From 9e7c906a8e6c2f73617a9daffd132605e9fd7015 Mon Sep 17 00:00:00 2001
From: vincent d warmerdam <vincentwarmerdam@gmail.com>
Date: Wed, 12 Mar 2025 15:41:16 +0100
Subject: [PATCH 1/2] moar

---
 README.md             |  90 ++++++-
 demo.py               |  19 ++
 imgs/logo.png         | Bin 0 -> 19802 bytes
 smartfunc/__init__.py |  11 +-
 uv.lock               | 555 ++++++++++++++++++++++++++++++++++++++++++
 5 files changed, 672 insertions(+), 3 deletions(-)
 create mode 100644 demo.py
 create mode 100644 imgs/logo.png

diff --git a/README.md b/README.md
index c77a597..6509bc0 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,88 @@
-# smartfunc
-Turn docstrings into LLM-functions
+<img src="imgs/logo.png" width="125" height="125" align="right" />
+
+### smartfunc
+
+> Turn docstrings into LLM-functions
+
+## Installation
+
+```bash
+uv pip install smartfunc
+```
+
+
+## What is this?
+
+Here is a nice example of what is possible with this library:
+
+```python
+from smartfunc import backend
+
+@backend("gpt-4")
+def generate_summary(text: str):
+    """Generate a summary of the following text: {{ text }}"""
+    pass
+```
+
+The `generate_summary` function will now return a string with the summary of the text.
+
+## How does it work?
+
+This library wraps around the [llm library](https://llm.datasette.io/en/stable/index.html) made by Simon Willison. The docstring is parsed and turned into a Jinja2 template which we inject with variables to generate a prompt at runtime. We then use the backend given by the decorator to run the prompt and return the result.
+
+The `llm` library is minimalistic and while it does not support all the features out there it does offer a solid foundation to build on. This library is mainly meant as a method to add some syntactic sugar on top. We do get a few nice benefits from the `llm` library though:
+
+- The `llm` library is well maintained and has a large community
+- An [ecosystem of backends](https://llm.datasette.io/en/stable/plugins/directory.html) for different LLM providers
+- Many of the vendors have `async` support, which allows us to do microbatching
+- Many of the vendors have schema support, which allows us to use Pydantic models to define the response
+- You can use `.env` files to store your API keys
+
+## Extra features
+
+The following snippet shows how you might create a re-useable backend decorator that uses a system prompt. Also notice how we're able to use a Pydantic model to define the response.
+
+```python
+from smartfunc import backend
+from pydantic import BaseModel
+
+class Summary(BaseModel):
+    summary: str
+    pros: list[str]
+    cons: list[str]
+
+llmify = backend("gpt-4", system="You are a helpful assistant.", temperature=0.5)
+
+@llmify
+def generate_poke_desc(text: str) -> Summary:
+    """Describe the following pokemon: {{ text }}"""
+    pass
+
+print(generate_poke_desc("pikachu"))
+```
+
+This is the result that we got back:
+
+```
+{
+    'summary': 'Pikachu is a small, electric-type Pokémon known for its adorable appearance and strong electrical abilities. It is recognized as the mascot of the Pokémon franchise, with distinctive features and a cheerful personality.', 
+    'pros': [
+        'Iconic and recognizable worldwide', 
+        'Strong electric attacks like Thunderbolt', 
+        'Has a cute and friendly appearance', 
+        'Evolves into Raichu with a Thunder Stone', 
+        'Popular choice in Pokémon merchandise and media'
+    ], 
+    'cons': [
+        'Not very strong in higher-level battles', 
+        'Weak against ground-type moves', 
+        'Limited to electric-type attacks unless learned through TMs', 
+        'Can be overshadowed by other powerful Pokémon in competitive play'
+    ],
+}
+```
+
+
+> [!NOTE] 
+> You might look at this example and wonder if you might be better off using [instructor](https://python.useinstructor.com/). After all, that library has more support for validation of parameters. This is true, but instructor requires you to learn a fair bit more about each individual backend. If you want to to use claude instead of openai then you will need to load in a different library. Here, you just need to make sure the `llm` plugin is installed and you're good to go.
+
diff --git a/demo.py b/demo.py
new file mode 100644
index 0000000..932bafb
--- /dev/null
+++ b/demo.py
@@ -0,0 +1,19 @@
+from smartfunc import backend
+from pydantic import BaseModel
+from dotenv import load_dotenv
+
+load_dotenv(".env")
+
+class Summary(BaseModel):
+    summary: str
+    pros: list[str]
+    cons: list[str]
+
+llmify = backend("gpt-4o-mini", system="You are a pirate.", temperature=0.5)
+
+@llmify
+def generate_poke_desc(text: str) -> Summary:
+    """Describe the following pokemon: {{ text }}"""
+    pass
+
+print(generate_poke_desc("pikachu"))
\ No newline at end of file
diff --git a/imgs/logo.png b/imgs/logo.png
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diff --git a/smartfunc/__init__.py b/smartfunc/__init__.py
index a86236c..d138bd1 100644
--- a/smartfunc/__init__.py
+++ b/smartfunc/__init__.py
@@ -180,4 +180,13 @@ async def wrapper(*args, **kwargs):
 
     async def run(self, func, *args, **kwargs):
         new_func = self(func)
-        return new_func(*args, **kwargs)
\ No newline at end of file
+        return new_func(*args, **kwargs)
+    
+
+def get_backend_models():
+    for model in llm.get_models():
+        print(model.model_id)
+
+def get_async_backend_models():
+    for model in llm.get_async_models():
+        print(model.model_id)
diff --git a/uv.lock b/uv.lock
index d58fbf2..f223a49 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,7 +1,562 @@
 version = 1
 requires-python = ">=3.12"
 
