text-adventure-llm/architecture.md

Text-Based LLM Interaction System Architecture

Overview

This document outlines the architecture for a text-based system that allows users to interact with an LLM running on LM Studio.

Components

1. User Interface Layer

• Text Input Handler: Captures user input from terminal
• Text Output Display: Shows LLM responses to user
• Session Manager: Manages conversation history

2. Communication Layer

• LLM Client: Handles HTTP communication with LM Studio
• API Interface: Formats requests/responses according to LM Studio's API

3. Core Logic Layer

• Message Processor: Processes user input and LLM responses
• Conversation History: Maintains context between messages

Data Flow

graph TD
    A[User] --> B[Text Input Handler]
    B --> C[Message Processor]
    C --> D[LLM Client]
    D --> E[LM Studio Server]
    E --> D
    D --> C
    C --> F[Text Output Display]
    F --> A

Technical Details

LM Studio API

• Endpoint: http://10.0.0.200:1234/v1/chat/completions
• Method: POST
• Content-Type: application/json

Request Format

{
  "model": "model_name",
  "messages": [
    {"role": "user", "content": "user message"}
  ],
  "temperature": 0.7,
  "max_tokens": -1
}

Response Format

{
  "id": "chatcmpl-123",
  "object": "chat.completion",
  "created": 1677652288,
  "model": "model_name",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "response message"
      },
      "finish_reason": "stop"
    }
  ]
}

Project Structure

text_adventure/
├── main.py              # Entry point
├── config.py             # Configuration settings
├── llm_client.py         # LLM communication
├── interface.py          # Text input/output
└── conversation.py       # Conversation history management

Implementation Plan

1. Create basic text input/output interface
2. Implement LLM client for LM Studio communication
3. Add conversation history management
4. Integrate components
5. Test functionality