ai-proxy-core 0.1.8

Minimal, reusable AI service handlers for Gemini and other LLMs

AI Proxy Core

A minimal Python package providing reusable AI service handlers for Gemini and other LLMs. No web framework dependencies - just the core logic.

Installation

pip install ai-proxy-core

Or install from source:

git clone https://github.com/ebowwa/ai-proxy-core.git
cd ai-proxy-core
pip install -e .

Usage

Completions Handler

from ai_proxy_core import CompletionsHandler

# Initialize handler
handler = CompletionsHandler(api_key="your-gemini-api-key")

# Create completion
response = await handler.create_completion(
    messages=[
        {"role": "user", "content": "Hello, how are you?"}
    ],
    model="gemini-1.5-flash",
    temperature=0.7
)

print(response["choices"][0]["message"]["content"])

Gemini Live Session

from ai_proxy_core import GeminiLiveSession

# Example 1: Basic session (no system prompt)
session = GeminiLiveSession(api_key="your-gemini-api-key")

# Example 2: Session with system prompt (simple string format)
session = GeminiLiveSession(
    api_key="your-gemini-api-key",
    system_instruction="You are a helpful voice assistant. Be concise and friendly."
)

# Example 3: Session with built-in tools enabled
session = GeminiLiveSession(
    api_key="your-gemini-api-key",
    enable_code_execution=True,      # Enable Python code execution
    enable_google_search=True,       # Enable web search
    system_instruction="You are a helpful assistant with access to code execution and web search."
)

# Example 4: Session with custom function declarations
from google.genai import types

def get_weather(location: str) -> dict:
    # Your custom function implementation
    return {"location": location, "temp": 72, "condition": "sunny"}

weather_function = types.FunctionDeclaration(
    name="get_weather",
    description="Get current weather for a location",
    parameters=types.Schema(
        type="OBJECT",
        properties={
            "location": types.Schema(type="STRING", description="City name")
        },
        required=["location"]
    )
)

session = GeminiLiveSession(
    api_key="your-gemini-api-key",
    custom_tools=[types.Tool(function_declarations=[weather_function])],
    system_instruction="You can help with weather information."
)

# Set up callbacks
session.on_audio = lambda data: print(f"Received audio: {len(data)} bytes")
session.on_text = lambda text: print(f"Received text: {text}")
session.on_function_call = lambda call: handle_function_call(call)

async def handle_function_call(call):
    if call["name"] == "get_weather":
        result = get_weather(**call["args"])
        await session.send_function_result(result)

# Start session
await session.start()

# Send audio/text
await session.send_audio(audio_data)
await session.send_text("What's the weather in Boston?")

# Stop when done
await session.stop()

Integration with FastAPI

Chat Completions API

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from ai_proxy_core import CompletionsHandler

app = FastAPI()
handler = CompletionsHandler()

class CompletionRequest(BaseModel):
    messages: list
    model: str = "gemini-1.5-flash"
    temperature: float = 0.7

@app.post("/api/chat/completions")
async def create_completion(request: CompletionRequest):
    try:
        response = await handler.create_completion(
            messages=request.messages,
            model=request.model,
            temperature=request.temperature
        )
        return response
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

WebSocket for Gemini Live

from fastapi import FastAPI, WebSocket, WebSocketDisconnect
from ai_proxy_core import GeminiLiveSession

app = FastAPI()

@app.websocket("/ws/gemini")
async def gemini_websocket(
    websocket: WebSocket,
    enable_code_execution: bool = False,
    enable_google_search: bool = False
):
    await websocket.accept()
    
    # Create session with tools if requested
    session = GeminiLiveSession(
        enable_code_execution=enable_code_execution,
        enable_google_search=enable_google_search
    )
    
    # Set up callbacks to forward to WebSocket
    session.on_audio = lambda data: websocket.send_json({
        "type": "audio", "data": data
    })
    session.on_text = lambda text: websocket.send_json({
        "type": "text", "data": text
    })
    
    await session.start()
    
    try:
        async for message in websocket.iter_json():
            if message["type"] == "audio":
                await session.send_audio(message["data"])
            elif message["type"] == "text":
                await session.send_text(message["data"])
    except WebSocketDisconnect:
        await session.stop()

Features

• No framework dependencies - Use with FastAPI, Flask, or any Python app
• Async/await support - Modern async Python
• Type hints - Full type annotations
• Minimal surface area - Just the core logic you need
• Easy testing - Mock the handlers in your tests
• Built-in tools - Code execution and Google Search with simple flags
• Custom functions - Add your own function declarations
• Reusable design - Tools configured by consumers, not hardcoded
• WebSocket support - Real-time audio/text streaming with Gemini Live
• Callback system - Handle responses with custom callbacks

Development

Building the Package

When building the package for distribution, use setup.py directly instead of python -m build to avoid pip isolation issues:

python setup.py sdist bdist_wheel

This will create both source distribution and wheel files in the dist/ directory.

Publishing to PyPI

twine upload dist/*

License

MIT