refactor: Update Gemini provider to handle conversation-based completions and add structured response

.gitignore

@@ -167,4 +167,3 @@ cython_debug/
 
 src/**
 requirements.txt
-Pipfile

CHANGELOG.md

@@ -1,19 +1,10 @@
 Release History
 ===============
 
-## 0.1.6 (2024-10-31)
-
-- Add `sm.Plugin` syntax sugar.
-- Improvements to Anthropic provider, related to max tokens.
-- General improvements.
-- Add tests for structured response.
-- Add `llm_model` to `structured_response`.
-
 ## 0.1.5 (2024-10-31)
 
 - Add Gemini provider.
 - Add structured response to Gemini provider.
-- Support for Python 3.10.
 
 ## 0.1.4 (2024-10-30)
 

README.md

@@ -6,6 +6,8 @@ Simplemind is AI library designed to simplify your experience with AI APIs in Py
 
 ![simplemind](https://github.com/user-attachments/assets/36df2103-2583-4958-ad5e-19cda7740256)
 
+[![Auto Wiki](https://img.shields.io/badge/Auto_Wiki-Mutable.ai-blue)](https://mutable.ai/kennethreitz/simplemind)
+
 ## Features
 
 With Simplemind, tapping into AI is as easy as a friendly conversation.
@@ -16,7 +18,7 @@ With Simplemind, tapping into AI is as easy as a friendly conversation.
 
 ## Supported APIs
 
-To specify a specific provider or model, you can use the `llm_provider` and `llm_model` parameters when calling: `generate_text`, `generate_data`, or `create_conversation`. The APIs remain identital between all supported providers/models.
+To specify a specific provider or model, you can use the `llm_provider` and `llm_model` parameters when calling: `generate_text`, `generate_data`, or `create_conversation`.
 
 - [**Anthropic's Claude**](https://www.anthropic.com/claude)
 - [**Google's Gemini**](https://gemini.google/)
@@ -25,7 +27,7 @@ To specify a specific provider or model, you can use the `llm_provider` and `llm
 - [**OpenAI's GPT**](https://openai.com/gpt)
 - [**xAI's Grok**](https://x.ai/)
 
-If you want to see Simplemind support, additional providers or models, please send a pull request!
+If you want to see Simplemind support, additional providers or models, please request a pull!
 
