llm-onesdk 0.0.2

OneSDK is a Python library that provides a unified interface for interacting with various Large Language Model (LLM) providers.

OneSDK: Unified LLM API Interface

OneSDK is a Python library providing a unified interface for various Large Language Model (LLM) providers. It simplifies interactions with different LLM APIs through a consistent set of methods.

Features

• Unified API for multiple LLM providers
• Flexible usage: per-call model specification or default model setting
• Intuitive interface for common LLM operations
• Synchronous and streaming text generation support
• Token counting functionality
• File upload capability (provider-dependent)
• Proxy setting for API calls
• Usage statistics retrieval (provider-dependent)

Installation

pip install llm-onesdk

Quick Start

OneSDK supports two main usage patterns:

1. Specify model for each call

from llm_onesdk import OneSDK

sdk = OneSDK("anthropic", {"api_key": "your-api-key"})

response = sdk.generate(
    model="claude-3-opus-20240229",
    messages=[{"role": "user", "content": "Tell me a joke about programming."}]
)
print(response['choices'][0]['message']['content'])

2. Set a default model

from llm_onesdk import OneSDK

sdk = OneSDK("anthropic", {"api_key": "your-api-key"})
sdk.set_model("claude-3-opus-20240229")

response = sdk.generate(
    messages=[{"role": "user", "content": "Tell me a joke about programming."}]
)
print(response['choices'][0]['message']['content'])

Streaming Generation

for chunk in sdk.stream_generate(
    model="claude-3-opus-20240229",  # Optional if using set_model()
    messages=[{"role": "user", "content": "Write a short story about AI."}]
):
    print(chunk['choices'][0]['message']['content'], end='', flush=True)

Additional Operations

# List models
models = sdk.list_models()
print(models)

# Count tokens
token_count = sdk.count_tokens(
    model="claude-3-opus-20240229",
    messages=[{"role": "user", "content": "How many tokens is this?"}]
)
print(f"Token count: {token_count}")

Supported Providers and Methods

The following table shows the supported providers, their default models, and the methods they support:

Provider	list_models	generate	stream_generate	count_tokens	create_embedding	create_image
Anthropic	✓	✓	✓	✓	✗	✗
Qwen	✓	✓	✓	✓	✓	✗
Cohere	✗	✓	✓	✓	✓	✗
Doubao	✗	✓	✓	✓	✓	✗
Gemini	✗	✓	✓	✗	✗	✗
Minimax	✗	✓	✓	✓	✓	✓
OpenAI	✓	✓	✓	✓	✓	✓
Wenxin	✗	✓	✓	✓	✗	✗

✓: Supported, ✗: Not supported

Key Methods

• set_model(model): Set default model
• list_models(): List available models
• get_model_info(model_id): Get model information
• generate(messages, model=None, **kwargs): Generate response
• stream_generate(messages, model=None, **kwargs): Stream response
• count_tokens(model, messages): Count tokens
• create_completion(model, prompt, **kwargs): Legacy API completion
• create_embedding(model, input, **kwargs): Create embeddings
• create_image(prompt, **kwargs): Create image (for supported providers)
• upload_file(file_path): Upload file
• set_proxy(proxy_url): Set proxy
• get_usage(): Get usage statistics

Error Handling

OneSDK uses custom exceptions inheriting from InvokeError (e.g., InvokeModelNotFoundError).

Contributing

We welcome contributions, especially new provider integrations! See our Contributing Guide for details.

License

This project is under the MIT License. See the LICENSE file for details.