add custom voices

.vscode/launch.json

@@ -1,9 +1,14 @@
 {
-    // Use IntelliSense to learn about possible attributes.
-    // Hover to view descriptions of existing attributes.
-    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
     "version": "0.2.0",
     "configurations": [
+        {
+            "name": "Vibe Bot: Module",
+            "type": "debugpy",
+            "request": "launch",
+            "module": "vibe_bot.main",
+            "console": "integratedTerminal",
+            "envFile": "${workspaceFolder}/.env"
+        },
         {
             "name": "Python Debugger: Current File",
             "type": "debugpy",

README.md

@@ -1,217 +1,282 @@
 # 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.
+A Discord bot that stores long-term chat history using SQLite with RAG (Retrieval-Augmented Generation) capabilities. It supports custom bots with personalities, text-to-speech via Kokoro, image generation, and image editing.
 
 - [Vibe Discord Bot with RAG Chat History](#vibe-discord-bot-with-rag-chat-history)
-  - [Quick Start - Available Commands](#quick-start---available-commands)
+  - [Available Commands](#available-commands)
-    - [Pre-built Bots](#pre-built-bots)
     - [Custom Bot Management](#custom-bot-management)
     - [Using Custom Bots](#using-custom-bots)
+    - [Text-to-Speech](#text-to-speech)
+    - [Image Commands](#image-commands)
+    - [Bot Conversations](#bot-conversations)
+    - [Chat History](#chat-history)
   - [Features](#features)
   - [Setup](#setup)
     - [Prerequisites](#prerequisites)
     - [Environment Variables](#environment-variables)
     - [Installation](#installation)
+    - [Running the Bot](#running-the-bot)
   - [How It Works](#how-it-works)
     - [Database Structure](#database-structure)
     - [RAG Process](#rag-process)
-    - [Configuration Options](#configuration-options)
-  - [Usage](#usage)
   - [File Structure](#file-structure)
-  - [Build](#build)
+  - [Building](#building)
-    - [Using uv](#using-uv)
+    - [Local](#local)
     - [Container](#container)
-  - [Docs](#docs)
+  - [Testing](#testing)
-    - [Open AI](#open-ai)
+  - [Configuration](#configuration)
-  - [Models](#models)
-    - [Qwen3.5](#qwen35)
 
-
+## Available Commands
-## 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                                    |
+| Command                            | Description                            | Example Usage                                        |
-| ------------------------------ | --------------------------------------------- | ------------------------------------------------ |
+| ---------------------------------- | -------------------------------------- | ---------------------------------------------------- |
-| `!custom <name> <personality>` | Create a custom bot with specific personality | `!custom alfred you are a proper british butler` |
+| `!custom-bot <name> <personality>` | Create a custom bot with a personality | `!custom-bot alfred you are a proper british butler` |
-| `!list-custom-bots`            | List all available custom bots                | `!list-custom-bots`                              |
+| `!list-custom-bots`                | List all available custom bots         | `!list-custom-bots`                                  |
-| `!delete-custom-bot <name>`    | Delete your custom bot                        | `!delete-custom-bot alfred`                      |
+| `!delete-custom-bot <name>`        | Delete your custom bot (owner only)    | `!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:
+Once you create a custom bot, interact with it by prefixing your message with the bot name:
 
