uniai 0.1.0

A universal AI interface layer for seamless LLM provider switching

UniAI

A Universal AI Interface Layer for Python

UniAI simplifies working with multiple LLM providers by offering a unified interface with automatic context management and streaming support.

Features

• 🔄 Unified Interface: Single API for multiple LLM providers
• 🔀 Easy Provider Switching: Switch between OpenAI, DeepSeek, and more with one line
• 💬 Automatic Context Management: Built-in conversation history tracking
• 🌊 Streaming Support: Stream responses in real-time
• 🛡️ Type Safety: Full type hints and Pydantic validation
• 🔧 Extensible: Easy to add custom providers

Installation

pip install uniai

Or install from source:

git clone https://github.com/Archie818/uniai.git
cd uniai
pip install -e .

Quick Start

from uniai import UniAI

# Initialize with your preferred provider
bot = UniAI(
    provider="openai",
    api_key="sk-your-api-key",
    model="gpt-4o-mini"
)

# Simple chat
response = bot.chat("Hello, who are you?")
print(response)

# Multi-turn conversation (context is automatically managed)
response = bot.chat("What can you help me with?")
print(response)

response = bot.chat("Tell me more about the first thing you mentioned")
print(response)

Streaming Responses

from uniai import UniAI

bot = UniAI(provider="openai", api_key="sk-...", model="gpt-4o-mini")

# Stream response chunks
for chunk in bot.stream("Tell me a story about a brave knight"):
    print(chunk, end="", flush=True)
print()  # New line at the end

Switching Providers

from uniai import UniAI

# Start with OpenAI
bot = UniAI(provider="openai", api_key="sk-openai-key", model="gpt-4o-mini")
response = bot.chat("Hello!")

# Switch to DeepSeek (preserves conversation history by default)
bot.switch_provider(
    provider="deepseek",
    api_key="sk-deepseek-key",
    model="deepseek-chat"
)
response = bot.chat("Continue our conversation")

# Switch and clear history
bot.switch_provider(
    provider="openai",
    api_key="sk-openai-key",
    model="gpt-4o",
    keep_history=False
)

Supported Providers

Provider	Models	Status
OpenAI	gpt-4o, gpt-4o-mini, gpt-4, gpt-3.5-turbo	✅ Supported
DeepSeek	deepseek-chat, deepseek-coder	✅ Supported
Claude	Coming soon	🚧 Planned

Advanced Usage

System Prompt

bot = UniAI(
    provider="openai",
    api_key="sk-...",
    model="gpt-4o-mini",
    system_prompt="You are a helpful coding assistant. Always provide code examples."
)

Conversation History Management

# Limit history to prevent context overflow
bot = UniAI(
    provider="openai",
    api_key="sk-...",
    model="gpt-4o-mini",
    max_history=20  # Keep only last 20 messages
)

# Get conversation history
history = bot.get_history()
print(history)

# Clear history
bot.clear_history()

# Access memory directly
print(f"Messages in memory: {len(bot.memory)}")

Full Response Object

# Get detailed response with metadata
response = bot.chat_with_response("What is Python?")

print(f"Content: {response.content}")
print(f"Model: {response.model}")
print(f"Tokens used: {response.usage.total_tokens}")
print(f"Finish reason: {response.finish_reason}")

Configuration Options

bot = UniAI(
    provider="openai",
    api_key="sk-...",
    model="gpt-4o-mini",
    base_url=None,           # Custom API endpoint
    system_prompt=None,      # System instructions
    temperature=1.0,         # Randomness (0.0-2.0)
    max_tokens=None,         # Max response length
    max_history=None,        # Message history limit
    timeout=60.0,            # Request timeout (seconds)
    max_retries=3,           # Retry attempts
)

Architecture

uniai/
├── __init__.py          # Package exports
├── client.py            # Main UniAI class
├── exceptions.py        # Custom exceptions
├── core/
│   ├── base.py          # Abstract BaseProvider
│   ├── config.py        # Pydantic config models
│   └── types.py         # Type definitions
├── context/
│   └── memory.py        # Conversation memory
└── providers/
    ├── openai.py        # OpenAI implementation
    └── deepseek.py      # DeepSeek implementation

Adding Custom Providers

from uniai.core.base import BaseProvider
from uniai.providers import register_provider

class MyCustomProvider(BaseProvider):
    name = "custom"

    def _init_client(self):
        # Initialize your client
        pass

    def chat(self, messages):
        # Implement chat
        pass

    def stream_chat(self, messages):
        # Implement streaming
        pass

# Register the provider
register_provider("custom", MyCustomProvider)

# Now you can use it
bot = UniAI(provider="custom", api_key="...", model="...")

Error Handling

from uniai import UniAI
from uniai.exceptions import (
    UniAIError,
    AuthenticationError,
    RateLimitError,
    APIError,
)

bot = UniAI(provider="openai", api_key="sk-...", model="gpt-4o-mini")

try:
    response = bot.chat("Hello!")
except AuthenticationError:
    print("Invalid API key")
except RateLimitError:
    print("Rate limit exceeded, please wait")
except APIError as e:
    print(f"API error: {e}")
except UniAIError as e:
    print(f"UniAI error: {e}")

Roadmap

• v0.1: Core functionality
  • Unified interface
  • OpenAI & DeepSeek support
  • Context management
  • Streaming
• v0.2: Extended provider support
  • Claude/Anthropic
  • Google Gemini
  • Local models (Ollama)
• v0.3: Advanced features
  • Async support
  • Function calling
  • Token counting
• v1.0: Agent framework
  • Agent development
  • Multi-model collaboration
  • Tool integration

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see LICENSE for details.