulation',
'args': {'location': 'New York City, NY'},
'id': 'c43374ea-bde5-49ca-8487-5b83ebeea1e6'}]
See ``ChatVertexAI.bind_tools()`` method for more.
Structured output:
.. code-block:: python
from typing import Optional
from langchain_core.pydantic_v1 import BaseModel, Field
class Joke(BaseModel):
'''Joke to tell user.'''
setup: str = Field(description="The setup of the joke")
punchline: str = Field(description="The punchline to the joke")
rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")
structured_llm = llm.with_structured_output(Joke)
structured_llm.invoke("Tell me a joke about cats")
.. code-block:: python
Joke(setup='What do you call a cat that loves to bowl?', punchline='An alley cat!', rating=None)
See ``ChatVertexAI.with_structured_output()`` for more.
Image input:
.. code-block:: python
import base64
import httpx
from langchain_core.messages import HumanMessage
image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
message = HumanMessage(
content=[
{"type": "text", "text": "describe the weather in this image"},
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
},
],
)
ai_msg = llm.invoke([message])
ai_msg.content
.. code-block:: python
'The weather in this image appears to be sunny and pleasant. The sky is a bright blue with scattered white clouds, suggesting a clear and mild day. The lush green grass indicates recent rainfall or sufficient moisture. The absence of strong shadows suggests that the sun is high in the sky, possibly late afternoon. Overall, the image conveys a sense of tranquility and warmth, characteristic of a beautiful summer day. \n'
You can also point to GCS files which is faster / more efficient because bytes are transferred back and forth.
.. code-block:: python
llm.invoke(
[
HumanMessage(
[
"What's in the image?",
{
"type": "media",
"file_uri": "gs://cloud-samples-data/generative-ai/image/scones.jpg",
"mime_type": "image/jpeg",
},
]
)
]
).content
.. code-block:: python
'The image is of five blueberry scones arranged on a piece of baking paper. \n\nHere is a list of what is in the picture:\n* **Five blueberry scones:** They are scattered across the parchment paper, dusted with powdered sugar. \n* **Two cups of coffee:** Two white cups with saucers. One appears full, the other partially drunk.\n* **A bowl of blueberries:** A brown bowl is filled with fresh blueberries, placed near the scones.\n* **A spoon:** A silver spoon with the words "Let\'s Jam" rests on the paper.\n* **Pink peonies:** Several pink peonies lie beside the scones, adding a touch of color.\n* **Baking paper:** The scones, cups, bowl, and spoon are arranged on a piece of white baking paper, splattered with purple. The paper is crinkled and sits on a dark surface. \n\nThe image has a rustic and delicious feel, suggesting a cozy and enjoyable breakfast or brunch setting. \n'
Video input:
**NOTE**: Currently only supported for ``gemini-...-vision`` models.
.. code-block:: python
llm = ChatVertexAI(model="gemini-1.0-pro-vision")
llm.invoke(
[
HumanMessage(
[
"What's in the video?",
{
"type": "media",
"file_uri": "gs://cloud-samples-data/video/animals.mp4",
"mime_type": "video/mp4",
},
]
)
]
).content
.. code-block:: python
'The video is about a new feature in Google Photos called "Zoomable Selfies". The feature allows users to take selfies with animals at the zoo. The video shows several examples of people taking selfies with animals, including a tiger, an elephant, and a sea otter. The video also shows how the feature works. Users simply need to open the Google Photos app and select the "Zoomable Selfies" option. Then, they need to choose an animal from the list of available animals. The app will then guide the user through the process of taking the selfie.'
Audio input:
.. code-block:: python
from langchain_core.messages import HumanMessage
llm = ChatVertexAI(model="gemini-1.5-flash-001")
llm.invoke(
[
HumanMessage(
[
"What's this audio about?",
{
"type": "media",
"file_uri": "gs://cloud-samples-data/generative-ai/audio/pixel.mp3",
"mime_type": "audio/mpeg",
},
]
)
]
).content
.. code-block:: python
"This audio is an interview with two product managers from Google who work on Pixel feature drops. They discuss how feature drops are important for showcasing how Google devices are constantly improving and getting better. They also discuss some of the highlights of the January feature drop and the new features coming in the March drop for Pixel phones and Pixel watches. The interview concludes with discussion of how user feedback is extremely important to them in deciding which features to include in the feature drops. "
Token usage:
.. code-block:: python
ai_msg = llm.invoke(messages)
ai_msg.usage_metadata
.. code-block:: python
{'input_tokens': 17, 'output_tokens': 7, 'total_tokens': 24}
Response metadata
.. code-block:: python
ai_msg = llm.invoke(messages)
ai_msg.response_metadata
.. code-block:: python
{'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': 17,
'candidates_token_count': 7,
'total_token_count': 24}}
Safety settings
.. code-block:: python
from langchain_google_vertexai import HarmBlockThreshold, HarmCategory
llm = ChatVertexAI(
model="gemini-1.5-pro",
safety_settings={
HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_ONLY_HIGH,
},
)
llm.invoke(messages).response_metadata
.. code-block:: python
{'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': 17,
'candidates_token_count': 7,
'total_token_count': 24}}
""" # noqa: E501

model_name: str = Field(default="chat-bison", alias="model")
"Underlying model name."
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4 changes: 4 additions & 0 deletions libs/vertexai/langchain_google_vertexai/gemma.py
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Expand Up @@ -103,6 +103,10 @@ class GemmaVertexAIModelGarden(VertexAIModelGarden):
def _llm_type(self) -> str:
return "gemma_vertexai_model_garden"

# Needed so that mypy doesn't flag missing aliased init args.
def __init__(self, **kwargs: Any) -> None:
super().__init__(**kwargs)


class GemmaChatVertexAIModelGarden(_GemmaBase, _BaseVertexAIModelGarden, BaseChatModel):
allowed_model_args: Optional[List[str]] = [
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8 changes: 8 additions & 0 deletions libs/vertexai/langchain_google_vertexai/model_garden.py
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Expand Up @@ -70,6 +70,10 @@ class Config:

allow_population_by_field_name = True

# Needed so that mypy doesn't flag missing aliased init args.
def __init__(self, **kwargs: Any) -> None:
super().__init__(**kwargs)

def _generate(
self,
prompts: List[str],
Expand Down Expand Up @@ -139,6 +143,10 @@ class Config:

allow_population_by_field_name = True

# Needed so that mypy doesn't flag missing aliased init args.
def __init__(self, **kwargs: Any) -> None:
super().__init__(**kwargs)

@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
from anthropic import ( # type: ignore
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