-```bash
+```text
 !<bot_name> <your message>
 ```
 
 **Example:**
 
-1. Create a bot: `!custom alfred you are a proper british butler`
+1. Create a bot: `!custom-bot 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
 
+### Text-to-Speech
+
+| Command                              | Description                                           | Example Usage                              |
+| ------------------------------------ | ----------------------------------------------------- | ------------------------------------------ |
+| `!speak <text>`                      | Convert text to speech (MP3 attachment)               | `!speak hello world`                       |
+| `!speak <text> --voice <voice>`      | Convert text to speech with a specific voice          | `!speak hello world --voice af_bella`      |
+| `!speak <bot_name> <text>`           | Have a custom bot respond and speak                   | `!speak alfred what time is it`            |
+| `!speak <bot_name> <text> --voice`   | Have a custom bot respond and speak with a voice      | `!speak alfred what time is it --voice am_puck` |
+| `!voices`                            | List all available TTS voices by category             | `!voices`                                  |
+
+### Image Commands
+
+| Command      | Description                          | Example Usage                              |
+| ------------ | ------------------------------------ | ------------------------------------------ |
+| `!doodlebob` | Generate an image from a text prompt | `!doodlebob a cat sitting on the moon`     |
+| `!retcon`    | Edit an attached image with text     | `!retcon <image attachment> Make it sunny` |
+
+### Bot Conversations
+
+| Command                                | Description                                 | Example Usage                                    |
+| -------------------------------------- | ------------------------------------------- | ------------------------------------------------ |
+| `!talkforme <bot1> <bot2> <n> <topic>` | Have two bots discuss a topic for n replies | `!talkforme alfred jarvis 4 the meaning of life` |
+
+### Chat History
+
+| Command               | Description                           | Example Usage     |
+| --------------------- | ------------------------------------- | ----------------- |
+| `!history <bot_name>` | View the chat history of a custom bot | `!history alfred` |
+
 ## Features
 
-- **Long-term chat history storage**: Persistent storage of all bot interactions
+- **Long-term chat history storage**: Persistent storage of all bot interactions in SQLite
 - **RAG-based context retrieval**: Smart retrieval of relevant conversation history using vector embeddings
-- **Custom embedding model**: Uses qwen3-embed-4b for semantic search capabilities
+- **Custom bots**: Create unlimited bots with unique personalities
-- **Efficient message management**: Automatic cleanup of old messages based on configurable limits
+- **Text-to-speech**: Kokoro TTS engine converts bot responses to MP3 audio
-
+- **Image generation**: Generate images from text prompts via OpenAI-compatible API
-- **Long-term chat history storage**: Persistent storage of all bot interactions
+- **Image editing**: Edit uploaded images with text instructions
-- **RAG-based context retrieval**: Smart retrieval of relevant conversation history using vector embeddings
+- **Bot conversations**: Two custom bots can discuss a topic autonomously
-- **Custom embedding model**: Uses qwen3-embed-4b for semantic search capabilities
+- **Chat history**: View the full conversation history of any custom bot with `!history`
-- **Efficient message management**: Automatic cleanup of old messages based on configurable limits
+- **Automatic message cleanup**: Configurable limits on stored messages
 
 ## Setup
 
 ### Prerequisites
 
-- Python 3.10 or higher
+- Python 3.13 or higher
 - [uv](https://docs.astral.sh/uv/) package manager
-- Embedding API key
 - Discord bot token
+- OpenAI-compatible API endpoints (for chat, embeddings, and image generation)
 
 ### Environment Variables
 
-Create a `.env` file or export the following variables:
+Create a `.env` file with the following variables:
 
 ```bash
-# Discord Bot Token
+# Discord Bot Token (required)
-export DISCORD_TOKEN=your_discord_bot_token
+DISCORD_TOKEN=your_discord_bot_token
 
-# Embedding API Configuration
+# Chat/Completion API (required)
-export OPENAI_API_KEY=your_embedding_api_key
+CHAT_ENDPOINT=https://your-api.com/v1
-export OPENAI_API_ENDPOINT=https://llama-embed.reeselink.com/embedding
+COMPLETION_ENDPOINT=https://your-api.com/v1
+CHAT_ENDPOINT_KEY=your_api_key
+COMPLETION_ENDPOINT_KEY=your_api_key
+CHAT_MODEL=your_model_name
+COMPLETION_MODEL=your_model_name
 
-# Image Generation (optional)
+# Image Generation (required)
-export IMAGE_GEN_ENDPOINT=http://toybox.reeselink.com:1234/v1
+IMAGE_GEN_ENDPOINT=https://your-api.com/v1
-export IMAGE_EDIT_ENDPOINT=http://toybox.reeselink.com:1235/v1
+IMAGE_EDIT_ENDPOINT=https://your-api.com/v1
+IMAGE_GEN_ENDPOINT_KEY=your_api_key
+IMAGE_EDIT_ENDPOINT_KEY=your_api_key
+IMAGE_GEN_MODEL=gen
+IMAGE_EDIT_MODEL=edit
 
