Merge pull request #22 from Siddhesh-Agarwal/main

Added Gemini Provider

.envrc.template

@@ -1,5 +1,6 @@
-export OPENAI_API_KEY=""
 export ANTHROPIC_API_KEY=""
-export XAI_API_KEY=""
-export OLLAMA_HOST_URL=""
+export GEMINI_API_KEY=""
 export GROQ_API_KEY=""
+export OLLAMA_HOST_URL=""
+export OPENAI_API_KEY=""
+export XAI_API_KEY=""

README.md

@@ -20,13 +20,14 @@ With Simplemind, tapping into AI is as easy as a friendly 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`.
 
-- **[OpenAI's GPT](https://openai.com/gpt)**
-- **[Anthropic's Claude](https://www.anthropic.com/claude)**
-- **[xAI's Grok](https://x.ai/)**
-- **[Groq's Groq](https://groq.com/)**
-- **[Ollama](https://ollama.com)**
+- [**Anthropic's Claude**](https://www.anthropic.com/claude)
+- [**Google's Gemini**](https://ai.google.dev/gemini-api)
+- [**Groq's Groq**](https://groq.com/)
+- [**Ollama**](https://ollama.com)
+- [**OpenAI's GPT**](https://openai.com/gpt)
+- [**xAI's Grok**](https://x.ai/)
 
-If you'd like 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.
@@ -140,7 +141,7 @@ response = gpt_4o_mini.generate_text(
 Harnessing the power of Python, you can easily create your own plugins to add additional functionality to your conversations:
 
 ```python
-class SimpleMemoryPlugin:
+class SimpleMemoryPlugin(sm.BasePlugin):
     def __init__(self):
         self.memories = [
             "the earth has fictionally beeen destroyed.",

examples/contextual_memory.py

@@ -1,14 +1,14 @@
-from _context import sm
-
-from pydantic import BaseModel
-import openai
-import faiss
-import numpy as np
 import os
 import pickle
 
+import faiss
+import numpy as np
+import openai
+from _context import sm
+from pydantic import BaseModel
 
-class ContextualMemoryPlugin:
+
+class ContextualMemoryPlugin(sm.BasePlugin):
     def __init__(
         self,
         api_key: str,

examples/generate_data.py

@@ -1,8 +1,7 @@
-from typing import List, Iterator
-
-from pydantic import BaseModel
+from typing import Iterator, List
 
 from _context import sm
+from pydantic import BaseModel
 
 
 class Movie(BaseModel):
@@ -25,7 +24,7 @@ class QuotesList(BaseModel):
     quotes: List[MovieQuote]
 
 
-def gen_quotes(n=10) -> Iterator[MovieQuote]:
+def gen_quotes(n: int = 10) -> Iterator[MovieQuote]:
     """Generate a list of quotes from famous movies."""
 
     for q in sm.generate_data(

examples/math_plugin.py

@@ -1,9 +1,11 @@
 from _context import sm
 
 
-class MathPlugin:
+class MathPlugin(sm.BasePlugin):
     def send_hook(self, conversation: sm.Conversation):
         last_user_message = conversation.get_last_message(role="user")
+        if last_user_message is None:
+            return
         if "calculate" in last_user_message.text.lower():
             expression = last_user_message.text.lower().replace("calculate", "").strip()
             try:
@@ -14,7 +16,7 @@ class MathPlugin:
             except Exception:
                 conversation.add_message(
                     role="assistant",
-                    text="I'm sorry, I couldn't compute that expression.",
+                    text="I'm sorry, I couldn't compute that expression. Please try again.",
                 )
 
 

examples/sentiment_analysis.py

@@ -1,8 +1,8 @@
-from _context import sm
-
-from pydantic import BaseModel
 from typing import Literal
 
+from _context import sm
+from pydantic import BaseModel
+
 
 class SentimentAnalysis(BaseModel):
     sentiment: Literal["positive", "negative", "neutral"]

examples/simple_memory.py

@@ -1,7 +1,7 @@
 from _context import sm
 
 
-class SimpleMemoryPlugin(sm.BasePlugin):
+class SimpleMemoryPlugin:
     def __init__(self):
         self.memories = [
             "the earth has fictionally beeen destroyed.",

examples/two_llms_talking.py

@@ -1,6 +1,7 @@
-import simplemind as sm
 import time
 
+import simplemind as sm
+
 
 class ConversationPlugin(sm.BasePlugin):
     def post_send_hook(self, conversation, response):
@@ -8,7 +9,7 @@ class ConversationPlugin(sm.BasePlugin):
         print(f"{conversation.llm_model}:\n{response.text.strip()}\n\n------------\n")
 
 
-def have_conversation(rounds=3):
+def have_conversation(rounds: int = 3):
     # Create two conversations - one for each AI
     with (
         sm.create_conversation(

pyproject.toml

@@ -3,8 +3,8 @@ name = "simplemind"
 version = "0.1.4"
 description = "An experimental client for AI providers that intends to replace LangChain and LangGraph for most common use cases."
