Bump version to v0.1.7 in conf.py and pyproject.toml

Add default kwargs logic to Groq, OpenAI, XAI, and Ollama providers

.gitignore

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

CHANGELOG.md

@@ -1,6 +1,26 @@
 Release History
 ===============
 
+## 0.1.7 (2024-11-01)
+
+- Add `logger` decorator.
+- Add `sm.enable_logfire()` function.
+- General improvements.
+
+## 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)
 
 - Introduce `Session` class to manage repeatability.

README.md

@@ -6,8 +6,6 @@ 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.
@@ -18,7 +16,7 @@ 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`.
+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.
 
 - [**Anthropic's Claude**](https://www.anthropic.com/claude)
 - [**Google's Gemini**](https://gemini.google/)
@@ -27,7 +25,7 @@ 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 request a pull!
+If you want to see Simplemind support, additional providers or models, please send a pull request!
 
 ## Why SimpleMind?
 - **Intuitive**: Built with Pythonic simplicity and readability in mind.
@@ -50,7 +48,7 @@ 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`, and `GROQ_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`, `GROQ_API_KEY`, and `GEMINI_API_KEY`.
 
 Next, import Simplemind and start using it:
 
@@ -219,3 +217,4 @@ 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 = "v0.1.4"
+release = "v0.1.7"
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "simplemind"
-version = "0.1.4"
+version = "0.1.7"
 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 = settings.DEFAULT_LLM_MODEL,
+        llm_model: str | None = None,
         **kwargs,
     ):
         self.llm_provider = llm_provider
@@ -113,6 +113,14 @@ def generate_text(
     return provider.generate_text(prompt=prompt, llm_model=llm_model, **kwargs)
 
 
+def enable_logfire() -> None:
+    """Enable logfire logging."""
+    settings.logging.enable_logfire()
+
+
+# Syntax sugar.
+Plugin = BasePlugin
+
 __all__ = [
     "create_conversation",
     "find_provider",
@@ -121,4 +129,6 @@ __all__ = [
     "settings",
     "BasePlugin",
     "Session",
+    "Plugin",
+    "enable_logfire",
 ]

simplemind/logging.py

@@ -0,0 +1,33 @@
+import time
+from typing import Any, Callable
+
+import logfire
+
+from .settings import settings
+
+
+def logger(func: Callable[..., Any]) -> Callable[..., Any]:
+    """A decorator that logs the function parameters, function returns,
+    and exceptions raised if logging is enabled, using logfire.
+    """
+
+    def wrapper(*args, **kwargs) -> Any:
+        if not settings.logging.is_enabled:
+            return func(*args, **kwargs)
+
+        logfire.info(f"Calling {func.__name__} with args: {args}, kwargs: {kwargs}")
+        t1 = time.perf_counter()
+
+        try:
+            result = func(*args, **kwargs)
+            t2 = time.perf_counter()
+            logfire.info(f"{func.__name__} returned: {result} in {t2-t1} seconds")
+
+            return result
+
+        except Exception as e:
+            t2 = time.perf_counter()
+            logfire.error(f"Error in {func.__name__}: {e} in {t2-t1} seconds")
+            raise e
+
+    return wrapper

simplemind/models.py

@@ -1,6 +1,6 @@
-from types import TracebackType
 import uuid
 from datetime import datetime
+from types import TracebackType
 from typing import Any, Dict, List, Literal, Optional
 
 from pydantic import BaseModel, Field

simplemind/providers/_base.py

@@ -1,10 +1,13 @@
 from abc import ABC, abstractmethod
 from functools import cached_property
-from typing import Any, Type, TypeVar
+from typing import TYPE_CHECKING, Any, Type, TypeVar
 
 from instructor import Instructor
 from pydantic import BaseModel
 
+if TYPE_CHECKING:
+    from ..models import Conversation, Message
+
 T = TypeVar("T", bound=BaseModel)
 
 

simplemind/providers/anthropic.py

@@ -1,24 +1,29 @@
 from functools import cached_property
-from typing import Type, TypeVar
+from typing import TYPE_CHECKING, Type, TypeVar
 
-import anthropic
 import instructor
 from pydantic import BaseModel
 
+from ..logging import logger
 from ..settings import settings
 from ._base import BaseProvider
 