-# Database Configuration (optional)
+# Embedding API (required)
-export CHAT_DB_PATH=chat_history.db
+EMBEDDING_ENDPOINT=https://your-api.com/v1
-export EMBEDDING_MODEL=qwen3-embed-4b
+EMBEDDING_ENDPOINT_KEY=your_api_key
-export EMBEDDING_DIMENSION=2048
+EMBEDDING_MODEL=your_embed_model
-export MAX_HISTORY_MESSAGES=1000
+
-export SIMILARITY_THRESHOLD=0.7
+# Optional: TTS Configuration
-export TOP_K_RESULTS=5
+TTS_MODEL_PATH=kokoro-v1.0.onnx
+TTS_VOICES_PATH=voices-v1.0.bin
+TTS_VOICE=af_sarah
+TTS_SPEED=1.0
+
+# Optional: Database/Chat Settings
+DB_PATH=chat_history.db
+MAX_COMPLETION_TOKENS=1000
+MAX_HISTORY_MESSAGES=1000
+SIMILARITY_THRESHOLD=0.7
+TOP_K_RESULTS=5
 ```
 
 ### Installation
 
-1. Sync dependencies with uv:
+1. Clone the repository and sync dependencies:
-```bash
+
-uv sync
+    ```bash
-```
+    uv sync
+    ```
+
+2. Ensure the TTS model files are present in the project root:
+
+   - `kokoro-v1.0.onnx`
+   - `voices-v1.0.bin`
+
+### Running the Bot
 
-2. Run the bot:
 ```bash
-uv run main.py
+uv run python -m vibe_bot.main
 ```
 
 ## How It Works
 
 ### Database Structure
 
-The system uses two SQLite tables:
+The system uses SQLite with three tables:
 
 1. **chat_messages**: Stores message metadata
-   - message_id, user_id, username, content, timestamp, channel_id, guild_id
+    - `message_id`, `user_id`, `username`, `content`, `bot_name`, `timestamp`, `channel_id`, `guild_id`
 
 2. **message_embeddings**: Stores vector embeddings for RAG
-   - message_id, embedding (as binary blob)
+   - `message_id` (PK), `embedding` (binary blob of float32 values)
+
+3. **custom_bots**: Stores custom bot configurations
+   - `bot_name` (PK), `system_prompt`, `created_by`, `created_at`, `is_active`
 
 ### RAG Process
 
-1. When a message is received, it's stored in the database
+1. When a message is sent to a custom bot, it's stored in `chat_messages`
-2. An embedding is generated using OpenAI's embedding API
+2. An embedding is generated via the configured embedding API and stored in `message_embeddings`
-3. The embedding is stored alongside the message
+3. When a new message is sent:
-4. When a new message is sent to the bot:
+   - The system retrieves recent messages from the same user
-   - The system searches for similar messages using vector similarity
+   - It searches for semantically similar messages using cosine similarity on embeddings
-   - Relevant context is retrieved and added to the prompt
+   - Relevant context (user + bot message pairs) is prepended 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
+├── vibe_bot/
-├── database.py          # SQLite database with RAG support
+│   ├── __init__.py            # Package marker
-├── pyproject.toml       # Project dependencies (uv)
+│   ├── main.py                # Main bot application (commands, event handlers)
-├── .env                 # Environment variables
+│   ├── config.py              # Environment variable loading and validation
-├── .venv/               # Virtual environment (created by uv)
+│   ├── database.py            # SQLite database with RAG + CustomBotManager
-└── README.md           # This file
+│   ├── llama_wrapper.py       # OpenAI-compatible API wrappers (chat, images, embeddings)
+│   ├── tts.py                 # Kokoro TTS engine
+│   └── tests/
+│       ├── conftest.py        # Shared test fixtures
+│       ├── test_main.py       # Bot command tests
+│       ├── test_config.py     # Config loading tests
+│       ├── test_database.py   # Database + CustomBotManager tests
+│       ├── test_llama_wrapper.py  # API wrapper tests
+│       └── test_tts.py        # TTS engine tests
+├── pyproject.toml             # Project dependencies (uv)
+├── uv.lock                    # Locked dependency versions
+├── .env                       # Environment variables
+├── kokoro-v1.0.onnx           # Kokoro TTS model
+├── voices-v1.0.bin            # Kokoro voice definitions
+├── Containerfile              # Podman/Docker build file
+└── README.md                  # This file
 ```
 
