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2026-05-12 19:40:31 +09:00
# Helpers for LLM Interactions
Class for representing a prompt entry.
## BasePrompt
```python
class BasePrompt()
```
Base class for representing an LLM prompt.
#### \_\_init\_\_
```python
def __init__(source: str,
output_schema: Optional[str] = None,
*,
xml_output_schema: Optional[str] = None)
```
Initialize and substitute constants in the prompt.
#### substitute\_constants
```python
def substitute_constants(text: str) -> str
```
Substitute constants in the prompt.
#### get\_prompt\_variables
```python
def get_prompt_variables() -> List[str]
```
#### format
```python
def format(**kwargs) -> "BasePrompt"
```
#### escape
```python
def escape() -> str
```
Escape single curly braces into double curly braces.
The LLM prompt.
## Prompt
```python
class Prompt(BasePrompt)
```
Prompt class.
The prompt is passed to the LLM as primary instructions.
#### format
```python
def format(**kwargs) -> "Prompt"
```
Format the prompt using the given keyword arguments.
Instructions to the LLM, to be passed in the prompt.
## Instructions
```python
class Instructions(BasePrompt)
```
Instructions class.
The instructions are passed to the LLM as secondary input. Different
model may use these differently. For example, chat models may
receive instructions in the system-prompt.
#### format
```python
def format(**kwargs) -> "Instructions"
```
Format the prompt using the given keyword arguments.
## PromptCallableBase
## LLMResponse
```python
class LLMResponse(ILLMResponse)
```
Standard information collection from LLM responses to feed the
validation loop.
**Attributes**:
- `output` _str_ - The output from the LLM.
- `stream_output` _Optional[Iterator]_ - A stream of output from the LLM.
Default None.
- `async_stream_output` _Optional[AsyncIterator]_ - An async stream of output
from the LLM. Default None.
- `prompt_token_count` _Optional[int]_ - The number of tokens in the prompt.
Default None.
- `response_token_count` _Optional[int]_ - The number of tokens in the response.
Default None.