+if TYPE_CHECKING:
+    from ..models import Conversation, Message
+
 T = TypeVar("T", bound=BaseModel)
 
 
 PROVIDER_NAME = "anthropic"
 DEFAULT_MODEL = "claude-3-5-sonnet-20241022"
-DEFAULT_MAX_TOKENS = 1000
+DEFAULT_MAX_TOKENS = 1_000
+DEFAULT_KWARGS = {"max_tokens": DEFAULT_MAX_TOKENS}
 
 
 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)
@@ -28,6 +33,13 @@ class Anthropic(BaseProvider):
         """The raw Anthropic client."""
         if not self.api_key:
             raise ValueError("Anthropic API key is required")
+        try:
+            import anthropic
+        except ImportError as exc:
+            raise ImportError(
+                "Please install the `anthropic` package: `pip install anthropic`"
+            ) from exc
+
         return anthropic.Anthropic(api_key=self.api_key)
 
     @cached_property
@@ -35,7 +47,8 @@ class Anthropic(BaseProvider):
         """A client patched with Instructor."""
         return instructor.from_anthropic(self.client)
 
-    def send_conversation(self, conversation: "Conversation", **kwargs):
+    @logger
+    def send_conversation(self, conversation: "Conversation", **kwargs) -> "Message":
         """Send a conversation to the Anthropic API."""
         from ..models import Message
 
@@ -46,8 +59,7 @@ class Anthropic(BaseProvider):
         response = self.client.messages.create(
             model=conversation.llm_model or self.DEFAULT_MODEL,
             messages=messages,
-            max_tokens=DEFAULT_MAX_TOKENS,
-            **kwargs,
+            **{**self.DEFAULT_KWARGS, **kwargs},
         )
 
         # Get the response content from the Anthropic response
@@ -62,12 +74,27 @@ class Anthropic(BaseProvider):
             llm_provider=PROVIDER_NAME,
         )
 
-    def structured_response(self, model: str, response_model: Type[T], **kwargs) -> T:
-        response = self.structured_client.messages.create(
-            model=model or self.DEFAULT_MODEL, response_model=response_model, **kwargs
-        )
-        return response
+    @logger
+    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}]
+
+        response = self.structured_client.messages.create(
+            model=model,
+            messages=messages,  # Add the messages parameter
+            response_model=response_model,
+            **{**self.DEFAULT_KWARGS, **kwargs},
+        )
+        return response_model.model_validate(response)
+
+    @logger
     def generate_text(self, prompt: str, *, llm_model: str, **kwargs):
         messages = [
             {"role": "user", "content": prompt},
@@ -76,8 +103,7 @@ class Anthropic(BaseProvider):
         response = self.client.messages.create(
             model=llm_model or self.DEFAULT_MODEL,
             messages=messages,
-            max_tokens=DEFAULT_MAX_TOKENS,
-            **kwargs,
+            **{**self.DEFAULT_KWARGS, **kwargs},
         )
 
         return response.content[0].text

simplemind/providers/gemini.py

@@ -2,21 +2,25 @@
 # IT is not currently working as desired.
 
 from functools import cached_property
-from typing import Type, TypeVar
+from typing import TYPE_CHECKING, Type, TypeVar
 
-import google.generativeai as genai
 import instructor
 from pydantic import BaseModel
 
+from ..logging import logger
 from ..settings import settings
 from ._base import BaseProvider
 
-PROVIDER_NAME = "gemini"
-DEFAULT_MODEL = "models/gemini-1.5-flash-latest"
+if TYPE_CHECKING:
+    from ..models import Conversation, Message
 
 T = TypeVar("T", bound=BaseModel)
 
 
+PROVIDER_NAME = "gemini"
+DEFAULT_MODEL = "models/gemini-1.5-flash-latest"
+
+
 class Gemini(BaseProvider):
     NAME = PROVIDER_NAME
     DEFAULT_MODEL = DEFAULT_MODEL
@@ -25,12 +29,21 @@ class Gemini(BaseProvider):
         self.api_key = api_key or settings.get_api_key(PROVIDER_NAME)
         self.model_name = DEFAULT_MODEL
 