-## Build
+## Building
 
-### Using uv
+### Local
 
 ```bash
-# Set environment variables
+# Sync dependencies
-export DISCORD_TOKEN=$(cat .token)
+uv sync
-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
+# Run the bot
-uv run main.py
+uv run python -m vibe_bot.main
 ```
 
 ### Container
 
 ```bash
-# Build
+# Build the container image
 podman build -t vibe-bot:latest .
 
-# Run
+# Run with environment file
 podman run --env-file .env localhost/vibe-bot:latest
 ```
 
-## Docs
+## Testing
 
-### Open AI
+Run the full test suite:
 
-Chat
+```bash
+uv run pytest vibe_bot/tests/ -v
+```
 
-<https://developers.openai.com/api/reference/resources/chat/subresources/completions/methods/create>
+Run linters:
 
-Images
+```bash
+# Ruff (linter + formatter)
+uv run ruff check vibe_bot/
 
-<https://developers.openai.com/api/reference/python/resources/images/methods/edit>
+# Mypy (type checking)
+uv run mypy vibe_bot/
 
-## Models
+# Pyright (type checking)
+uv run pyright vibe_bot/
 
-### Qwen3.5
+# Black (formatter check)
+uv run black --check vibe_bot/
+```
 
-> We recommend using the following set of sampling parameters for generation
+## Configuration
 
-- Non-thinking mode for text tasks: temperature=1.0, top_p=1.00, top_k=20, min_p=0.0, presence_penalty=2.0, repetition_penalty=1.0
+| Variable                | Default            | Description                           |
-- Non-thinking mode for VL tasks: temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
+| ----------------------- | ------------------ | ------------------------------------- |
-- Thinking mode for text tasks: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
+| `DISCORD_TOKEN`         | *(required)*       | Discord bot authentication token      |
-- Thinking mode for VL or precise coding (e.g. WebDev) tasks : temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
+| `CHAT_ENDPOINT`         | *(required)*       | OpenAI-compatible chat API URL        |
-
+| `CHAT_MODEL`            | *(required)*       | Model name for chat completions       |
-> Please note that the support for sampling parameters varies according to inference frameworks.
+| `IMAGE_GEN_ENDPOINT`    | *(required)*       | Image generation API URL              |
+| `IMAGE_EDIT_ENDPOINT`   | *(required)*       | Image editing API URL                 |
+| `EMBEDDING_ENDPOINT`    | *(required)*       | Embedding API URL                     |
+| `EMBEDDING_MODEL`       | *(required)*       | Model name for text embeddings        |
+| `MAX_COMPLETION_TOKENS` | `1000`             | Max tokens in LLM responses           |
+| `MAX_HISTORY_MESSAGES`  | `1000`             | Max messages kept in the database     |
+| `SIMILARITY_THRESHOLD`  | `0.7`              | Min cosine similarity for RAG context |
+| `TOP_K_RESULTS`         | `5`                | Number of similar messages retrieved  |
+| `TTS_MODEL_PATH`        | `kokoro-v1.0.onnx` | Path to Kokoro ONNX model file        |
+| `TTS_VOICES_PATH`       | `voices-v1.0.bin`  | Path to Kokoro voices binary file     |
+| `TTS_VOICE`             | `af_sarah`         | Default voice for TTS                 |
+| `TTS_SPEED`             | `1.0`              | Speech speed multiplier               |
+| `DB_PATH`               | `chat_history.db`  | SQLite database file path             |

pyproject.toml

@@ -14,9 +14,47 @@ dependencies = [
     "python-dotenv>=1.2.2",
     "pytest-env>=1.5.0",
     "kokoro-tts>=2.3.1",
+    "mypy>=2.1.0",
+]
+
+[project.optional-dependencies]
+dev = [
+    "pyright>=1.1.398",
+    "mypy>=1.17.0",
+    "black>=25.1.0",
+    "debugpy>=1.8.0",
 ]
 