 readme = "README.md"
-requires-python = ">=3.11"
-dependencies = ["pydantic", "pydantic-settings", "instructor", "openai", "anthropic", "ollama", "groq"]
+requires-python = ">=3.10"
+dependencies = ["pydantic", "pydantic-settings", "instructor", "openai", "anthropic", "ollama", "groq", "google-generativeai"]
 
 [build-system]
 requires = ["hatchling"]

simplemind/__init__.py

@@ -1,8 +1,8 @@
-from typing import List, Optional, Type
+from typing import List, Type
 
-from .models import Conversation, BasePlugin, BaseModel
-from .utils import find_provider
+from .models import BaseModel, BasePlugin, Conversation
 from .settings import settings
+from .utils import find_provider
 
 
 class Session:
@@ -56,9 +56,9 @@ class Session:
 
 def create_conversation(
     *,
-    llm_model=None,
-    llm_provider=None,
-    plugins: Optional[List[BasePlugin]] = None,
+    llm_model: str | None = None,
+    llm_provider: str | None = None,
+    plugins: List[BasePlugin] | None = None,
     **kwargs,
 ) -> Conversation:
     """Create a new conversation."""
@@ -77,7 +77,12 @@ def create_conversation(
 
 
 def generate_data(
-    prompt, *, llm_model=None, llm_provider=None, response_model=None, **kwargs
+    prompt: str,
+    *,
+    llm_model: str | None = None,
+    llm_provider: str | None = None,
+    response_model: Type[BaseModel],
+    **kwargs,
 ) -> BaseModel:
     """Generate structured data from a given prompt."""
 
@@ -92,7 +97,13 @@ def generate_data(
     )
 
 
-def generate_text(prompt, *, llm_model=None, llm_provider=None, **kwargs) -> str:
+def generate_text(
+    prompt: str,
+    *,
+    llm_model: str | None = None,
+    llm_provider: str | None = None,
+    **kwargs,
+) -> str:
     """Generate text from a given prompt."""
 
     # Find the provider.

simplemind/models.py

@@ -1,18 +1,18 @@
+from types import TracebackType
 import uuid
-from abc import ABC, abstractmethod
 from datetime import datetime
 from typing import Any, Dict, List, Literal, Optional
 
-
 from pydantic import BaseModel, Field
 
-from .utils import find_provider
-
+from .providers import find_provider
 
 MESSAGE_ROLE = Literal["system", "user", "assistant"]
 
 
 class SMBaseModel(BaseModel):
+    """The base SimpleMind model class."""
+
     date_created: datetime = Field(default_factory=datetime.now)
 
     def __str__(self):
@@ -22,34 +22,36 @@ class SMBaseModel(BaseModel):
         return str(self)
 
 
-class BasePlugin:
+class BasePlugin(SMBaseModel):
     """The base conversation plugin class."""
 
     # Plugin metadata.
     meta: Dict[str, Any] = {}
 
-    def initialize_hook(self, conversation: "Conversation"):
+    def initialize_hook(self, conversation: "Conversation") -> Any:
         """Initialize a hook for the plugin."""
         raise NotImplementedError
 
-    def cleanup_hook(self, conversation: "Conversation"):
+    def cleanup_hook(self, conversation: "Conversation") -> Any:
         """Cleanup a hook for the plugin."""
         raise NotImplementedError
 
-    def add_message_hook(self, conversation: "Conversation", message: "Message"):
+    def add_message_hook(self, conversation: "Conversation", message: "Message") -> Any:
         """Add a message hook for the plugin."""
         raise NotImplementedError
 
-    def pre_send_hook(self, conversation: "Conversation"):
+    def pre_send_hook(self, conversation: "Conversation") -> Any:
         """Pre-send hook for the plugin."""
         raise NotImplementedError
 
-    def post_send_hook(self, conversation: "Conversation", response: "Message"):
+    def post_send_hook(self, conversation: "Conversation", response: "Message") -> Any:
         """Post-send hook for the plugin."""
         raise NotImplementedError
 
 
 class Message(SMBaseModel):
+    """A message in a conversation."""