+    def set_model(self, model_name: str):
+        self.model_name = model_name
+
     @cached_property
-    def client(self, model_name: str = DEFAULT_MODEL):
+    def client(self):
         """The raw Gemini client."""
         if not self.api_key:
             raise ValueError("Gemini API key is required")
-        self.model_name = model_name
+        try:
+            import google.generativeai as genai
+        except ImportError as exc:
+            raise ImportError(
+                "Please install the `google-generativeai` package: `pip install google-generativeai`"
+            ) from exc
+        genai.configure(api_key=self.api_key)
         return genai.GenerativeModel(model_name=self.model_name)
 
     @cached_property
@@ -38,6 +51,7 @@ class Gemini(BaseProvider):
         """A Gemini client patched with Instructor."""
         return instructor.from_gemini(self.client)
 
+    @logger
     def send_conversation(self, conversation: "Conversation") -> "Message":
         """Send a conversation to the Gemini API."""
         from ..models import Message
@@ -64,9 +78,11 @@ class Gemini(BaseProvider):
             llm_provider=PROVIDER_NAME,
         )
 
+    @logger
     def structured_response(self, prompt: str, response_model: Type[T], **kwargs) -> T:
         """Send a structured response to the Gemini API."""
-        llm_model = kwargs.pop("llm_model", self.model_name)
+        # Only try to pop if the key exists
+        kwargs.pop("llm_model", None)  # Add default value of None
 
         try:
             response = self.structured_client.chat.completions.create(
@@ -79,12 +95,12 @@ class Gemini(BaseProvider):
             raise RuntimeError(
                 f"Failed to send structured response to Gemini API: {e}"
             ) from e
-        return response
+        return response_model.model_validate(response)
 
+    @logger
     def generate_text(self, prompt: str, **kwargs) -> str:
         """Generate text using the Gemini API."""
-        llm_model = kwargs.pop("llm_model", self.model_name)
-
+        kwargs.pop("llm_model")
         try:
             response = self.client.generate_content(prompt, **kwargs)
         except Exception as e:

simplemind/providers/groq.py

@@ -1,22 +1,29 @@
 from functools import cached_property
-from typing import Type, TypeVar
+from typing import TYPE_CHECKING, Type, TypeVar
 
-import groq
 import instructor
 from pydantic import BaseModel
 
+from ..logging import logger
 from ..settings import settings
 from ._base import BaseProvider
 
-PROVIDER_NAME = "groq"
-DEFAULT_MODEL = "llama3-8b-8192"
+if TYPE_CHECKING:
+    from ..models import Conversation, Message
 
 T = TypeVar("T", bound=BaseModel)
 
 
+PROVIDER_NAME = "groq"
+DEFAULT_MODEL = "llama3-8b-8192"
+DEFAULT_MAX_TOKENS = 1_000
+DEFAULT_KWARGS = {"max_tokens": DEFAULT_MAX_TOKENS}
+
+
 class Groq(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)
@@ -26,6 +33,12 @@ class Groq(BaseProvider):
         """The raw Groq client."""
         if not self.api_key:
             raise ValueError("Groq API key is required")
+        try:
+            import groq
+        except ImportError as exc:
+            raise ImportError(
+                "Please install the `groq` package: `pip install groq`"
+            ) from exc
         return groq.Groq(api_key=self.api_key)
 
     @cached_property
@@ -33,6 +46,7 @@ class Groq(BaseProvider):
         """A client patched with Instructor."""
         return instructor.from_groq(self.client)
 
+    @logger
     def send_conversation(
         self,
         conversation: "Conversation",
@@ -48,7 +62,7 @@ class Groq(BaseProvider):
         response = self.client.chat.completions.create(
             model=conversation.llm_model or self.DEFAULT_MODEL,
             messages=messages,
-            **kwargs,
+            **{**self.DEFAULT_KWARGS, **kwargs},
         )
 
         # Get the response content from the Groq response
@@ -63,6 +77,7 @@ class Groq(BaseProvider):
             llm_provider=PROVIDER_NAME,
         )
 
+    @logger
     def structured_response(self, prompt: str, response_model: Type[T], **kwargs) -> T:
         # Ensure messages are provided in kwargs
         messages = [
@@ -72,17 +87,19 @@ class Groq(BaseProvider):
         response = self.structured_client.chat.completions.create(
             messages=messages,
             response_model=response_model,
-            **kwargs,
+            model=kwargs.pop("llm_model", self.DEFAULT_MODEL),
+            **{**self.DEFAULT_KWARGS, **kwargs},
         )
-        return response
+        return response_model.model_validate(response)
 