 [tool.uv]
 required-environments = [
     "sys_platform == 'linux' and platform_machine == 'x86_64'",
 ]
+
+[tool.mypy]
+strict = true
+python_version = "3.13"
+warn_return_any = true
+warn_unused_configs = true
+disallow_untyped_defs = true
+disallow_incomplete_defs = true
+check_untyped_defs = true
+disallow_untyped_decorators = true
+no_implicit_optional = true
+
+[tool.pyright]
+typeCheckingMode = "strict"
+pythonVersion = "3.13"
+reportMissingTypeStubs = false
+reportUnknownVariableType = false
+reportUnknownMemberType = false
+reportUnknownArgumentType = false
+reportPrivateUsage = false
+
+[tool.black]
+line-length = 88
+target-version = ["py313"]
+
+[tool.pytest.ini_options]
+filterwarnings = [
+    "ignore::pytest.PytestUnraisableExceptionWarning",
+]

uv.lock

@@ -1,6 +1,10 @@
 version = 1
 revision = 3
 requires-python = ">=3.13"
+resolution-markers = [
+    "python_full_version >= '3.15'",
+    "python_full_version < '3.15'",
+]
 required-markers = [
     "platform_machine == 'x86_64' and sys_platform == 'linux'",
 ]
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 ]
 
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+]
+
 [[package]]
 name = "pyyaml"
 version = "6.0.3"
@@ -1946,6 +2194,7 @@ source = { virtual = "." }
 dependencies = [
     { name = "discord" },
     { name = "kokoro-tts" },
+    { name = "mypy" },
     { name = "numpy" },
     { name = "openai" },
     { name = "pytest" },
@@ -1955,18 +2204,32 @@ dependencies = [
     { name = "types-requests" },
 ]
 
+[package.optional-dependencies]
+dev = [
+    { name = "black" },
+    { name = "debugpy" },
+    { name = "mypy" },
+    { name = "pyright" },
+]
+
 [package.metadata]
 requires-dist = [
+    { name = "black", marker = "extra == 'dev'", specifier = ">=25.1.0" },
+    { name = "debugpy", marker = "extra == 'dev'", specifier = ">=1.8.0" },
     { name = "discord", specifier = ">=2.3.2" },
     { name = "kokoro-tts", specifier = ">=2.3.1" },
+    { name = "mypy", specifier = ">=2.1.0" },
+    { name = "mypy", marker = "extra == 'dev'", specifier = ">=1.17.0" },
     { name = "numpy", specifier = ">=1.24.0" },
     { name = "openai", specifier = ">=2.24.0" },
+    { name = "pyright", marker = "extra == 'dev'", specifier = ">=1.1.398" },
     { name = "pytest", specifier = ">=9.0.2" },
     { name = "pytest-env", specifier = ">=1.5.0" },
     { name = "python-dotenv", specifier = ">=1.2.2" },
     { name = "requests", specifier = ">=2.32.5" },
     { name = "types-requests", specifier = ">=2.32.4.20260107" },
 ]
+provides-extras = ["dev"]
 
 [[package]]
 name = "yarl"

vibe_bot/__init__.py

@@ -0,0 +1 @@
+"""Vibe Discord Bot package."""

vibe_bot/config.py

@@ -1,91 +1,185 @@
-from dotenv import load_dotenv
+"""Configuration module for the vibe bot."""
-import os
+
+from __future__ import annotations
+
 import logging
+import os
+
+from dotenv import load_dotenv
 
 # Configure logging
 logging.basicConfig(
-    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
 )
 logger = logging.getLogger(__name__)
 
 load_dotenv()
 