+
     role: MESSAGE_ROLE
     text: str
     meta: Dict[str, Any] = {}
@@ -61,7 +63,16 @@ class Message(SMBaseModel):
         return f"<Message role={self.role} text={self.text!r}>"
 
     @classmethod
-    def from_raw_response(cls, *, text: str, raw):
+    def from_raw_response(cls, *, text: str, raw: Any) -> "Message":
+        """Create a Message instance from a raw response.
+
+        Args:
+            text (str): The message text.
+            raw (Any): The raw response data.
+
+        Returns:
+            Message: A new Message instance.
+        """
         self = cls()
         self.text = text
         self.raw = raw
@@ -69,11 +80,13 @@ class Message(SMBaseModel):
 
 
 class Conversation(SMBaseModel):
+    """A conversation between a user and an assistant."""
+
     id: str = Field(default_factory=lambda: str(uuid.uuid4()))
     messages: List[Message] = []
     llm_model: Optional[str] = None
     llm_provider: Optional[str] = None
-    plugins: List[Any] = []
+    plugins: List[BasePlugin] = []
 
     def __str__(self):
         return f"<Conversation id={self.id!r}>"
@@ -89,8 +102,13 @@ class Conversation(SMBaseModel):
 
         return self
 
-    def __exit__(self, exc_type, exc_value, traceback):
-        # Execute all cleanup hooks.
+    def __exit__(
+        self,
+        exc_type: type[BaseException],
+        exc_value: BaseException,
+        traceback: TracebackType,
+    ) -> None:
+        """Execute all cleanup hooks."""
         for plugin in self.plugins:
             if hasattr(plugin, "cleanup_hook"):
                 try:
@@ -99,7 +117,7 @@ class Conversation(SMBaseModel):
                     pass
 
     def prepend_system_message(
-        self, role: str, text: str, meta: Optional[Dict[str, Any]] = None
+        self, role: MESSAGE_ROLE, text: str, meta: Dict[str, Any] | None = None
     ):
         """Prepend a system message to the conversation."""
         self.messages = [Message(role=role, text=text, meta=meta or {})] + self.messages
@@ -127,7 +145,9 @@ class Conversation(SMBaseModel):
         self.messages.append(Message(role=role, text=text, meta=meta))
 
     def send(
-        self, llm_model: Optional[str] = None, llm_provider: Optional[str] = None
+        self,
+        llm_model: str | None = None,
+        llm_provider: str | None = None,
     ) -> Message:
         """Send the conversation to the LLM."""
 
@@ -156,10 +176,10 @@ class Conversation(SMBaseModel):
 
         return response
 
-    def get_last_message(self, role: MESSAGE_ROLE) -> Optional[Message]:
+    def get_last_message(self, role: MESSAGE_ROLE) -> Message | None:
         """Get the last message with the given role."""
         return next((m for m in reversed(self.messages) if m.role == role), None)
 
-    def add_plugin(self, plugin: Any):
+    def add_plugin(self, plugin: BasePlugin) -> None:
         """Add a plugin to the conversation."""
         self.plugins.append(plugin)

simplemind/providers/__init__.py

@@ -2,9 +2,10 @@ from typing import List, Type
 
 from ._base import BaseProvider
 from .anthropic import Anthropic
+from .gemini import Gemini
 from .groq import Groq
-from .openai import OpenAI
 from .ollama import Ollama
+from .openai import OpenAI
 from .xai import XAI
 
-providers: List[Type[BaseProvider]] = [Anthropic, Groq, OpenAI, Ollama, XAI]
+providers: List[Type[BaseProvider]] = [Anthropic, Gemini, Groq, OpenAI, Ollama, XAI]

simplemind/providers/_base.py

@@ -1,6 +1,11 @@
 from abc import ABC, abstractmethod
+from functools import cached_property
+from typing import Any, Type, TypeVar
 
 from instructor import Instructor
+from pydantic import BaseModel
+
+T = TypeVar("T", bound=BaseModel)
 
 
 class BaseProvider(ABC):
@@ -9,13 +14,13 @@ class BaseProvider(ABC):
     NAME: str
     DEFAULT_MODEL: str
 
-    @property
+    @cached_property
     @abstractmethod
-    def client(self):
+    def client(self) -> Any:
         """The instructor client for the provider."""