+    @logger
     def generate_text(
         self,
         prompt: str,
         *,
         llm_model: str,
         **kwargs,
-    ):
+    ) -> str:
         messages = [
             {"role": "user", "content": prompt},
         ]
@@ -90,7 +107,7 @@ class Groq(BaseProvider):
         response = self.client.chat.completions.create(
             messages=messages,
             model=llm_model or self.DEFAULT_MODEL,
-            **kwargs,
+            **{**self.DEFAULT_KWARGS, **kwargs},
         )
 
-        return response.choices[0].message.content
+        return str(response.choices[0].message.content)

simplemind/providers/ollama.py

@@ -1,25 +1,30 @@
 from functools import cached_property
-from typing import Type, TypeVar
+from typing import TYPE_CHECKING, Type, TypeVar
 
 import instructor
-import ollama as ol
 from openai import OpenAI
 from pydantic import BaseModel
 
+from ..logging import logger
 from ..settings import settings
 from ._base import BaseProvider
 
+if TYPE_CHECKING:
+    from ..models import Conversation, Message
+
 T = TypeVar("T", bound=BaseModel)
 
 
 PROVIDER_NAME = "ollama"
 DEFAULT_MODEL = "llama3.2"
 DEFAULT_TIMEOUT = 60
+DEFAULT_KWARGS = {}
 
 
 class Ollama(BaseProvider):
     NAME = PROVIDER_NAME
     DEFAULT_MODEL = DEFAULT_MODEL
+    DEFAULT_KWARGS = DEFAULT_KWARGS
     TIMEOUT = DEFAULT_TIMEOUT
 
     def __init__(self, host_url: str | None = None):
@@ -30,6 +35,12 @@ class Ollama(BaseProvider):
         """The raw Ollama client."""
         if not self.host_url:
             raise ValueError("No ollama host url provided")
+        try:
+            import ollama as ol
+        except ImportError as exc:
+            raise ImportError(
+                "Please install the `ollama` package: `pip install ollama`"
+            ) from exc
         return ol.Client(timeout=self.TIMEOUT, host=self.host_url)
 
     @cached_property
@@ -43,7 +54,8 @@ class Ollama(BaseProvider):
             mode=instructor.Mode.JSON,
         )
 
-    def send_conversation(self, conversation: "Conversation") -> "Message":
+    @logger
+    def send_conversation(self, conversation: "Conversation", **kwargs) -> "Message":
         """Send a conversation to the Ollama API."""
         from ..models import Message
 
@@ -51,7 +63,9 @@ class Ollama(BaseProvider):
             {"role": msg.role, "content": msg.text} for msg in conversation.messages
         ]
         response = self.client.chat(
-            model=conversation.llm_model or DEFAULT_MODEL, messages=messages
+            model=conversation.llm_model or DEFAULT_MODEL,
+            messages=messages,
+            **{**self.DEFAULT_KWARGS, **kwargs},
         )
         assistant_message = response.get("message")
 
@@ -64,8 +78,14 @@ class Ollama(BaseProvider):
             llm_provider=PROVIDER_NAME,
         )
 
+    @logger
     def structured_response(
-        self, prompt: str, response_model: Type[T], *, llm_model: str, **kwargs
+        self,
+        prompt: str,
+        response_model: Type[T],
+        *,
+        llm_model: str | None = None,
+        **kwargs,
     ) -> T:
         """Get a structured response from the Ollama API."""
         messages = [
@@ -76,18 +96,23 @@ class Ollama(BaseProvider):
             messages=messages,
             model=llm_model or self.DEFAULT_MODEL,
             response_model=response_model,
-            **kwargs,
+            **{**self.DEFAULT_KWARGS, **kwargs},
         )
-        return response
+        return response_model.model_validate(response)
 
