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")

Custom Configuration per Session

custom_config = LLMConfig(
    base_url=base_url,
    api_key=api_key,
    model="deepseek-chat",
    temperature=0.9,
    system_prompt="You are a creative writer"
)
chat3 = manager.get_chat("user3", config=custom_config)

API Reference

LLMChat

| Method | Description | |--------|-------------| | ask(prompt, stream=False) | Single-turn conversation without history | | chat(prompt, stream=True) | Multi-turn conversation with history tracking | | update_system_prompt(prompt) | Update system prompt in-place | | clear_history() | Clear conversation history (keeps system prompt) | | history (property) | Get copy of current conversation history |

ChatManager

| Method | Description | |--------|-------------| | get_chat(chat_id, stream=True, config=None) | Get or create chat instance by ID | | remove_chat(chat_id) | Remove chat session |

Parameters

| Parameter | Default | Description | |-----------|---------|-------------| | base_url | required | API base URL (e.g., https://api.deepseek.com/v1) | | api_key | required | API authentication key | | model | "deepseek-chat" | Model name | | temperature | 0.7 | Controls randomness (0-2) | | max_tokens | 2048/8192 | Maximum output tokens | | top_p | 1.0 | Nucleus sampling (0-1) | | frequency_penalty | 0.0 | Reduces repetition (-2 to 2) | | presence_penalty | 0.0 | Encourages new topics (-2 to 2) | | system_prompt | "你是一个严谨的助手" | System message | | timeout | inf | Session timeout in seconds (ChatManager only) |

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)

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")