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참고/guardrails-main/docs/api_reference/llm_interaction.md
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# Helpers for LLM Interactions
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Class for representing a prompt entry.
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## BasePrompt
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```python
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class BasePrompt()
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```
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Base class for representing an LLM prompt.
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#### \_\_init\_\_
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```python
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def __init__(source: str,
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output_schema: Optional[str] = None,
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*,
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xml_output_schema: Optional[str] = None)
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```
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Initialize and substitute constants in the prompt.
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#### substitute\_constants
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```python
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def substitute_constants(text: str) -> str
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```
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Substitute constants in the prompt.
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#### get\_prompt\_variables
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```python
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def get_prompt_variables() -> List[str]
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```
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#### format
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```python
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def format(**kwargs) -> "BasePrompt"
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```
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#### escape
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```python
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def escape() -> str
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```
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Escape single curly braces into double curly braces.
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The LLM prompt.
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## Prompt
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```python
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class Prompt(BasePrompt)
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```
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Prompt class.
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The prompt is passed to the LLM as primary instructions.
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#### format
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```python
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def format(**kwargs) -> "Prompt"
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```
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Format the prompt using the given keyword arguments.
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Instructions to the LLM, to be passed in the prompt.
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## Instructions
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```python
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class Instructions(BasePrompt)
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```
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Instructions class.
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The instructions are passed to the LLM as secondary input. Different
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model may use these differently. For example, chat models may
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receive instructions in the system-prompt.
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#### format
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```python
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def format(**kwargs) -> "Instructions"
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```
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Format the prompt using the given keyword arguments.
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## PromptCallableBase
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## LLMResponse
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```python
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class LLMResponse(ILLMResponse)
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```
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Standard information collection from LLM responses to feed the
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validation loop.
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**Attributes**:
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- `output` _str_ - The output from the LLM.
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- `stream_output` _Optional[Iterator]_ - A stream of output from the LLM.
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Default None.
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- `async_stream_output` _Optional[AsyncIterator]_ - An async stream of output
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from the LLM. Default None.
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- `prompt_token_count` _Optional[int]_ - The number of tokens in the prompt.
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Default None.
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- `response_token_count` _Optional[int]_ - The number of tokens in the response.
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Default None.
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