ai-proxy-core 0.3.2

Minimal, reusable AI service handlers for Gemini and other LLMs

AI Proxy Core

A unified Python package providing a single interface for AI completions across multiple providers (OpenAI, Gemini, Ollama). Features intelligent model management, automatic provider routing, and zero-config setup.

💡 Why not LangChain? Read our philosophy and architectural rationale for choosing simplicity over complexity.

🎯 What's Next? See our wrapper layer roadmap for planned features and what belongs in a clean LLM wrapper.

Installation

Basic (Google Gemini only):

pip install ai-proxy-core

With specific providers (optional dependencies):

pip install ai-proxy-core[openai]     # OpenAI support
pip install ai-proxy-core[anthropic]  # Anthropic support (coming soon)
pip install ai-proxy-core[telemetry]  # OpenTelemetry support
pip install ai-proxy-core[all]        # Everything

Or install from source:

git clone https://github.com/ebowwa/ai-proxy-core.git
cd ai-proxy-core
pip install -e .
# With all extras: pip install -e ".[all]"

Quick Start

🤖 AI Integration Help: Copy our expert agent prompt to any LLM (ChatGPT, Claude, etc.) for instant integration guidance and code examples tailored to your project.

Unified Interface (Recommended)

from ai_proxy_core import CompletionClient

# Single client for all providers
client = CompletionClient()

# Works with any model - auto-detects provider
response = await client.create_completion(
    messages=[{"role": "user", "content": "Hello!"}],
    model="gpt-4"  # Auto-routes to OpenAI
)

response = await client.create_completion(
    messages=[{"role": "user", "content": "Hello!"}],
    model="gemini-1.5-flash"  # Auto-routes to Gemini
)

response = await client.create_completion(
    messages=[{"role": "user", "content": "Hello!"}],
    model="llama2"  # Auto-routes to Ollama
)

# All return the same standardized format
print(response["choices"][0]["message"]["content"])

Intelligent Model Selection

# Find the best model for your needs
best_model = await client.find_best_model({
    "multimodal": True,
    "min_context_limit": 32000,
    "local_preferred": False
})

response = await client.create_completion(
    messages=[{"role": "user", "content": "Describe this image"}],
    model=best_model["id"]
)

Model Discovery

# List all available models across providers
models = await client.list_models()
for model in models:
    print(f"{model['id']} ({model['provider']}) - {model['context_limit']:,} tokens")

# List models from specific provider
openai_models = await client.list_models(provider="openai")

Ollama Integration

Prerequisites

# Install Ollama from https://ollama.ai
# Start Ollama service
ollama serve

# Pull a model
ollama pull llama3.2

Using Ollama with CompletionClient

from ai_proxy_core import CompletionClient, ModelManager

# Option 1: Auto-detection (Ollama will be detected if running)
client = CompletionClient()

# Option 2: With custom ModelManager
manager = ModelManager()
client = CompletionClient(model_manager=manager)

# List Ollama models
models = await client.list_models(provider="ollama")
print(f"Available Ollama models: {[m['id'] for m in models]}")

# Create completion
response = await client.create_completion(
    messages=[{"role": "user", "content": "Hello!"}],
    model="llama3.2",
    provider="ollama",  # Optional, auto-detected from model name
    temperature=0.7
)

Direct Ollama Usage

from ai_proxy_core import OllamaCompletions

ollama = OllamaCompletions()

# List available models
models = ollama.list_models()
print(f"Available models: {models}")

# Create completion
response = await ollama.create_completion(
    messages=[{"role": "user", "content": "Explain quantum computing"}],
    model="llama3.2",
    temperature=0.7,
    max_tokens=500
)

See examples/ollama_complete_guide.py for comprehensive examples including error handling, streaming, and advanced features.