         raise NotImplementedError
 
-    @property
+    @cached_property
     @abstractmethod
     def structured_client(self) -> Instructor:
         """The structured client for the provider."""
@@ -27,7 +32,7 @@ class BaseProvider(ABC):
         raise NotImplementedError
 
     @abstractmethod
-    def structured_response(self, prompt: str, response_model, **kwargs):
+    def structured_response(self, prompt: str, response_model: Type[T], **kwargs) -> T:
         """Get a structured response."""
         raise NotImplementedError
 

simplemind/providers/anthropic.py

@@ -1,10 +1,15 @@
-from typing import Union
+from functools import cached_property
+from typing import Type, TypeVar
 
 import anthropic
 import instructor
+from pydantic import BaseModel
 
-from ._base import BaseProvider
 from ..settings import settings
+from ._base import BaseProvider
+
+T = TypeVar("T", bound=BaseModel)
+
 
 PROVIDER_NAME = "anthropic"
 DEFAULT_MODEL = "claude-3-5-sonnet-20241022"
@@ -15,17 +20,17 @@ class Anthropic(BaseProvider):
     NAME = PROVIDER_NAME
     DEFAULT_MODEL = DEFAULT_MODEL
 
-    def __init__(self, api_key: Union[str, None] = None):
+    def __init__(self, api_key: str | None = None):
         self.api_key = api_key or settings.get_api_key(PROVIDER_NAME)
 
-    @property
+    @cached_property
     def client(self):
         """The raw Anthropic client."""
         if not self.api_key:
             raise ValueError("Anthropic API key is required")
         return anthropic.Anthropic(api_key=self.api_key)
 
-    @property
+    @cached_property
     def structured_client(self):
         """A client patched with Instructor."""
         return instructor.from_anthropic(self.client)
@@ -57,13 +62,13 @@ class Anthropic(BaseProvider):
             llm_provider=PROVIDER_NAME,
         )
 
-    def structured_response(self, model, response_model, **kwargs):
+    def structured_response(self, model: str, response_model: Type[T], **kwargs) -> T:
         response = self.structured_client.messages.create(
-            model=model, response_model=response_model or self.DEFAULT_MODEL, **kwargs
+            model=model or self.DEFAULT_MODEL, response_model=response_model, **kwargs
         )
         return response
 
-    def generate_text(self, prompt, *, llm_model, **kwargs):
+    def generate_text(self, prompt: str, *, llm_model: str, **kwargs):
         messages = [
             {"role": "user", "content": prompt},
         ]

simplemind/providers/gemini.py

@@ -0,0 +1,94 @@
+from functools import cached_property
+from typing import Type, TypeVar
+
+import google.generativeai as genai
+import instructor
+from pydantic import BaseModel
+
+from ..models import Conversation, Message
+from ..settings import settings
+from ._base import BaseProvider
+
+PROVIDER_NAME = "gemini"
+DEFAULT_MODEL = "models/gemini-1.5-flash-latest"
+
+T = TypeVar("T", bound=BaseModel)
+
+
+class Gemini(BaseProvider):
+    NAME = PROVIDER_NAME
+    DEFAULT_MODEL = DEFAULT_MODEL
+
+    def __init__(self, api_key: str | None = None):
+        self.api_key = api_key or settings.get_api_key(PROVIDER_NAME)
+        self.model_name = DEFAULT_MODEL
+
+    @cached_property
+    def client(self, model_name: str = DEFAULT_MODEL):
+        """The raw Gemini client."""
+        if not self.api_key:
+            raise ValueError("Gemini API key is required")
+        self.model_name = model_name
+        return genai.GenerativeModel(model_name=self.model_name)
+
+    @cached_property
+    def structured_client(self):
+        """A Gemini client patched with Instructor."""