-    def generate_text(self, prompt: str, *, llm_model: str) -> str:
+    @logger
+    def generate_text(
+        self, prompt: str, *, llm_model: str | None = None, **kwargs
+    ) -> str:
         """Generate text using the Ollama API."""
         messages = [
             {"role": "user", "content": prompt},
         ]
 
         response = self.client.chat(
-            messages=messages, model=llm_model or self.DEFAULT_MODEL
+            messages=messages,
+            model=llm_model or self.DEFAULT_MODEL,
+            **{**self.DEFAULT_KWARGS, **kwargs},
         )
 
         return response.get("message", {}).get("content", "")

simplemind/providers/openai.py

@@ -1,22 +1,28 @@
 from functools import cached_property
-from typing import Type, TypeVar
+from typing import TYPE_CHECKING, Type, TypeVar
 
 import instructor
-import openai as oa
 from pydantic import BaseModel
 
+from ..logging import logger
 from ..settings import settings
 from ._base import BaseProvider
 
+if TYPE_CHECKING:
+    from ..models import Conversation, Message
+
 T = TypeVar("T", bound=BaseModel)
 
 PROVIDER_NAME = "openai"
 DEFAULT_MODEL = "gpt-4o-mini"
+DEFAULT_MAX_TOKENS = 1_000
+DEFAULT_KWARGS = {"max_tokens": DEFAULT_MAX_TOKENS}
 
 
 class OpenAI(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)
@@ -26,6 +32,12 @@ class OpenAI(BaseProvider):
         """The raw OpenAI client."""
         if not self.api_key:
             raise ValueError("OpenAI API key is required")
+        try:
+            import openai as oa
+        except ImportError as exc:
+            raise ImportError(
+                "Please install the `openai` package: `pip install openai`"
+            ) from exc
         return oa.OpenAI(api_key=self.api_key)
 
     @cached_property
@@ -33,7 +45,8 @@ class OpenAI(BaseProvider):
         """A OpenAI client with Instructor."""
         return instructor.from_openai(self.client)
 
-    def send_conversation(self, conversation: "Conversation", **kwargs):
+    @logger
+    def send_conversation(self, conversation: "Conversation", **kwargs) -> "Message":
         """Send a conversation to the OpenAI API."""
         from ..models import Message
 
@@ -42,7 +55,9 @@ class OpenAI(BaseProvider):
         ]
 
         response = self.client.chat.completions.create(
-            model=conversation.llm_model or DEFAULT_MODEL, messages=messages, **kwargs
+            model=conversation.llm_model or DEFAULT_MODEL,
+            messages=messages,
+            **{**self.DEFAULT_KWARGS, **kwargs},
         )
 
         # Get the response content from the OpenAI response
@@ -57,6 +72,7 @@ class OpenAI(BaseProvider):
             llm_provider=PROVIDER_NAME,
         )
 
+    @logger
     def structured_response(
         self,
         prompt: str,
@@ -74,16 +90,19 @@ class OpenAI(BaseProvider):
             messages=messages,
             model=llm_model or self.DEFAULT_MODEL,
             response_model=response_model,
-            **kwargs,
+            **{**self.DEFAULT_KWARGS, **kwargs},
         )
-        return response
+        return response_model.model_validate(response)
 
+    @logger
     def generate_text(self, prompt: str, *, llm_model: str | None = None, **kwargs):
         """Generate text using the OpenAI API."""
         messages = [
             {"role": "user", "content": prompt},
         ]
         response = self.client.chat.completions.create(
-            messages=messages, model=llm_model or self.DEFAULT_MODEL, **kwargs
+            messages=messages,
+            model=llm_model or self.DEFAULT_MODEL,
+            **{**self.DEFAULT_KWARGS, **kwargs},
         )
         return response.choices[0].message.content

simplemind/providers/xai.py

@@ -1,20 +1,30 @@
 from functools import cached_property
+from typing import TYPE_CHECKING, Type, TypeVar
 
 import instructor
-import openai as oa
+from pydantic import BaseModel
 
+from ..logging import logger
 from ..settings import settings
 from ._base import BaseProvider
 
+if TYPE_CHECKING:
+    from ..models import Conversation, Message
+
+T = TypeVar("T", bound=BaseModel)
+
+
 PROVIDER_NAME = "xai"
 DEFAULT_MODEL = "grok-beta"
 BASE_URL = "https://api.x.ai/v1"
 DEFAULT_MAX_TOKENS = 1000
+DEFAULT_KWARGS = {"max_tokens": DEFAULT_MAX_TOKENS}
 
 
 class XAI(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)
@@ -24,6 +34,12 @@ class XAI(BaseProvider):
         """The raw OpenAI client."""
         if not self.api_key:
             raise ValueError("XAI API key is required")
+        try:
+            import openai as oa
+        except ImportError as exc:
+            raise ImportError(
+                "Please install the `openai` package: `pip install openai`"
+            ) from exc
         return oa.OpenAI(
             api_key=self.api_key,
             base_url=BASE_URL,
@@ -34,7 +50,8 @@ class XAI(BaseProvider):
         """A client patched with Instructor."""
         return instructor.from_openai(self.client)
 
