Naga-LLM-MCP

# Deepseek-Naga Emotion AI An emotion AI chat program based on the DeepSeek API, featuring memory functionality and intelligent topic indexing. ## Features - Basic emotional chat functionality - Conversation history memory and context management - Intelligent topic recognition and automatic indexing - Simple and user-friendly interface - Comprehensive logging system ## Usage 1. Configure the API key: Edit the config.py file and enter your DeepSeek API key. 2. Install dependencies: `pip install -r requirements.txt` 3. Run the program: `python main.py` ## Index Recording System Naga has an intelligent index recording system that can: 1. Automatically record each conversation to a date file (e.g., `2025-04-19.txt`) 2. Automatically generate conversation topics when the number of conversations reaches a threshold or the user explicitly mentions "topic" related content 3. Save the generated topics to `summary.txt`, organized by date 4. Allow the AI to reference historical topic indexes when answering questions, enabling understanding of past conversations ### Topic Index Triggering Methods - The number of conversations reaches the configured threshold (default is 5) - The user mentions keywords such as "topic," "index," or "summary" - The user can force an update by sending commands like "update index" ### Topic Generation Mechanism The system uses AI to intelligently understand conversation content and generate topics, supporting multiple methods: 1. **Natural Language Embedded Index**: When the AI's reply contains a specific format `[Index Topic]: xxx[End]`, the system will automatically extract this part as the topic. This allows the AI to include index information in natural conversations without affecting overall expression. 2. **Automatic Index Generation**: When the user mentions keywords like "topic" or "index," or when the conversation reaches a certain number of exchanges, the system will automatically prompt the AI to generate a topic index. 3. **Standard Format Assurance**: The system ensures that all index topics follow a uniform format for easy viewing and understanding by users. The AI is guided to naturally incorporate the index topic in its replies when users mention index-related content, for example: ``` User: Summarize our previous conversation AI: We discussed the applications of artificial intelligence, and I believe the topic of this conversation is [Index Topic]: Discussion on AI Applications[End], covering three main areas: healthcare, education, and finance. ``` This approach ensures the AI's expression is natural and fluent while providing clear index information for the system. ### System Commands The system supports the following special commands: - `View Index`: Display all currently recorded conversation topic indexes - `Update Index`: Force an update of the topic index file - `Clear Screen`: Clear the terminal display content - `Exit`: End the conversation and exit the program ### Index File Description - `summary.txt`: Topic index file, an overview of conversation topics organized by date - `index_metadata.json`: Index metadata, recording processed files and update times ## Directory Structure - main.py: Main program entry - conversation.py: Core conversation processing - config.py: Global configuration file - chatlog/: Directory for storing chat records and indexes - summary.txt: Topic index file - YYYY-MM-DD.txt: Conversation records stored by date ## Configuration Options You can adjust the following configurations in `config.py`: - MAX_HISTORY_ROUNDS: Number of historical conversation rounds to remember - INDEX_DIALOG_THRESHOLD: Threshold for the number of conversations to automatically update the index - DEBUG: Whether to enable debug logging