custom bots and memory

2026-03-03 10:20:17 -05:00
parent 25e3484193
commit b44cdfbeb3
6 changed files with 934 additions and 71 deletions
--- a/README.md
+++ b/README.md
@@ -1,14 +1,158 @@
-# Vibe Discord Bots
+# Vibe Discord Bot with RAG Chat History
+
+A Discord bot that stores long-term chat history using SQLite database with RAG (Retrieval-Augmented Generation) capabilities powered by custom embedding models.
+
+## Quick Start - Available Commands
+
+### Pre-built Bots
+
+| Command      | Description                   | Example Usage                              |
+| ------------ | ----------------------------- | ------------------------------------------ |
+| `!doodlebob` | Generate images from text     | `!doodlebob a cat sitting on a moon`       |
+| `!retcon`    | Edit images with text prompts | `!retcon <image attachment> Make it sunny` |
+
+### Custom Bot Management
+
+| Command                        | Description                                   | Example Usage                                    |
+| ------------------------------ | --------------------------------------------- | ------------------------------------------------ |
+| `!custom <name> <personality>` | Create a custom bot with specific personality | `!custom alfred you are a proper british butler` |
+| `!list-custom-bots`            | List all available custom bots                | `!list-custom-bots`                              |
+| `!delete-custom-bot <name>`    | Delete your custom bot                        | `!delete-custom-bot alfred`                      |
+
+### Using Custom Bots
+
+Once you create a custom bot, you can interact with it directly by prefixing your message with the bot name:
+
+```bash
+!<bot_name> <your message>
+```
+
+**Example:**
+
+1. Create a bot: `!custom alfred you are a proper british butler`
+2. Use the bot: `alfred Could you fetch me some tea?`
+3. The bot will respond in character as a British butler
+
+## Features
+
+- **Long-term chat history storage**: Persistent storage of all bot interactions
+- **RAG-based context retrieval**: Smart retrieval of relevant conversation history using vector embeddings
+- **Custom embedding model**: Uses qwen3-embed-4b for semantic search capabilities
+- **Efficient message management**: Automatic cleanup of old messages based on configurable limits
+
+- **Long-term chat history storage**: Persistent storage of all bot interactions
+- **RAG-based context retrieval**: Smart retrieval of relevant conversation history using vector embeddings
+- **Custom embedding model**: Uses qwen3-embed-4b for semantic search capabilities
+- **Efficient message management**: Automatic cleanup of old messages based on configurable limits
+
+## Setup
+
+### Prerequisites
+
+- Python 3.10 or higher
+- [uv](https://docs.astral.sh/uv/) package manager
+- Embedding API key
+- Discord bot token
+
+### Environment Variables
+
+Create a `.env` file or export the following variables:
+
+```bash
+# Discord Bot Token
+export DISCORD_TOKEN=your_discord_bot_token
+
+# Embedding API Configuration
+export OPENAI_API_KEY=your_embedding_api_key
+export OPENAI_API_ENDPOINT=https://llama-embed.reeselink.com/embedding
+
+# Image Generation (optional)
+export IMAGE_GEN_ENDPOINT=http://toybox.reeselink.com:1234/v1
+export IMAGE_EDIT_ENDPOINT=http://toybox.reeselink.com:1235/v1
+
+# Database Configuration (optional)
+export CHAT_DB_PATH=chat_history.db
+export EMBEDDING_MODEL=qwen3-embed-4b
+export EMBEDDING_DIMENSION=2048
+export MAX_HISTORY_MESSAGES=1000
+export SIMILARITY_THRESHOLD=0.7
+export TOP_K_RESULTS=5
+```
+
+### Installation
+
+1. Sync dependencies with uv:
+```bash
+uv sync
+```
+
+2. Run the bot:
+```bash
+uv run main.py
+```
+
+## How It Works
+
+### Database Structure
+
+The system uses two SQLite tables:
+
+1. **chat_messages**: Stores message metadata
+   - message_id, user_id, username, content, timestamp, channel_id, guild_id
+
+2. **message_embeddings**: Stores vector embeddings for RAG
+   - message_id, embedding (as binary blob)
+
+### RAG Process
+
+1. When a message is received, it's stored in the database
+2. An embedding is generated using OpenAI's embedding API
+3. The embedding is stored alongside the message
+4. When a new message is sent to the bot:
+   - The system searches for similar messages using vector similarity
+   - Relevant context is retrieved and added to the prompt
+   - The LLM generates a response with awareness of past conversations
+
+### Configuration Options
+
+- **MAX_HISTORY_MESSAGES**: Maximum number of messages to keep (default: 1000)
+- **SIMILARITY_THRESHOLD**: Minimum similarity score for context retrieval (default: 0.7)
+- **TOP_K_RESULTS**: Number of similar messages to retrieve (default: 5)
+- **EMBEDDING_MODEL**: OpenAI embedding model to use (default: text-embedding-3-small)
+
+## Usage
+
+The bot maintains conversation context automatically. When you ask a question, it will:
+
+1. Search for similar past conversations
+2. Include relevant context in the prompt
+3. Generate responses that are aware of the conversation history
+
+## File Structure
+
+```text
+vibe_discord_bots/
+├── main.py              # Main bot application
+├── database.py          # SQLite database with RAG support
+├── pyproject.toml       # Project dependencies (uv)
+├── .env                 # Environment variables
+├── .venv/               # Virtual environment (created by uv)
+└── README.md           # This file
+```

 ## Build

-### UV
+### Using uv

 ```bash
+# Set environment variables
 export DISCORD_TOKEN=$(cat .token)
+export OPENAI_API_KEY=your_api_key
 export OPENAI_API_ENDPOINT="https://llama-cpp.reeselink.com"
 export IMAGE_GEN_ENDPOINT="http://toybox.reeselink.com:1234/v1"
 export IMAGE_EDIT_ENDPOINT="http://toybox.reeselink.com:1235/v1"
+
+# Run with uv
 uv run main.py
 ```