simplified-genai

A streamlined, unified Generative AI abstraction layer for Python supporting Google Gemini, OpenAI GPT, and xAI Grok.

Features

• Unified Provider Interface (IGenAiProvider): Switch seamlessly between Gemini, GPT, and Grok without rewriting code.
• Structured Outputs: Direct type-safe deserialization into Pydantic models with schema enforcement.
• Python Function Calling / Tool Use (ToolDefinition): Automatic JSON schema generation from standard Python functions and type hints.
• Multimodal File Uploads: Uniform file uploading and handling across multimodal models.
• Decoupled Chat Sessions: IChatSessionStore protocol with a built-in InMemoryChatSessionStore for instant local usage or testing.
• Provider Factory (GenAiProviderFactory): Dynamic primary and fallback provider resolution.

Installation

pip install simplified-genai

Or using uv:

uv add simplified-genai

Quickstart

1. Basic Text Generation

import asyncio
from simplified_genai import GeminiGenAiProvider, GenAiModel

async def main():
    provider = GeminiGenAiProvider(
        api_key="YOUR_GEMINI_API_KEY",
        supported_models=[GenAiModel.GEMINI_2_5_FLASH],
        default_model=GenAiModel.GEMINI_2_5_FLASH,
    )

    response = await provider.get_raw_text_response(
        user_prompt="Explain quantum computing in one sentence."
    )
    print(response)

asyncio.run(main())

2. Structured Output with Pydantic

import asyncio
from pydantic import BaseModel
from simplified_genai import GptGenAiProvider, GenAiModel

class CapitalCity(BaseModel):
    country: str
    capital: str
    population_millions: float

async def main():
    provider = GptGenAiProvider(
        api_key="YOUR_OPENAI_API_KEY",
        supported_models=[GenAiModel.GPT_5_NANO],
        default_model=GenAiModel.GPT_5_NANO,
    )

    result = await provider.get_structured_response(
        schema=CapitalCity,
        user_prompt="What is the capital of France?",
    )
    print(f"{result.capital}, {result.country} (Pop: {result.population_millions}M)")

asyncio.run(main())

3. Tool Calling / Function Calling

import asyncio
from simplified_genai import GeminiGenAiProvider, GenAiModel, ToolDefinition

def get_weather(location: str) -> str:
    """Get the current weather for a given location."""
    return f"Weather in {location} is 22°C and sunny."

weather_tool = ToolDefinition(
    function=get_weather,
    description="Get current weather in a city"
)

async def main():
    provider = GeminiGenAiProvider(
        api_key="YOUR_GEMINI_API_KEY",
        supported_models=[GenAiModel.GEMINI_2_5_FLASH],
        default_model=GenAiModel.GEMINI_2_5_FLASH,
    )

    response = await provider.get_raw_text_response(
        user_prompt="What's the weather in Tokyo?",
        tools=[weather_tool],
    )
    print(response)

asyncio.run(main())

4. Chat Session Management

import asyncio
from simplified_genai import (
    ChatSessionService,
    InMemoryChatSessionStore,
    GenAiChatMessage,
    GenAiChatMessageRole,
)

async def main():
    # Use the built-in in-memory store, or implement IChatSessionStore for Redis/Postgres/Mongo
    store = InMemoryChatSessionStore()
    chat_service = ChatSessionService(store=store)

    session_id = chat_service.start_session()

    await chat_service.append_turn(
        session_id=session_id,
        user_message=GenAiChatMessage(role=GenAiChatMessageRole.USER, content="Hello!"),
        assistant_message=GenAiChatMessage(role=GenAiChatMessageRole.ASSISTANT, content="Hi there! How can I help?"),
    )

    history = await chat_service.get_history(session_id)
    for msg in history:
        print(f"[{msg.role}]: {msg.content}")

asyncio.run(main())

License

This project is licensed under the MIT License.