Gemini - VertexAI

ertexAI is Google’s cloud platform for building, training, and deploying machine learning models.

​Authentication

Authenticate with Gcloud

​Example

Use Gemini with your Agent:

agent.py

from bitca.agent import Agent, RunResponse
from bitca.model.vertexai import Gemini

agent = Agent(
    model=Gemini(id="gemini-1.5-flash"),
    markdown=True
)

# Get the response in a variable
# run: RunResponse = agent.run("Share a 2 sentence horror story.")
# print(run.content)

# Print the response on the terminal
agent.print_response("Share a 2 sentence horror story.")

​Params

Parameter	Type	Default	Description
id	str	"claude-3-5-sonnet-20240620"	The specific model ID used for generating responses.
name	str	"Claude"	The name identifier for the agent.
provider	str	"Anthropic"	The provider of the model.
max_tokens	Optional[int]	1024	The maximum number of tokens to generate in the response.
temperature	Optional[float]	-	The sampling temperature to use, between 0 and 2. Higher values like 0.8 make the output more random, while lower values like 0.2 make it more focused and deterministic.
stop_sequences	Optional[List[str]]	-	A list of sequences where the API will stop generating further tokens.
top_p	Optional[float]	-	Nucleus sampling parameter. The model considers the results of the tokens with top_p probability mass.
top_k	Optional[int]	-	The number of highest probability vocabulary tokens to keep for top-k-filtering.
request_params	Optional[Dict[str, Any]]	-	Additional parameters to include in the request.
api_key	Optional[str]	-	The API key for authenticating requests to the service.
client_params	Optional[Dict[str, Any]]	-	Additional parameters for client configuration.
client	Optional[AnthropicClient]	-	A pre-configured instance of the Anthropic client.