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