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
+]
+
+[[package]]
+name = "anyio"
+version = "4.8.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "idna" },
+    { name = "sniffio" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a3/73/199a98fc2dae33535d6b8e8e6ec01f8c1d76c9adb096c6b7d64823038cde/anyio-4.8.0.tar.gz", hash = "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a", size = 181126 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/46/eb/e7f063ad1fec6b3178a3cd82d1a3c4de82cccf283fc42746168188e1cdd5/anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a", size = 96041 },
+]
+
+[[package]]
+name = "certifi"
+version = "2025.1.31"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/1c/ab/c9f1e32b7b1bf505bf26f0ef697775960db7932abeb7b516de930ba2705f/certifi-2025.1.31.tar.gz", hash = "sha256:3d5da6925056f6f18f119200434a4780a94263f10d1c21d032a6f6b2baa20651", size = 167577 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/38/fc/bce832fd4fd99766c04d1ee0eead6b0ec6486fb100ae5e74c1d91292b982/certifi-2025.1.31-py3-none-any.whl", hash = "sha256:ca78db4565a652026a4db2bcdf68f2fb589ea80d0be70e03929ed730746b84fe", size = 166393 },
+]
+
+[[package]]
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+version = "8.1.8"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "platform_system == 'Windows'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b9/2e/0090cbf739cee7d23781ad4b89a9894a41538e4fcf4c31dcdd705b78eb8b/click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a", size = 226593 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7e/d4/7ebdbd03970677812aac39c869717059dbb71a4cfc033ca6e5221787892c/click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2", size = 98188 },
+]
+
+[[package]]
+name = "click-default-group"
+version = "1.2.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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From cb58c81ac7bb34d647ffd2fe2098f274d808dd38 Mon Sep 17 00:00:00 2001
From: vincent d warmerdam <vincentwarmerdam@gmail.com>
Date: Wed, 12 Mar 2025 15:49:40 +0100
Subject: [PATCH 2/2] readme

---
 README.md | 42 ++++++++++++++++++++++++++++++++++++++----
 demo.py   | 19 -------------------
 2 files changed, 38 insertions(+), 23 deletions(-)
 delete mode 100644 demo.py

diff --git a/README.md b/README.md
index 6509bc0..a5f8f42 100644
--- a/README.md
+++ b/README.md
@@ -40,18 +40,23 @@ The `llm` library is minimalistic and while it does not support all the features
 
 ## Extra features
 
+### Schemas
+
 The following snippet shows how you might create a re-useable backend decorator that uses a system prompt. Also notice how we're able to use a Pydantic model to define the response.
 
 ```python
 from smartfunc import backend
 from pydantic import BaseModel
+from dotenv import load_dotenv
+
+load_dotenv(".env")
 
 class Summary(BaseModel):
     summary: str
     pros: list[str]
     cons: list[str]
 
-llmify = backend("gpt-4", system="You are a helpful assistant.", temperature=0.5)
+llmify = backend("gpt-4o-mini", system="You are a helpful assistant.", temperature=0.5)
 
 @llmify
 def generate_poke_desc(text: str) -> Summary:
@@ -63,7 +68,7 @@ print(generate_poke_desc("pikachu"))
 
 This is the result that we got back:
 
-```
+```python
 {
     'summary': 'Pikachu is a small, electric-type Pokémon known for its adorable appearance and strong electrical abilities. It is recognized as the mascot of the Pokémon franchise, with distinctive features and a cheerful personality.', 
     'pros': [
@@ -82,7 +87,36 @@ This is the result that we got back:
 }
 ```
 
+Not every backend supports schemas, but you will a helpful error message if that is the case.
+
+> [!NOTE]  
+> You might look at this example and wonder if you might be better off using [instructor](https://python.useinstructor.com/). After all, that library has more support for validation of parameters and even has some utilities for multi-turn conversations. All of this is true, but instructor requires you to learn a fair bit more about each individual backend. If you want to to use claude instead of openai then you will need to load in a different library. Here, you just need to make sure the `llm` plugin is installed and you're good to go.
+
+
+### Async
+
+The library also supports async functions. This is useful if you want to do microbatching or if you want to use the async backends from the `llm` library.
+
+```python
+import asyncio
+from smartfunc import async_backend
+from pydantic import BaseModel
+from dotenv import load_dotenv
+
+load_dotenv(".env")
+
 
-> [!NOTE] 
-> You might look at this example and wonder if you might be better off using [instructor](https://python.useinstructor.com/). After all, that library has more support for validation of parameters. This is true, but instructor requires you to learn a fair bit more about each individual backend. If you want to to use claude instead of openai then you will need to load in a different library. Here, you just need to make sure the `llm` plugin is installed and you're good to go.
+class Summary(BaseModel):
+    summary: str
+    pros: list[str]
+    cons: list[str]
 
+
+@async_backend("gpt-4o-mini")
+async def generate_poke_desc(text: str) -> Summary:
+    """Describe the following pokemon: {{ text }}"""
+    pass
+
+resp = asyncio.run(generate_poke_desc("pikachu"))
+print(resp)
+```
diff --git a/demo.py b/demo.py
deleted file mode 100644
index 932bafb..0000000
--- a/demo.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from smartfunc import backend
-from pydantic import BaseModel
-from dotenv import load_dotenv
-
-load_dotenv(".env")
-
-class Summary(BaseModel):
-    summary: str
-    pros: list[str]
-    cons: list[str]
-
-llmify = backend("gpt-4o-mini", system="You are a pirate.", temperature=0.5)
-
-@llmify
-def generate_poke_desc(text: str) -> Summary:
-    """Describe the following pokemon: {{ text }}"""
-    pass
-
-print(generate_poke_desc("pikachu"))
\ No newline at end of file