SChat is a powerful and flexible chat library that provides a unified interface for multiple LLM providers.
- 🚀 Unified Interface: One consistent API for multiple LLM providers (OpenAI, Anthropic, Google, DeepSeek, etc.)
- 🔄 Easy Provider Switching: Seamlessly switch between different models in the same chat session
- 🛠️ Rich Features Support:
- Multi-modal chat (text + images)
- Function calling / Tools
- Streaming responses
- Chat history management
- System prompts
- 🔑 Smart Key Management: Automatic API key rotation and load balancing
- 💾 Session Management: Save and load chat sessions
- 🔌 Extensible: Easy to add new providers
pip install schatfrom schat import ChatSession
# Create a session with OpenAI
session = ChatSession("openai:gpt-4o")
# Send a message
response = session.send("What is Python?")
print(response.text)
# Multi-turn conversation
response = session.send("What are its main features?")
print(response.text)
# Switch to a different model
response = session.send("Tell me more", model="anthropic:claude-3-5-haiku-20241022")
print(response.text)# Chat with images
response = session.send(
"What's in this image?",
files=["path/to/image.jpg"]
)
print(response.text)
# Use both local and online images
response = session.send(
"Compare these images:",
files=[
"local_image.jpg",
"https://example.com/online_image.jpg"
]
)# Define a weather tool
weather_tool = {
"type": "function",
"function": {
"name": "get_weather",
"description": "Get weather information",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string"},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
},
"required": ["location"]
}
}
}
# Send request with tool
response = session.send(
"What's the weather in Beijing?",
tools=[weather_tool]
)
# Handle tool calls
if response.tool_calls:
for tool_call in response.tool_calls:
if tool_call["function"]["name"] == "get_weather":
# Mock tool response
result = {
"location": "Beijing",
"temperature": 20,
"unit": "celsius",
"condition": "sunny"
}
session.add_tool_message(result, tool_call["id"])
# Get final response
final_response = session.send("Continue with the weather information")
print(final_response.text)# Enable streaming
for chunk in session.send("Tell me a story", stream=True):
print(chunk, end="", flush=True)# Set system prompt
session.set_system_prompt("You are a helpful assistant who speaks like Shakespeare.")
# Save chat history
session.save("chat_history.json")
# Load chat history
new_session = ChatSession()
new_session.load("chat_history.json")import os
# Multiple keys for load balancing
os.environ["OPENAI_KEY"] = "key1,key2,key3"
os.environ["ANTHROPIC_KEY"] = "key1,key2"
os.environ["GOOGLE_KEY"] = "key1"
# Or set key directly
from schat.models.openai import OpenAIModel
model = OpenAIModel()
model.set_api_key("your-api-key")- OpenAI (GPT-4, GPT-3.5)
- Anthropic (Claude-3)
- Google (Gemini)
- DeepSeek
- OpenRouter
- GLM (ChatGLM)
- Qwen (通义千问)
- More coming soon...
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License.