Pywayne Llm Chat Bot

LLM chat interface using OpenAI-compatible APIs with streaming support and session management. Use when working with pywayne.llm.chat_bot module for creating LLM chat instances with custom configuration, managing multi-turn conversations with history, handling streaming responses, or managing multiple chat sessions with ChatManager

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

Quick Start

python

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

python

# 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

python

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

python

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

Managing Multiple Sessions

python

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

python

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)