marvin.utilities.jinja

Module for Jinja utilities.

BaseEnvironment

BaseEnvironment provides a configurable environment for rendering Jinja templates.

This class encapsulates a Jinja environment with customizable global functions and template settings, allowing for flexible template rendering.

Attributes:

Name	Type	Description
environment	Environment	The Jinja environment for template rendering.
globals	dict[str, Any]	A dictionary of global functions and variables available in templates.

Example

Basic Usage of BaseEnvironment

env = BaseEnvironment()

rendered = env.render("Hello, {{ name }}!", name="World")
print(rendered)  # Output: Hello, World!

render

Renders a given template str or BaseTemplate with provided context.

Parameters:

Name	Type	Description	Default
template	Union[str, Template]	The template to be rendered.	required
**kwargs	Any	Context variables to be passed to the template.	{}

Returns:

Type	Description
str	The rendered template as a string.

Example

Basic Usage of BaseEnvironment.render

from marvin.utilities.jinja import Environment as jinja_env

rendered = jinja_env.render("Hello, {{ name }}!", name="World")
print(rendered) # Output: Hello, World!

Transcript

Transcript is a model representing a conversation involving multiple roles.

Attributes:

Name	Type	Description
content	str	The content of the transcript.
roles	dict[str, str]	The roles involved in the transcript.
environment	BaseEnvironment	The jinja environment to use for rendering the transcript.

Example

Basic Usage of Transcript:

from marvin.utilities.jinja import Transcript

transcript = Transcript(
    content="system: Hello, there! user: Hello, yourself!",
    roles=["system", "user"],
)
print(transcript.render_to_messages())
# [
#   BaseMessage(content='system: Hello, there!', role='system'),
#   BaseMessage(content='Hello, yourself!', role='user')
# ]

split_text_by_tokens

Splits a given text by a list of tokens.

Parameters:

Name	Type	Description	Default
text	str	The text to be split. split_tokens: The tokens to split the text	required
by.	only_on_newline	If True, only match tokens that are either	required

Returns:

Type	Description
list[tuple[str, str]]	A list of tuples containing the token and the text following it.

Example

Basic Usage of split_text_by_tokens ```python from marvin.utilities.jinja import split_text_by_tokens

text = "Hello, World!" split_tokens = ["Hello", "World"] pairs = split_text_by_tokens(text, split_tokens) print(pairs) # Output: [("Hello", ", "), ("World", "!")] ```