multi-ai-handler 1.1.1

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, and Ollama (local LLMs).

Features

• Unified interface for multiple AI providers
• Support for text-only, file-only, or combined text and file inputs
• Automatic payload formatting for each provider's API requirements
• Support for images and documents (PDF)
• Local LLM support with Ollama
• Advanced document processing with Docling (OCR, table extraction)
• Streaming support for Anthropic Claude
• Environment-based API key management
• Optional dependencies for lightweight installations

Supported Providers

• Anthropic Claude
• Google Gemini
• OpenAI
• OpenRouter
• Ollama (Local LLMs)

Installation

Prerequisites

• Python 3.11 or higher

Basic Installation

pip install multi-ai-handler

This installs the core package with support for Anthropic, Google, OpenAI, and OpenRouter.

Optional Dependencies

For local LLM support and advanced document processing, install optional dependencies:

# For Ollama support (local LLMs)
pip install multi-ai-handler[ollama]

# For document processing with Docling (OCR, table extraction)
pip install multi-ai-handler[docling]

# For full local LLM support with document processing
pip install multi-ai-handler[local]

# Install all optional dependencies
pip install multi-ai-handler[all]

Optional features:

• ollama - Local LLM support via Ollama
• docling - Advanced document processing (OCR, table extraction) with EasyOCR
• local - Both ollama and docling for complete local setup
• all - All optional dependencies

Setup

1. Create a .env file

Create a .env file in your project root with your API keys:

ANTHROPIC_API_KEY=your_anthropic_api_key_here
GEMINI_API_KEY=your_gemini_api_key_here
OPENROUTER_API_KEY=your_openrouter_api_key_here

2. Import the library

from multi_ai_handler import request_ai

Usage

Using request_ai() (Recommended)

The request_ai() function provides a unified interface across all providers with automatic routing and JSON parsing support.

Basic text request

from multi_ai_handler import request_ai

# Uses default provider (Google Gemini)
response = request_ai(
    system_prompt="You are a helpful assistant.",
    user_text="What is the capital of France?"
)
print(response)

Specify a provider and model

# Provider specified as string
response = request_ai(
    system_prompt="You are a data extraction expert.",
    user_text="Extract key information from: John Doe, age 30, lives in NYC",
    provider="anthropic",
    model="claude-sonnet-4-5-20250929"
)

Supported providers: "google", "anthropic", "openai", "openrouter", "ollama"

JSON output parsing

# Automatically parses JSON from response
data = request_ai(
    system_prompt="You are a JSON formatter. Return valid JSON only.",
    user_text="Convert to JSON: Name: Alice, Age: 25, City: London",
    json_output=True
)
print(data)  # Returns parsed dict: {'name': 'Alice', 'age': 25, 'city': 'London'}

File processing (images and documents)

# With images
response = request_ai(
    system_prompt="You are an image analysis expert.",
    user_text="Describe what you see in this image.",
    file="image.jpg",
    provider="google"
)

# With documents
response = request_ai(
    system_prompt="Summarize this document.",
    file="document.pdf",
    provider="anthropic"
)

# Using pathlib.Path
from pathlib import Path
response = request_ai(
    system_prompt="Analyze this document.",
    file=Path("documents/report.pdf"),
    provider="anthropic"
)

Local LLM with Ollama

# Requires: pip install multi-ai-handler[ollama]
response = request_ai(
    system_prompt="You are a helpful assistant.",
    user_text="What is the capital of France?",
    provider="ollama",
    model="llama3.2"
)

Using Provider-Specific Functions

For direct access to provider-specific features, you can use individual functions. All provider functions share the same parameter structure:

from multi_ai_handler import (
    request_anthropic,
    request_google,
    request_openai,
    request_openrouter,
    request_ollama
)

Examples

Anthropic Claude (with streaming support):

response = request_anthropic(
    system_prompt="You are a helpful assistant.",
    user_text="What is the capital of France?",
    model="claude-3-5-sonnet-20241022",
    temperature=0.7
)

Google Gemini (with token usage reporting):

response = request_google(
    system_prompt="Analyze this image.",
    file="image.jpg",
    model="gemini-1.5-flash"
)

OpenAI (with custom endpoint support):

response = request_openai(
    system_prompt="You are helpful.",
    user_text="Hello!",
    model="gpt-4",
    link="https://custom-endpoint.com"  # Optional custom base URL
)

Ollama (with local document processing):

# Requires: pip install multi-ai-handler[local]
response = request_ollama(
    system_prompt="Summarize this document.",
    file="document.pdf",  # Uses Docling for OCR and table extraction
    model="llama3.2"
)

API Reference

request_ai() (Unified Interface)

Recommended - Unified function that automatically routes to the appropriate provider with built-in JSON parsing support.

def request_ai(
    system_prompt: str,
    user_text: str = None,
    file: str | Path | dict = None,
    provider: str = None,
    model: str = None,
    temperature: float = 0.2,
    json_output: bool = False
) -> str | dict

