ComputaCo / templated

Templated

Templated provides a painless experience for developers looking to build semantic templated functions. With Templated, you can easily define a function with a Jinja2 template string, and use it to generate outputs with dynamic data passed as function arguments.

Installation

Templated can be installed via pip:

pip install templated

Usage

Here is an example of how to use Templated:

from templated import Function

f = Function("Hello, {{name}}!")
result = f(name="world")
print(result)

The output of the above code will be:

Hello, world!

In this example, we have defined a function f with a Jinja2 template string "Hello, {{name}}!". We then called this function with an argument name="world", which filled in the {{name}} placeholder in the template, resulting in the output "Hello, world!".

Stateful Template Arguments

In Templated, you can also define stateful template arguments. Here is an example:

f = Function("{{greeting}}, {{name}}!")
f(greeting="Hello")
f(name="world")
# Output: "Hello, world!"

f(greeting="Goodbye")
f()
# Output: "Goodbye, world!"

In this example, we have defined a function f with a Jinja2 template string "{{greeting}}, {{name}}!". We then called this function with an argument greeting="Hello", which filled in the {{greeting}} placeholder in the template. We then called the function again with an argument name="world", which filled in the {{name}} placeholder in the template, resulting in the output "Hello, world!".

We then called the function again with a different value for the greeting argument, "Goodbye". We did not pass a value for the name argument, so it retained its previous value of "world". This resulted in the output "Goodbye, world!".

Custom Language Models

In Templated, you can also use custom language models for generating output from templates. Here is an example:

from langchain.chat_models import ChatOpenAI
from templated._utils.chat2vanilla_lm import Chat2VanillaLM
from templated.function import Function

LLMChatOpenAI = Chat2VanillaLM(ChatOpenAI)
f = Function("Hello, {{name}}!", llm=LLMChatOpenAI)
result = f(name="world")
print(result)

In this example, we have defined a custom language model LLMChatOpenAI that wraps a ChatOpenAI language model. We then defined a function f with a Jinja2 template string "Hello, {{name}}!", and passed our custom language model as the llm argument to the Function constructor.

Contributing

We welcome contributions to Templated! Please refer to the contributing guidelines for more information.

Tests

poetry run py.test --doctest-modules

License

Templated is licensed under our Modified MIT License.