-    def send_conversation(self, conversation: "Conversation", **kwargs):
+    @logger
+    def send_conversation(self, conversation: "Conversation", **kwargs) -> "Message":
         """Send a conversation to the OpenAI API."""
         from ..models import Message
 
@@ -45,7 +62,7 @@ class XAI(BaseProvider):
         response = self.client.chat.completions.create(
             model=conversation.llm_model or self.DEFAULT_MODEL,
             messages=messages,
-            **kwargs,
+            **{**self.DEFAULT_KWARGS, **kwargs},
         )
 
         # Get the response content from the OpenAI response
@@ -60,10 +77,14 @@ class XAI(BaseProvider):
             llm_provider=PROVIDER_NAME,
         )
 
-    def structured_response(self, prompt: str, response_model, *, llm_model: str):
+    @logger
+    def structured_response(
+        self, prompt: str, response_model: Type[T], *, llm_model: str
+    ) -> T:
         raise NotImplementedError("XAI does not support structured responses")
 
-    def generate_text(self, prompt: str, *, llm_model: str, **kwargs):
+    @logger
+    def generate_text(self, prompt: str, *, llm_model: str, **kwargs) -> str:
         messages = [
             {"role": "user", "content": prompt},
         ]
@@ -71,7 +92,7 @@ class XAI(BaseProvider):
         response = self.client.chat.completions.create(
             messages=messages,
             model=llm_model or self.DEFAULT_MODEL,
-            **kwargs,
+            **{**self.DEFAULT_KWARGS, **kwargs},
         )
 
-        return response.choices[0].message.content
+        return str(response.choices[0].message.content)

simplemind/settings.py

@@ -1,19 +1,42 @@
-from typing import Literal, Optional, Union
+from typing import Optional, Union
 
 from pydantic import Field, SecretStr, field_validator
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
-logging_level = Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]
-
 
 class LoggingConfig(BaseSettings):
     """The class that holds all the logging settings for the application."""
 
-    enabled: bool = Field(False, description="Enable logging")
-    level: logging_level = Field("INFO", description="The logging level")
+    is_enabled: bool = Field(False, description="Enable logging")
 
     model_config = SettingsConfigDict(extra="forbid")
 
+    def enable_logfire(self, **kwargs) -> None:
+        """Enable logging for the application."""
+        # adding imports here to avoid forced dependencies
+        try:
+            import logfire
+            from logging import basicConfig
+        except ImportError as e:
+            raise ImportError(
+                "To enable logging, please install logfire: `pip install logfire`"
+            ) from e
+
+        self.is_enabled = True
+        logfire.configure(**kwargs)
+        basicConfig(handlers=[logfire.LogfireLoggingHandler()])
+
+        try:
+            logfire.configure(**kwargs)
+            basicConfig(handlers=[logfire.LogfireLoggingHandler()])
+        except Exception as e:
+            self.is_enabled = False  # Reset flag on failure
+            raise RuntimeError("Failed to configure logging") from e
+
+    def disable_logfire(self) -> None:
+        """Disable logging for the application."""
+        self.is_enabled = False
+
 
 class Settings(BaseSettings):
     """The class that holds all the API keys for the application."""
@@ -29,7 +52,6 @@ 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

@@ -0,0 +1,15 @@
+import os
+import sys
+
+import pytest
+
+# 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

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

tests/test_generate_data.py

@@ -0,0 +1,28 @@
+from pydantic import BaseModel
+
+import pytest
+from simplemind.providers import Anthropic, Gemini, Groq, Ollama, OpenAI
+
+
+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 isinstance(data.result, int)

tests/test_generate_text.py

@@ -0,0 +1,22 @@
+import pytest
+from simplemind.providers import Anthropic, Gemini, Groq, Ollama, OpenAI
+
+
+@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