+        return instructor.from_gemini(self.client)
+
+    def send_conversation(self, conversation: "Conversation") -> "Message":
+        """Send a conversation to the Gemini API."""
+
+        messages = [
+            {
+                "role": msg.role,
+                "content": msg.text,
+                "metadata": msg.meta or {},
+            }
+            for msg in conversation.messages
+        ]
+
+        try:
+            response = self.structured_client.chat.completions.create(
+                messages=messages, response_model=None
+            )
+        except Exception as e:
+            # Handle the exception appropriately, e.g., log the error or raise a custom exception
+            raise RuntimeError(f"Failed to send conversation to Gemini API: {e}") from e
+
+        # Create and return a properly formatted Message instance
+        return Message(
+            role="assistant",
+            text=str(response),
+            raw=response,
+            llm_model=self.model_name,
+            llm_provider=PROVIDER_NAME,
+        )
+
+    def structured_response(self, prompt: str, response_model: Type[T], **kwargs) -> T:
+        """Send a structured response to the Gemini API."""
+        try:
+            response = self.structured_client.chat.completions.create(
+                messages=[{"role": "user", "content": prompt}],
+                response_model=response_model,
+                **kwargs,
+            )
+        except Exception as e:
+            # Handle the exception appropriately, e.g., log the error or raise a custom exception
+            raise RuntimeError(
+                f"Failed to send structured response to Gemini API: {e}"
+            ) from e
+        return response
+
+    def generate_text(self, prompt: str, **kwargs) -> str:
+        """Generate text using the Gemini API."""
+        try:
+            response = self.structured_client.chat.completions.create(
+                messages=[{"role": "user", "content": prompt}],
+                response_model=None,
+                **kwargs,
+            )
+        except Exception as e:
+            # Handle the exception appropriately, e.g., log the error or raise a custom exception
+            raise RuntimeError(f"Failed to generate text with Gemini API: {e}") from e
+        return str(response)

simplemind/providers/groq.py

@@ -1,30 +1,34 @@
-from typing import Union
+from functools import cached_property
+from typing import Type, TypeVar
 
 import groq
 import instructor
+from pydantic import BaseModel
 
-from ._base import BaseProvider
 from ..settings import settings
+from ._base import BaseProvider
 
 PROVIDER_NAME = "groq"
 DEFAULT_MODEL = "llama3-8b-8192"
 
+T = TypeVar("T", bound=BaseModel)
+
 
 class Groq(BaseProvider):
     NAME = PROVIDER_NAME
     DEFAULT_MODEL = DEFAULT_MODEL
 
-    def __init__(self, api_key: Union[str, None] = None):
+    def __init__(self, api_key: str | None = None):
         self.api_key = api_key or settings.get_api_key(PROVIDER_NAME)
 
-    @property
+    @cached_property
     def client(self):
         """The raw Groq client."""
         if not self.api_key:
             raise ValueError("Groq API key is required")
         return groq.Groq(api_key=self.api_key)
 
-    @property
+    @cached_property
     def structured_client(self):
         """A client patched with Instructor."""
         return instructor.from_groq(self.client)
@@ -59,7 +63,7 @@ class Groq(BaseProvider):
             llm_provider=PROVIDER_NAME,
         )
 
-    def structured_response(self, prompt: str, response_model, **kwargs):
+    def structured_response(self, prompt: str, response_model: Type[T], **kwargs) -> T:
         # Ensure messages are provided in kwargs
         messages = [
             {"role": "user", "content": prompt},

simplemind/providers/ollama.py

@@ -1,9 +1,16 @@
-import ollama as ol
-import instructor
-from openai import OpenAI
+from functools import cached_property
+from typing import Type, TypeVar
+
+import instructor
+import ollama as ol
+from openai import OpenAI
+from pydantic import BaseModel
 
-from ._base import BaseProvider
 from ..settings import settings
+from ._base import BaseProvider
+
+T = TypeVar("T", bound=BaseModel)
+
 
 PROVIDER_NAME = "ollama"
 DEFAULT_MODEL = "llama3.2"
@@ -15,18 +22,18 @@ class Ollama(BaseProvider):
     DEFAULT_MODEL = DEFAULT_MODEL
     TIMEOUT = DEFAULT_TIMEOUT
 
-    def __init__(self, host_url: str = None):
+    def __init__(self, host_url: str | None = None):
         self.host_url = host_url or settings.OLLAMA_HOST_URL
 
-    @property
+    @cached_property
     def client(self):
         """The raw Ollama client."""
