Class Prompt (1.95.1) 

Prompt(
    prompt_data: typing.Optional[PartsType] = None,
    *,
    variables: typing.Optional[typing.List[typing.Dict[str, PartsType]]] = None,
    prompt_name: typing.Optional[str] = None,
    generation_config: typing.Optional[
        vertexai.generative_models._generative_models.GenerationConfig
    ] = None,
    model_name: typing.Optional[str] = None,
    safety_settings: typing.Optional[
        vertexai.generative_models._generative_models.SafetySetting
    ] = None,
    system_instruction: typing.Optional[PartsType] = None,
    tools: typing.Optional[
        typing.List[vertexai.generative_models._generative_models.Tool]
    ] = None,
    tool_config: typing.Optional[
        vertexai.generative_models._generative_models.ToolConfig
    ] = None
)

A prompt which may be a template with variables.

The Prompt class allows users to define a template string with variables represented in curly braces {variable}. The variable name must be a valid Python variable name (no spaces, must start with a letter). These placeholders can be replaced with specific values using the assemble_contents method, providing flexibility in generating dynamic prompts.

Usage: Generate content from a single set of variables:

prompt = Prompt(
    prompt_data="Hello, {name}! Today is {day}. How are you?",
    variables=[{"name": "Alice", "day": "Monday"}]
    generation_config=GenerationConfig(
        temperature=0.1,
        top_p=0.95,
        top_k=20,
        candidate_count=1,
        max_output_tokens=100,
    ),
    model_name="gemini-1.0-pro-002",
    safety_settings=[SafetySetting(
        category=SafetySetting.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
        threshold=SafetySetting.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
        method=SafetySetting.HarmBlockMethod.SEVERITY,
    )],
    system_instruction="Please answer in a short sentence.",
)

# Generate content using the assembled prompt.
prompt.generate_content(
    contents=prompt.assemble_contents(**prompt.variables)
)
```
Generate content with multiple sets of variables:
```
prompt = Prompt(
    prompt_data="Hello, {name}! Today is {day}. How are you?",
    variables=[
        {"name": "Alice", "day": "Monday"},
        {"name": "Bob", "day": "Tuesday"},
    ],
    generation_config=GenerationConfig(
        temperature=0.1,
        top_p=0.95,
        top_k=20,
        candidate_count=1,
        max_output_tokens=100,
    ),
    model_name="gemini-1.0-pro-002",
    safety_settings=[SafetySetting(
        category=SafetySetting.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
        threshold=SafetySetting.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
        method=SafetySetting.HarmBlockMethod.SEVERITY,
    )],
    system_instruction="Please answer in a short sentence.",
)

# Generate content using the assembled prompt for each variable set.
for i in range(len(prompt.variables)):
    prompt.generate_content(
        contents=prompt.assemble_contents(**prompt.variables[i])
    )
```

Methods

Prompt

Prompt(
    prompt_data: typing.Optional[PartsType] = None,
    *,
    variables: typing.Optional[typing.List[typing.Dict[str, PartsType]]] = None,
    prompt_name: typing.Optional[str] = None,
    generation_config: typing.Optional[
        vertexai.generative_models._generative_models.GenerationConfig
    ] = None,
    model_name: typing.Optional[str] = None,
    safety_settings: typing.Optional[
        vertexai.generative_models._generative_models.SafetySetting
    ] = None,
    system_instruction: typing.Optional[PartsType] = None,
    tools: typing.Optional[
        typing.List[vertexai.generative_models._generative_models.Tool]
    ] = None,
    tool_config: typing.Optional[
        vertexai.generative_models._generative_models.ToolConfig
    ] = None
)

Initializes the Prompt with a given prompt, and variables.

__repr__

__repr__() -> str

Returns a string representation of the unassembled prompt.

__str__

__str__() -> str

Returns the prompt data as a string, without any variables replaced.

assemble_contents

assemble_contents(
    **variables_dict: PartsType,
) -> typing.List[vertexai.generative_models._generative_models.Content]

Returns the prompt data, as a List[Content], assembled with variables if applicable. Can be ingested into model.generate_content to make API calls.

generate_content

generate_content(
    contents: ContentsType,
    *,
    generation_config: typing.Optional[GenerationConfigType] = None,
    safety_settings: typing.Optional[SafetySettingsType] = None,
    model_name: typing.Optional[str] = None,
    tools: typing.Optional[
        typing.List[vertexai.generative_models._generative_models.Tool]
    ] = None,
    tool_config: typing.Optional[
        vertexai.generative_models._generative_models.ToolConfig
    ] = None,
    stream: bool = False,
    system_instruction: typing.Optional[PartsType] = None
) -> typing.Union[
    vertexai.generative_models._generative_models.GenerationResponse,
    typing.Iterable[vertexai.generative_models._generative_models.GenerationResponse],
]

Generates content using the saved Prompt configs.

get_unassembled_prompt_data

get_unassembled_prompt_data() -> PartsType

Returns the prompt data, without any variables replaced.