 ## Why SimpleMind?
 - **Intuitive**: Built with Pythonic simplicity and readability in mind.
@@ -48,7 +50,7 @@ First, authenticate your API keys by setting them in the environment variables:
 $ export OPENAI_API_KEY="sk-..."
 ```
 
-This pattern allows you to keep your API keys private and out of your codebase. Other supported environment variables: `ANTHROPIC_API_KEY`, `XAI_API_KEY`, `GROQ_API_KEY`, and `GEMINI_API_KEY`.
+This pattern allows you to keep your API keys private and out of your codebase. Other supported environment variables: `ANTHROPIC_API_KEY`, `XAI_API_KEY`, and `GROQ_API_KEY`.
 
 Next, import Simplemind and start using it:
 
@@ -217,4 +219,3 @@ Simplemind is licensed under the Apache 2.0 License.
 
 ## Acknowledgements
 Simplemind is inspired by the philosophy of "code for humans" and aims to make working with AI models accessible to all. Special thanks to the open-source community for their contributions and inspiration.
-

docs/conf.py

@@ -16,7 +16,7 @@ import simplemind
 project = "simplemind"
 copyright = "2024 Kenneth Reitz"
 author = "Kenneth Reitz"
-release = "v0.1.6"
+release = "v0.1.5"
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "simplemind"
-version = "0.1.6"
+version = "0.1.5"
 description = "An experimental client for AI providers that intends to replace LangChain and LangGraph for most common use cases."
 readme = "README.md"
 requires-python = ">=3.10"

simplemind/__init__.py

@@ -16,7 +16,7 @@ class Session:
         self,
         *,
         llm_provider: str = settings.DEFAULT_LLM_PROVIDER,
-        llm_model: str | None = None,
+        llm_model: str = settings.DEFAULT_LLM_MODEL,
         **kwargs,
     ):
         self.llm_provider = llm_provider
@@ -113,9 +113,6 @@ def generate_text(
     return provider.generate_text(prompt=prompt, llm_model=llm_model, **kwargs)
 
 
-# Syntax sugar.
-Plugin = BasePlugin
-
 __all__ = [
     "create_conversation",
     "find_provider",
@@ -124,5 +121,4 @@ __all__ = [
     "settings",
     "BasePlugin",
     "Session",
-    "Plugin",
 ]

simplemind/providers/anthropic.py

@@ -13,14 +13,12 @@ T = TypeVar("T", bound=BaseModel)
 
 PROVIDER_NAME = "anthropic"
 DEFAULT_MODEL = "claude-3-5-sonnet-20241022"
-DEFAULT_MAX_TOKENS = 1_000
-DEFAULT_KWARGS = {"max_tokens": DEFAULT_MAX_TOKENS}
+DEFAULT_MAX_TOKENS = 1000
 
 
 class Anthropic(BaseProvider):
     NAME = PROVIDER_NAME
     DEFAULT_MODEL = DEFAULT_MODEL
-    DEFAULT_KWARGS = DEFAULT_KWARGS
 
     def __init__(self, api_key: str | None = None):
         self.api_key = api_key or settings.get_api_key(PROVIDER_NAME)
@@ -48,7 +46,8 @@ class Anthropic(BaseProvider):
         response = self.client.messages.create(
             model=conversation.llm_model or self.DEFAULT_MODEL,
             messages=messages,
-            **{**self.DEFAULT_KWARGS, **kwargs},
+            max_tokens=DEFAULT_MAX_TOKENS,
+            **kwargs,
         )
 
         # Get the response content from the Anthropic response
@@ -63,22 +62,9 @@ class Anthropic(BaseProvider):
             llm_provider=PROVIDER_NAME,
         )
 
-    def structured_response(
-        self, response_model: Type[T], *, llm_model: str | None = None, **kwargs
-    ) -> T:
-        model = llm_model or self.DEFAULT_MODEL
-
-        # Extract the prompt from kwargs if it exists
-        prompt = kwargs.pop("prompt", kwargs.pop("messages", ""))
-
-        # Format the messages properly
-        messages = [{"role": "user", "content": prompt}]
-
+    def structured_response(self, model: str, response_model: Type[T], **kwargs) -> T:
         response = self.structured_client.messages.create(
-            model=model,
-            messages=messages,  # Add the messages parameter
-            response_model=response_model,
-            **{**self.DEFAULT_KWARGS, **kwargs},
+            model=model or self.DEFAULT_MODEL, response_model=response_model, **kwargs
         )
         return response
 
@@ -90,7 +76,8 @@ class Anthropic(BaseProvider):
         response = self.client.messages.create(
             model=llm_model or self.DEFAULT_MODEL,
             messages=messages,
-            **{**self.DEFAULT_KWARGS, **kwargs},
+            max_tokens=DEFAULT_MAX_TOKENS,
+            **kwargs,
         )
 
         return response.content[0].text

simplemind/providers/groq.py

@@ -72,7 +72,6 @@ class Groq(BaseProvider):
         response = self.structured_client.chat.completions.create(
             messages=messages,
             response_model=response_model,
-            model=kwargs.pop("llm_model", self.DEFAULT_MODEL),
             **kwargs,
         )
         return response

simplemind/providers/ollama.py

@@ -65,12 +65,7 @@ class Ollama(BaseProvider):
         )
 
     def structured_response(
-        self,
-        prompt: str,
-        response_model: Type[T],
-        *,
-        llm_model: str | None = None,
-        **kwargs,
+        self, prompt: str, response_model: Type[T], *, llm_model: str, **kwargs
     ) -> T:
         """Get a structured response from the Ollama API."""
         messages = [
@@ -85,7 +80,7 @@ class Ollama(BaseProvider):
         )
         return response
 
-    def generate_text(self, prompt: str, *, llm_model: str | None = None) -> str:
+    def generate_text(self, prompt: str, *, llm_model: str) -> str:
         """Generate text using the Ollama API."""
         messages = [
             {"role": "user", "content": prompt},

simplemind/settings.py

@@ -29,6 +29,7 @@ class Settings(BaseSettings):
     )
     XAI_API_KEY: Optional[SecretStr] = Field(None, description="API key for xAI")
     DEFAULT_LLM_PROVIDER: str = Field("openai", description="The default LLM provider")
+    DEFAULT_LLM_MODEL: str = Field("gpt-4o-mini", description="The default LLM model")
 
     model_config = SettingsConfigDict(
         env_file=".env", env_file_encoding="utf-8", case_sensitive=True, extra="ignore"

tests/conftest.py

@@ -1,15 +0,0 @@
-import pytest
-
-import os
-import sys
-
-# Add the project root to the Python path.
-sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-
-from simplemind import Session
-
-
-@pytest.fixture
-def sm():
-    """Fixture that provides a simplemind Session instance with default settings."""
-    return Session()

tests/test_basics.py

@@ -1,2 +0,0 @@
-def test_basic_math():
-    assert 1 + 1 == 2

tests/test_generate_data.py

@@ -1,28 +0,0 @@
-import pytest
-
-from simplemind.providers import Anthropic, Gemini, OpenAI, Groq, Ollama
-from pydantic import BaseModel
-
-
-class ResponseModel(BaseModel):
-    result: int
-
-
-@pytest.mark.parametrize(
-    "provider_cls",
-    [
-        Anthropic,
-        Gemini,
-        OpenAI,
-        Groq,
-        Ollama,
-    ],
-)
-def test_generate_data(provider_cls):
-    provider = provider_cls()
-    prompt = "What is 2+2?"
-
-    data = provider.structured_response(prompt=prompt, response_model=ResponseModel)
-
-    assert isinstance(data, ResponseModel)
-    assert type(data.result) == int

tests/test_generate_text.py

@@ -1,23 +0,0 @@
-import pytest
-
-from simplemind.providers import Anthropic, Gemini, OpenAI, Groq, Ollama
-
-
-@pytest.mark.parametrize(
-    "provider_cls",
-    [
-        Anthropic,
-        Gemini,
-        OpenAI,
-        Groq,
-        Ollama,
-    ],
-)
-def test_generate_text(provider_cls):
-    provider = provider_cls()
-    prompt = "What is 2+2?"
-
-    response = provider.generate_text(prompt=prompt, llm_model=provider.DEFAULT_MODEL)
-
-    assert isinstance(response, str)
-    assert len(response) > 0