         if not self.host_url:
             raise ValueError("No ollama host url provided")
         return ol.Client(timeout=self.TIMEOUT, host=self.host_url)
 
-    @property
-    def structured_client(self):
+    @cached_property
+    def structured_client(self) -> instructor.Instructor:
         """A client patched with Instructor."""
         return instructor.from_openai(
             OpenAI(
@@ -36,7 +43,7 @@ class Ollama(BaseProvider):
             mode=instructor.Mode.JSON,
         )
 
-    def send_conversation(self, conversation: "Conversation"):
+    def send_conversation(self, conversation: "Conversation") -> "Message":
         """Send a conversation to the Ollama API."""
         from ..models import Message
 
@@ -57,7 +64,10 @@ class Ollama(BaseProvider):
             llm_provider=PROVIDER_NAME,
         )
 
-    def structured_response(self, prompt, response_model, *, llm_model: str, **kwargs):
+    def structured_response(
+        self, prompt: str, response_model: Type[T], *, llm_model: str, **kwargs
+    ) -> T:
+        """Get a structured response from the Ollama API."""
         messages = [
             {"role": "user", "content": prompt},
         ]
@@ -70,7 +80,8 @@ class Ollama(BaseProvider):
         )
         return response
 
-    def generate_text(self, prompt, *, llm_model):
+    def generate_text(self, prompt: str, *, llm_model: str) -> str:
+        """Generate text using the Ollama API."""
         messages = [
             {"role": "user", "content": prompt},
         ]
@@ -79,4 +90,4 @@ class Ollama(BaseProvider):
             messages=messages, model=llm_model or self.DEFAULT_MODEL
         )
 
-        return response.get("message").get("content")
+        return response.get("message", {}).get("content", "")

simplemind/providers/openai.py

@@ -1,10 +1,14 @@
-from typing import Union
+from functools import cached_property
+from typing import Type, TypeVar
 
 import instructor
 import openai as oa
+from pydantic import BaseModel
 
-from ._base import BaseProvider
 from ..settings import settings
+from ._base import BaseProvider
+
+T = TypeVar("T", bound=BaseModel)
 
 PROVIDER_NAME = "openai"
 DEFAULT_MODEL = "gpt-4o-mini"
@@ -14,17 +18,17 @@ class OpenAI(BaseProvider):
     NAME = PROVIDER_NAME
     DEFAULT_MODEL = DEFAULT_MODEL
 
-    def __init__(self, api_key: Union[str, None] = None):
+    def __init__(self, api_key: str | None = None):
         self.api_key = api_key or settings.get_api_key(PROVIDER_NAME)
 
-    @property
+    @cached_property
     def client(self):
         """The raw OpenAI client."""
         if not self.api_key:
             raise ValueError("OpenAI API key is required")
         return oa.OpenAI(api_key=self.api_key)
 
-    @property
+    @cached_property
     def structured_client(self):
         """A OpenAI client with Instructor."""
         return instructor.from_openai(self.client)
@@ -53,12 +57,19 @@ class OpenAI(BaseProvider):
             llm_provider=PROVIDER_NAME,
         )
 
-    def structured_response(self, prompt, response_model, *, llm_model: str, **kwargs):
+    def structured_response(
+        self,
+        prompt: str,
+        response_model: Type[T],
+        *,
+        llm_model: str | None = None,
+        **kwargs,
+    ) -> T:
+        """Get a structured response from the OpenAI API."""
