multi-ai-handler 2.2.0

Unified Python interface for multiple AI providers with support for text, images, and documents.

Multi AI Handler

A unified Python library for interacting with multiple AI providers through a consistent interface. Supports text and file inputs across OpenAI, Anthropic Claude, Google Gemini, OpenRouter, Cerebras and Ollama (local LLMs).

Features

• Unified interface for multiple AI providers
• Conversation history for multi-turn interactions
• Streaming support for real-time token output
• Async support for concurrent workloads
• Support for images and documents (PDF)
• Local LLM support with Ollama
• Advanced document processing with Docling (OCR, table extraction)
• Model information retrieval

Installation

pip install multi-ai-handler

Optional dependencies:

pip install multi-ai-handler[ollama]   # Local LLM support
pip install multi-ai-handler[docling]  # Document processing (OCR, tables)
pip install multi-ai-handler[all]      # All optional dependencies

Setup

Create a .env file with your API keys:

ANTHROPIC_API_KEY=your_anthropic_api_key_here
CEREBRAS_API_KEY=your_cerebras_api_key_here
GEMINI_API_KEY=your_gemini_api_key_here
OPENAI_API_KEY=your_openai_api_key_here
OPENROUTER_API_KEY=your_openrouter_api_key_here

Usage

Basic Request

from multi_ai_handler import request_ai

response = request_ai(
    provider="google",  # or "anthropic", "openai", "openrouter", "cerebras", "ollama"
    model="gemini-2.5-flash",
    system_prompt="You are a helpful assistant.",
    user_text="What is the capital of France?"
)

JSON Output

data = request_ai(
    provider="openai",
    model="gpt-4o-mini",
    system_prompt="Return valid JSON only.",
    user_text="Convert to JSON: Name: Alice, Age: 25",
    json_output=True
)
# Returns: {'name': 'Alice', 'age': 25}

File Processing

response = request_ai(
    provider="anthropic",
    model="claude-sonnet-4-5-20250929",
    system_prompt="Summarize this document.",
    file="document.pdf"
)

Streaming

from multi_ai_handler import stream_ai

for chunk in stream_ai(provider="cerebras", model="llama-3.3-70b", user_text="Write a poem"):
    print(chunk, end="", flush=True)

Async Support

import asyncio
from multi_ai_handler import arequest_ai, astream_ai

async def main():
    # Concurrent requests
    responses = await asyncio.gather(
        arequest_ai(provider="google", model="gemini-2.0-flash", user_text="Hello"),
        arequest_ai(provider="anthropic", model="claude-sonnet-4-20250514", user_text="Hello"),
    )

    # Async streaming
    async for chunk in astream_ai(provider="openai", model="gpt-4o-mini", user_text="Hi"):
        print(chunk, end="", flush=True)

asyncio.run(main())

Conversation History

Use the Conversation class for multi-turn interactions:

from multi_ai_handler import AIProviderManager

manager = AIProviderManager()
conv = manager.conversation(
    provider="anthropic",
    model="claude-sonnet-4-20250514",
    system_prompt="You are a helpful assistant.",
)

response = conv.send("My name is Alice.")
print(response.content)

response = conv.send("What's my name?")  # Remembers context
print(response.content)

conv.clear()  # Reset conversation

With file processing:

conv = manager.conversation(provider="google", model="gemini-2.0-flash")

response = conv.send("Summarize this document", file="report.pdf")
print(response.content)

response = conv.send("What are the key findings?")  # Follow-up without re-sending file
print(response.content)

Model Information

from multi_ai_handler import list_models, get_model_info

all_models = list_models()  # {'google': [...], 'anthropic': [...], ...}
info = get_model_info(provider="anthropic", model="claude-sonnet-4-20250514")

API Reference

Functions

Function	Description
request_ai(provider, model, ...)	Generate a response
stream_ai(provider, model, ...)	Stream response tokens
arequest_ai(provider, model, ...)	Async generation
astream_ai(provider, model, ...)	Async streaming
list_models()	List all available models
get_model_info(provider, model)	Get model metadata

Parameters

Parameter	Type	Description
provider	str	"google", "anthropic", "openai", "openrouter", "cerebras", "ollama"
model	str	Model name (e.g., "gemini-2.5-flash", "claude-sonnet-4-5-20250929")
system_prompt	str	System instruction
user_text	str	User input text
messages	list[dict]	Conversation history from previous response.history
file	str/Path	File path for images or documents
temperature	float	Randomness (0.0-1.0), default: 0.2
json_output	bool	Parse response as JSON, default: False
local	bool	Use local text extraction (Docling), default: False

Classes

• AIProviderManager - Manage providers, register custom providers
• Conversation - Multi-turn conversation with automatic history management
• AIProvider - Abstract base class for implementing custom providers
• Provider classes: AnthropicProvider, GoogleProvider, OpenAIProvider, OpenrouterProvider, OllamaProvider, CerebrasProvider

License

MIT

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

Support

For issues and questions, please open an issue on the GitHub repository.