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

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Install

openclaw skills install 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)