         # Ensure messages are provided in kwargs
         messages = [
             {"role": "user", "content": prompt},
         ]
-
         response = self.structured_client.chat.completions.create(
             messages=messages,
             model=llm_model or self.DEFAULT_MODEL,
@@ -67,13 +78,12 @@ class OpenAI(BaseProvider):
         )
         return response
 
-    def generate_text(self, prompt, *, llm_model, **kwargs):
+    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
         )
-
         return response.choices[0].message.content

simplemind/providers/xai.py

@@ -1,10 +1,10 @@
-from typing import Union
+from functools import cached_property
 
 import instructor
 import openai as oa
 
-from ._base import BaseProvider
 from ..settings import settings
+from ._base import BaseProvider
 
 PROVIDER_NAME = "xai"
 DEFAULT_MODEL = "grok-beta"
@@ -16,10 +16,10 @@ class XAI(BaseProvider):
     NAME = PROVIDER_NAME
     DEFAULT_MODEL = DEFAULT_MODEL
 
-    def __init__(self, api_key: Union[str, None] = None):
+    def __init__(self, api_key: str | None = None):
         self.api_key = api_key or settings.get_api_key(PROVIDER_NAME)
 
-    @property
+    @cached_property
     def client(self):
         """The raw OpenAI client."""
         if not self.api_key:
@@ -29,7 +29,7 @@ class XAI(BaseProvider):
             base_url=BASE_URL,
         )
 
-    @property
+    @cached_property
     def structured_client(self):
         """A client patched with Instructor."""
         return instructor.from_openai(self.client)
@@ -60,10 +60,10 @@ class XAI(BaseProvider):
             llm_provider=PROVIDER_NAME,
         )
 
-    def structured_response(self, prompt: str, response_model, *, llm_model):
+    def structured_response(self, prompt: str, response_model, *, llm_model: str):
         raise NotImplementedError("XAI does not support structured responses")
 
-    def generate_text(self, prompt, *, llm_model, **kwargs):
+    def generate_text(self, prompt: str, *, llm_model: str, **kwargs):
         messages = [
             {"role": "user", "content": prompt},
         ]

simplemind/settings.py

@@ -1,8 +1,19 @@
-from typing import Optional, Union
+from typing import Literal, 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")
+
+    model_config = SettingsConfigDict(extra="forbid")
+
 
 class Settings(BaseSettings):
     """The class that holds all the API keys for the application."""
@@ -11,6 +22,7 @@ class Settings(BaseSettings):
         None, description="API key for Anthropic"
     )
     GROQ_API_KEY: Optional[SecretStr] = Field(None, description="API key for Groq")
+    GEMINI_API_KEY: Optional[SecretStr] = Field(None, description="API key for Gemini")
     OPENAI_API_KEY: Optional[SecretStr] = Field(None, description="API key for OpenAI")
     OLLAMA_HOST_URL: Optional[str] = Field(
         "http://127.0.0.1:11434", description="Fully qualified host URL for Ollama"
@@ -22,6 +34,7 @@ class Settings(BaseSettings):
     model_config = SettingsConfigDict(
         env_file=".env", env_file_encoding="utf-8", case_sensitive=True, extra="ignore"
     )
+    logging: LoggingConfig = LoggingConfig()
 
     @field_validator("*", mode="before")
     @classmethod

simplemind/utils.py

@@ -1,24 +1,26 @@
 import difflib
-from typing import Optional, Type
 
-from .providers import providers, BaseProvider
+from .providers import BaseProvider, providers
 
 _PROVIDER_NAMES = [provider.NAME.lower() for provider in providers]
 
 
-def find_provider(provider_name: str) -> BaseProvider:
+def find_provider(provider_name: str | None) -> BaseProvider:
     """
     Find and instantiate a provider by name.
 
     Parameters:
-    provider_name (Union[str, None]): The name of the provider to find.
+        provider_name (Union[str, None]): The name of the provider to find.
 
     Returns:
-    An instance of the provider class if found.
+        An instance of the provider class if found.
 
     Raises:
-    ValueError: If the provider is not found, with a suggestion for the closest match.
+        ValueError: If the provider is not specified or is not found, with a suggestion for the closest match.
     """
+    if provider_name is None:
+        raise ValueError("No provider specified.")
+
     # Find the provider by name.
     for provider_class in providers:
         if provider_class.NAME.lower() == provider_name.lower():
@@ -29,10 +31,8 @@ def find_provider(provider_name: str) -> BaseProvider:
     provider_found = difflib.get_close_matches(
         provider_name.lower(), _PROVIDER_NAMES, n=1
     )
-
     if provider_found:
         raise ValueError(
             f"Provider {provider_name!r} not found. Did you mean {provider_found[0]!r}?"
         )
-    else:
-        raise ValueError(f"Provider {provider_name} not found.")
+    raise ValueError(f"Provider {provider_name} not found.")