Parameters:

• system_prompt (str, required): The system instruction for the AI model
• user_text (str, optional): The user's text input
• file (str | Path | dict, optional): File to process. Can be:
  • File path: "image.jpg" or Path("image.jpg") - automatically reads and encodes
  • Dict: {"filename": "image.jpg", "encoded_data": "base64..."} - for pre-encoded data
• provider (str, optional): Provider name - "google", "anthropic", "openai", "openrouter", or "ollama". Defaults to "google"
• model (str, optional): Model to use. Defaults to first supported model for the provider
• temperature (float, optional): Controls randomness (0.0 = deterministic, 1.0 = creative). Default: 0.2
• json_output (bool, optional): If True, parses and returns JSON as dict. Default: False

Returns:

• str if json_output=False
• dict if json_output=True

Note: Either user_text or file must be provided.

---

Direct Provider Functions

For advanced use cases requiring specific provider features, use these functions directly.

Common Parameters

All direct provider functions share these parameters:

• system_prompt (str, required): The system instruction for the AI model
• user_text (str, optional): The user's text input
• file (str | Path | dict, optional): File to process (formats same as above)
• model (str, required): The specific model to use
• temperature (float, optional): Controls randomness. Default: 0.0

Note: Either user_text or file must be provided.

request_anthropic()

Makes a request to Anthropic's Claude API with streaming support.

def request_anthropic(
    system_prompt: str,
    user_text: str = None,
    file: str | Path | dict = None,
    model: str = None,
    temperature: float = 0.0
) -> str

Supported file types: Images (PNG, JPEG, GIF, WebP), Documents (PDF, DOCX, TXT, etc.)

request_google()

Makes a request to Google's Gemini API with token usage reporting.

def request_google(
    system_prompt: str,
    user_text: str = None,
    file: str | Path | dict = None,
    model: str = None,
    temperature: float = 0.0
) -> str

Supported file types: Images, videos, audio, documents

Note: Prints token usage (prompt, output, and total tokens) to console.

request_openai()

Makes a request to OpenAI's API.

def request_openai(
    system_prompt: str,
    user_text: str = None,
    file: str | Path | dict = None,
    model: str = None,
    temperature: float = 0.0,
    link: str = None
) -> str

Supported file types: Images (PNG, JPEG, GIF, WebP), PDFs (via file API)

Additional parameter:

• link (str, optional): Custom base URL for API endpoint

request_openrouter()

Makes a request through OpenRouter's unified API.

def request_openrouter(
    system_prompt: str,
    user_text: str = None,
    file: str | Path | dict = None,
    model: str = None,
    temperature: float = 0.0
) -> str

Uses OpenAI-compatible format. Requires OPENROUTER_API_KEY in environment.

request_ollama()

Makes a request to a local Ollama instance for running LLMs locally.

def request_ollama(
    system_prompt: str,
    user_text: str = None,
    file: str | Path | dict = None,
    model: str = None,
    temperature: float = 0.0
) -> str

Requirements:

• Install with: pip install multi-ai-handler[ollama]
• For document processing: pip install multi-ai-handler[local]
• Ollama must be running locally

Supported file types: Documents (PDF, DOCX, etc.) via Docling extraction

Note: File processing extracts text using Docling (OCR, table extraction) and includes it in the prompt.

---

Payload Generation

The library automatically formats payloads for each provider using specialized functions:

• generate_openai_payload(): Creates OpenAI-compatible content blocks
• generate_gemini_payload(): Creates Gemini-compatible parts
• generate_claude_payload(): Creates Claude-compatible content blocks
• generate_ollama_payload(): Creates Ollama-compatible messages with text extraction

These functions handle:

• MIME type detection from filenames
• Base64 data URL formatting
• Provider-specific content structure
• Document text extraction (Ollama with Docling)
• Validation of required inputs

Error Handling

The library raises errors in the following cases:

• ValueError: Neither user_text nor file is provided
• ValueError: MIME type cannot be detected from filename
• FileNotFoundError: File path provided but file doesn't exist
• ValueError: Path provided is not a file (e.g., is a directory)
• Invalid API credentials

Example:

from multi_ai_handler import request_ai

try:
    response = request_ai(
        system_prompt="You are helpful.",
        file="document.pdf",
        provider="anthropic",
        model="claude-3-5-sonnet-20241022"
    )
except FileNotFoundError as e:
    print(f"File not found: {e}")
except ValueError as e:
    print(f"Error: {e}")

Best Practices

1. Use request_ai() for most tasks: The unified interface provides consistent behavior across all providers
2. Use specific model names: Always specify the exact model version when needed (e.g., claude-3-5-sonnet-20241022)
3. Handle errors: Wrap API calls in try-except blocks
4. Manage API keys securely: Never commit .env files to version control
5. Optimize temperature: Use lower values (0.0-0.3) for factual tasks, higher (0.7-1.0) for creative tasks

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.