chatstack 0.0.3

Message-based LLM tools.

Chatstack

Minimalist Context Management for message-based GPTs

This Python code provides a chatbot implementation with context management using OpenAI's GPT-3.5-turbo or GPT-4 chat models. The chatbot maintains a conversation history and manages the context to ensure meaningful responses.

Dependencies

• loguru
• pydantic
• openai
• tiktoken

Classes

• ChatRoleMessage: A base class for messages with role, text, and tokens.
• SystemMessage: A message with the role 'system'.
• ContextMessage: A message added to the model input context to provide context for the model.
• AssistantMessage: A message with the role 'assistant'.
• UserMessage: A message with the role 'user'.
• ChatContext: A class that manages the conversation context and generates responses using OpenAI message interface models.

Usage

1. Import the ChatContext class.
2. Create an instance of the ChatContext class with the desired configuration.
3. Call the user_message method with the user's message text to get a response from the chatbot.

Example:

from chatstack import ChatContext

BASE_SYSTEM_PROMPT  = "You are a clever bot.  Do not apologize, or make excuses.  "
BASE_SYSTEM_PROMPT += "Do not mention that you are an AI language model since that is annoying to users."

def main():
    chat_context = ChatContext(base_system_msg_text=BASE_SYSTEM_PROMPT)

    print("Welcome to the Chatbot!")
    
    while True:
        user_input = input("You: ")        
        response = chat_context.user_message(user_input, stream=True)
        print("Chatbot:", response)

if __name__ == "__main__":
    main()

Configuration

The ChatContext class accepts the following parameters:

• min_response_tokens: Minimum number of tokens to reserve for model completion response.
• max_response_tokens: Maximum number of tokens to allow for model completion response.
• max_context_assistant_messages: Number of recent assistant messages to keep in context.
• max_context_user_messages: Number of recent user messages to keep in context.
• model: The name of the GPT model to use (default: "gpt-3.5-turbo").
• temperature: The temperature for the model's response generation.
• base_system_msg_text: The base system message text to provide context for the model.