docs: Document template lifecycle and refine #10
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tagline - Expand README with definition, proxy vs non-proxy lifecycle, examples, and guidance - Update project description to "Type-safe templating for python"
Summary of ChangesHello @srnnkls, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request primarily enhances the documentation for the Highlights
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Code Review
This pull request significantly improves the documentation by adding a detailed explanation of the template lifecycle, which is a great addition for users of the library. The new content is well-structured and clarifies the differences between using and not using SerializationProxy. I've provided a couple of suggestions to make the code examples in the new documentation self-contained and runnable, which should improve the user experience.
| from pydantic import PlainSerializer | ||
| from typing import Annotated |
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This code example is missing some necessary imports (dataclass and template). To make it easier for users to copy and run the code, it's best to include all required imports.
| from pydantic import PlainSerializer | |
| from typing import Annotated | |
| from dataclasses import dataclass | |
| from deigma import template | |
| from pydantic import PlainSerializer | |
| from typing import Annotated |
| @template("{{ user }}", use_proxy=False) | ||
| class UserTemplate: | ||
| user: User | ||
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| t = UserTemplate(user=User(first_name="Li", last_name="Si")) | ||
| # At this point, nothing is serialized yet | ||
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| str(t) # Serialization happens here | ||
| # If you call str(t) again, serialization runs again | ||
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| # Note: In templates with loops/conditionals, you lose type info: | ||
| @template( | ||
| """ | ||
| {% for keyword in keywords %} | ||
| - {{ keyword.name }} {# keyword is now a plain dict, not SQLKeyword #} | ||
| {% endfor %} | ||
| """, | ||
| use_proxy=False | ||
| ) | ||
| class ListTemplate: | ||
| keywords: list[SQLKeyword] |
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This example uses User and SQLKeyword types without defining them, and is also missing the import for @template. This makes the example incomplete and could be confusing for readers. I suggest adding minimal definitions for these types and the necessary import to make the snippet self-contained and runnable.
from dataclasses import dataclass
from deigma import template
# Assuming User and SQLKeyword are defined as dataclasses for this example
@dataclass
class User:
first_name: str
last_name: str
@dataclass
class SQLKeyword:
name: str
description: str
@template("{{ user }}", use_proxy=False)
class UserTemplate:
user: User
t = UserTemplate(user=User(first_name="Li", last_name="Si"))
# At this point, nothing is serialized yet
str(t) # Serialization happens here
# If you call str(t) again, serialization runs again
# Note: In templates with loops/conditionals, you lose type info:
@template(
"""
{% for keyword in keywords %}
- {{ keyword.name }} {# keyword is now a plain dict, not SQLKeyword #}
{% endfor %}
""",
use_proxy=False
)
class ListTemplate:
keywords: list[SQLKeyword]
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