pywayne-llm-chat-bot

Pywayne LLM Chat Bot

This module provides a synchronous LLM chat interface compatible with OpenAI APIs (including local servers like Ollama).

Quick Start

from pywayne.llm.chat_bot import LLMChat

# Create chat instance
chat = LLMChat(
    base_url="https://api.example.com/v1",
    api_key="your_api_key",
    model="deepseek-chat"
)

# Single-turn conversation (non-streaming)
response = chat.ask("Hello, LLM!", stream=False)
print(response)

# Streaming response
for token in chat.ask("Explain recursion", stream=True):
    print(token, end='', flush=True)

Multi-turn Conversation

# Use chat() for history tracking
for token in chat.chat("What is a class in Python?"):
    print(token, end='', flush=True)

# Continuation - remembers previous context
for token in chat.chat("How do I define a constructor?"):
    print(token, end='', flush=True)

# View history
for msg in chat.history:
    print(f"{msg['role']}: {msg['content']}")

# Clear history
chat.clear_history()

Configuration

LLMConfig Class

from pywayne.llm.chat_bot import LLMConfig

config = LLMConfig(
    base_url="https://api.example.com/v1",
    api_key="your_api_key",
    model="deepseek-chat",
    temperature=0.7,
    max_tokens=8192,
    top_p=1.0,
    frequency_penalty=0.0,
    presence_penalty=0.0,
    system_prompt="You are a helpful assistant"
)

chat = LLMChat(**config.to_dict())

Dynamic System Prompt Update

chat.update_system_prompt("You are now a Python expert, provide code examples")

Managing Multiple Sessions

from pywayne.llm.chat_bot import ChatManager

manager = ChatManager(
    base_url="https://api.example.com/v1",
    api_key="your_api_key",
    model="deepseek-chat",
    timeout=300  # Session timeout in seconds
)

# Get or create chat instance (maintains per-session history)
chat1 = manager.get_chat("user1")
chat2 = manager.get_chat("user2")

# Sessions are independent
chat1.chat("Hello from user1")
chat2.chat("Hello from user2")

# Remove a session
manager.remove_chat("user1")