eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]#
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool#
Return whether or not the class is serializable.
pydantic model langchain.llms.VertexAI[source]#
Wrapper around Google Vertex AI large language models.
Validators
raise_deprecation » all fields
set_verbose » verbose
validate_environment » all fields
field credentials: Any = None#
The default custom credentials (google.auth.credentials.Credentials) to use
field location: str = 'us-central1'#
The default location to use when making API calls.
field max_output_tokens: int = 128#
Token limit determines the maximum amount of text output from one prompt.
field project: Optional[str] = None#
The default GCP project to use when making Vertex API calls.
field stop: Optional[List[str]] = None#
Optional list of stop words to use when generating.
field tags: Optional[List[str]] = None#
Tags to add to the run trace.
field temperature: float = 0.0#
Sampling temperature, it controls the degree of randomness in token selection.
field top_k: int = 40#
How the model selects tokens for output, the next token is selected from
field top_p: float = 0.95#
Tokens are selected from most probable to least until the sum of their
field tuned_model_name: Optional[str] = None#
The name of a tuned model, if it’s provided, model_name is ignored.
field verbose: bool [Optional]#
Whether to print out response text.
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/llms.html