llm-onesdk 0.0.3

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
• Embedding creation (for supported providers)
• Image generation (for supported providers)
• File operations (for supported providers)
• Proxy setting for API calls
• Usage statistics retrieval (for supported providers)

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("openai", {"api_key": "your-api-key"})

response = sdk.generate(
    model="gpt-3.5-turbo",
    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("openai", {"api_key": "your-api-key"})
sdk.set_model("gpt-3.5-turbo")

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="gpt-3.5-turbo",  # 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 (for supported providers)
models = sdk.list_models()
print(models)

# Count tokens
token_count = sdk.count_tokens(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "How many tokens is this?"}]
)
print(f"Token count: {token_count}")

# Create embeddings (for supported providers)
embeddings = sdk.create_embedding(
    model="text-embedding-ada-002",
    input="Hello, world!"
)
print(embeddings)

# Generate image (for supported providers)
image_response = sdk.create_image("A futuristic city with flying cars")
print(image_response)

Supported Providers and Core Methods

The following table shows the supported providers and their core method support:

Provider	list_models	generate	stream_generate	count_tokens	create_embedding	create_image
Anthropic	✓	✓	✓	✓	✗	✗
Qwen (通义千问)	✓	✓	✓	✓	✓	✗
Cohere	✓	✓	✓	✓	✓	✗
Doubao	✓	✓	✓	✓	✓	✗
Gemini	✗	✓	✓	✓	✓	✗
Kimi	✓	✓	✓	✓	✗	✗
MiniMax	✗	✓	✓	✓	✓	✓
Ollama	✓	✓	✓	✓	✓	✗
OpenAI	✓	✓	✓	✓	✓	✓
Wenxin (文心一言)	✗	✓	✓	✓	✗	✗

✓: Supported, ✗: Not supported

Note: Some providers may have additional provider-specific methods. Refer to individual provider documentation for details.

Key Methods

• set_model(model): Set default model
• list_models(): List available models (if supported)
• generate(messages, model=None, **kwargs): Generate response
• stream_generate(messages, model=None, **kwargs): Stream response
• count_tokens(model, messages): Count tokens
• create_embedding(model, input, **kwargs): Create embeddings (if supported)
• create_image(prompt, **kwargs): Create image (if supported)
• upload_file(file_path): Upload file (if supported)
• set_proxy(proxy_url): Set proxy for API calls

Error Handling

OneSDK uses custom exceptions inheriting from InvokeError. Always wrap API calls in try-except blocks:

from llm_onesdk.utils.error_handler import InvokeError

try:
    response = sdk.generate(model, messages)
except InvokeError as e:
    print(f"An error occurred: {str(e)}")

Documentation

For detailed information on each provider's capabilities and usage, please refer to the individual documentation files in the docs/ directory.

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.