weycop 1.0.3

Python client for WeyCP API - Advanced AI chat completions service

WeyCP Python Client

The official Python client for WeyCP API chat completions created by Weycop.

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🚀 Features

• Familiar API Interface – Easy-to-use chat completions API.
• Sync & Async support – Use synchronous or asynchronous clients.
• Built-in error handling – Comprehensive exception handling.
• Usage tracking – Monitor token usage and costs.
• Type hints – Full type annotation support.
• High performance – Optimized for concurrent requests.

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📦 Installation

pip install weycop

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⚡ Quick Start

Synchronous Client

from weycop import WeycopClient

# Initialize client
client = WeycopClient(api_key="your-api-key")

# Simple chat
response = client.chat(
    message="Hello, how are you?",
    model="llama3.2:3b",
    system_prompt="You are a helpful assistant"
)
print(response)

Asynchronous Client

import asyncio
from weycop import AsyncWeycopClient

async def main():
    async with AsyncWeycopClient(api_key="your-api-key") as client:
        response = await client.chat(
            message="What is machine learning?",
            model="llama3.1:8b-8k"
        )
        print(response)

asyncio.run(main())

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🧠 Supported Models

• llama3.2:3b – Fast, lightweight model (~4 GB VRAM).
• llama3.1:8b-8k – Advanced model with 8K context (~8.9 GB VRAM).

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🛡️ Error Handling

from weycop import WeycopClient, AuthenticationError, RateLimitError, APIError

client = WeycopClient(api_key="your-key")

try:
    response = client.chat("Hello", model="llama3.2:3b")
    print(response)
except AuthenticationError:
    print("Invalid API key")
except RateLimitError:
    print("Rate limit exceeded")
except APIError as e:
    print(f"API error: {e}")

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🔄 Examples

System Prompts

from weycop import Message

completion = client.chat_completions_create(
    model="llama3.1:8b",
    messages=[
        Message("system", "You are a SQL expert. Only respond with SQL code."),
        Message("user", "Get all users created in the last 30 days")
    ]
)

Multi-turn Conversation

messages = [
    Message("system", "You are a helpful math tutor."),
    Message("user", "What is 15 + 27?"),
]

completion = client.chat_completions_create(model="llama3.2:3b", messages=messages)
assistant_response = completion.choices[0].message.content
messages.append(Message("assistant", assistant_response))

messages.append(Message("user", "Now multiply that by 3"))
completion = client.chat_completions_create(model="llama3.2:3b", messages=messages)
print(completion.choices[0].message.content)

Context Management

with WeycopClient(api_key="your-key") as client:
    response = client.chat("Hello!")
    print(response)
# Client is automatically closed

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Support

• Email: [e-mail]

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📜 License

MIT License - see LICENSE file for details.