Advanced Usage

Provider-Specific Completions

If you need provider-specific features, you can still use the individual clients:

from ai_proxy_core import GoogleCompletions, OpenAICompletions, OllamaCompletions

# Google Gemini with safety settings
google = GoogleCompletions(api_key="your-gemini-api-key")
response = await google.create_completion(
    messages=[{"role": "user", "content": "Hello!"}],
    model="gemini-1.5-flash",
    safety_settings=[{"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"}]
)

# OpenAI with tool calling
openai = OpenAICompletions(api_key="your-openai-key")
response = await openai.create_completion(
    messages=[{"role": "user", "content": "What's the weather?"}],
    model="gpt-4",
    tools=[{"type": "function", "function": {"name": "get_weather"}}]
)

# Ollama for local models
ollama = OllamaCompletions()  # Auto-detects localhost:11434
response = await ollama.create_completion(
    messages=[{"role": "user", "content": "Hello!"}],
    model="llama3.2",
    temperature=0.7
)

OpenAI-Compatible Endpoints

# Works with any OpenAI-compatible API (Groq, Anyscale, Together, etc.)
groq = OpenAICompletions(
    api_key="your-groq-key",
    base_url="https://api.groq.com/openai/v1"
)

response = await groq.create_completion(
    messages=[{"role": "user", "content": "Hello!"}],
    model="mixtral-8x7b-32768"
)

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 CompletionClient

app = FastAPI()
client = CompletionClient()

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 client.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

🚀 Unified Interface

• Single client for all providers - No more provider-specific code
• Automatic provider routing - Detects provider from model name
• Intelligent model selection - Find best model based on requirements
• Zero-config setup - Auto-detects available providers from environment

🧠 Model Management

• Cross-provider model discovery - List models from OpenAI, Gemini, Ollama
• Rich model metadata - Context limits, capabilities, multimodal support
• Automatic model provisioning - Downloads Ollama models as needed
• Model compatibility checking - Ensures models support requested features

🔧 Developer Experience

• No framework dependencies - Use with FastAPI, Flask, or any Python app
• Async/await support - Modern async Python
• Type hints - Full type annotations
• Easy testing - Mock the unified client in your tests
• Backward compatible - All existing provider-specific code continues to work

🎯 Advanced Features

• WebSocket support - Real-time audio/text streaming with Gemini Live
• Built-in tools - Code execution and Google Search with simple flags
• Custom functions - Add your own function declarations
• Optional telemetry - OpenTelemetry integration for production monitoring
• Provider-specific optimizations - Access advanced features when needed

Telemetry

Basic observability with OpenTelemetry (optional):

# Install with: pip install "ai-proxy-core[telemetry]"

# Enable telemetry via environment variables
export OTEL_ENABLED=true
export OTEL_EXPORTER_TYPE=console  # or "otlp" for production
export OTEL_ENDPOINT=localhost:4317  # for OTLP exporter

# Automatic telemetry for:
# - Request counts by model/status
# - Request latency tracking
# - Session duration for WebSockets
# - Error tracking with types

The telemetry is completely optional and has zero overhead when disabled.

Project Structure

📝 Note: Full documentation of the project structure is being tracked in Issue #12

This project serves dual purposes:

• Python Library (/ai_proxy_core): Installable via pip for use in Python applications
• Web Service (/api): FastAPI endpoints for REST API access

Development

Releasing New Versions

We provide an automated release script that handles version bumping, building, and publishing:

# Make the script executable (first time only)
chmod +x release.sh

# Release a new version
./release.sh 0.1.9

The script will:

1. Show current version and validate the new version format
2. Prompt for a release description (for CHANGELOG)
3. Update version in all necessary files (pyproject.toml, setup.py, init.py)
4. Update CHANGELOG.md with your description
5. Build the package
6. Upload to PyPI
7. Commit changes and create a git tag
8. Push to GitHub with the new tag

Manual Build Process

If you prefer to build manually:

python setup.py sdist bdist_wheel
twine upload dist/*

License

MIT