 # Discord
-DISCORD_TOKEN = os.getenv("DISCORD_TOKEN", "")
+DISCORD_TOKEN: str = os.getenv("DISCORD_TOKEN", "")
 
 # Endpoints
-CHAT_ENDPOINT = os.getenv("CHAT_ENDPOINT", "")
+CHAT_ENDPOINT: str = os.getenv("CHAT_ENDPOINT", "")
-COMPLETION_ENDPOINT = os.getenv("COMPLETION_ENDPOINT", "")
+COMPLETION_ENDPOINT: str = os.getenv("COMPLETION_ENDPOINT", "")
-IMAGE_GEN_ENDPOINT = os.getenv("IMAGE_GEN_ENDPOINT", "")
+IMAGE_GEN_ENDPOINT: str = os.getenv("IMAGE_GEN_ENDPOINT", "")
-IMAGE_EDIT_ENDPOINT = os.getenv("IMAGE_EDIT_ENDPOINT", "")
+IMAGE_EDIT_ENDPOINT: str = os.getenv("IMAGE_EDIT_ENDPOINT", "")
-EMBEDDING_ENDPOINT = os.getenv("EMBEDDING_ENDPOINT", "")
+EMBEDDING_ENDPOINT: str = os.getenv("EMBEDDING_ENDPOINT", "")
-MAX_COMPLETION_TOKENS = int(os.getenv("MAX_COMPLETION_TOKENS", "1000"))
+MAX_COMPLETION_TOKENS: int = int(os.getenv("MAX_COMPLETION_TOKENS", "1000"))
 
 # API Keys
-CHAT_ENDPOINT_KEY = os.getenv("CHAT_ENDPOINT_KEY", "placeholder")
+CHAT_ENDPOINT_KEY: str = os.getenv("CHAT_ENDPOINT_KEY", "placeholder")
-COMPLETION_ENDPOINT_KEY = os.getenv("COMPLETION_ENDPOINT_KEY", "placeholder")
+COMPLETION_ENDPOINT_KEY: str = os.getenv("COMPLETION_ENDPOINT_KEY", "placeholder")
-IMAGE_GEN_ENDPOINT_KEY = os.getenv("IMAGE_GEN_ENDPOINT_KEY", "placeholder")
+IMAGE_GEN_ENDPOINT_KEY: str = os.getenv("IMAGE_GEN_ENDPOINT_KEY", "placeholder")
-IMAGE_EDIT_ENDPOINT_KEY = os.getenv("IMAGE_EDIT_ENDPOINT_KEY", "placeholder")
+IMAGE_EDIT_ENDPOINT_KEY: str = os.getenv("IMAGE_EDIT_ENDPOINT_KEY", "placeholder")
-EMBEDDING_ENDPOINT_KEY = os.getenv("EMBEDDING_ENDPOINT_KEY", "placeholder")
+EMBEDDING_ENDPOINT_KEY: str = os.getenv("EMBEDDING_ENDPOINT_KEY", "placeholder")
 
 # Models
-CHAT_MODEL = os.getenv("CHAT_MODEL", "")
+CHAT_MODEL: str = os.getenv("CHAT_MODEL", "")
-COMPLETION_MODEL = os.getenv("COMPLETION_MODEL", "")
+COMPLETION_MODEL: str = os.getenv("COMPLETION_MODEL", "")
-IMAGE_GEN_MODEL = os.getenv("IMAGE_GEN_MODEL", "")
+IMAGE_GEN_MODEL: str = os.getenv("IMAGE_GEN_MODEL", "")
-IMAGE_EDIT_MODEL = os.getenv("IMAGE_EDIT_MODEL", "")
+IMAGE_EDIT_MODEL: str = os.getenv("IMAGE_EDIT_MODEL", "")
-EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL", "")
+EMBEDDING_MODEL: str = os.getenv("EMBEDDING_MODEL", "")
 
 # Database and embeddings
-DB_PATH = os.getenv("DB_PATH", "chat_history.db")
+DB_PATH: str = os.getenv("DB_PATH", "chat_history.db")
-EMBEDDING_DIMENSION = 2048
+EMBEDDING_DIMENSION: int = 2048
-MAX_HISTORY_MESSAGES = int(os.getenv("MAX_HISTORY_MESSAGES", "1000"))
+MAX_HISTORY_MESSAGES: int = int(os.getenv("MAX_HISTORY_MESSAGES", "1000"))
-SIMILARITY_THRESHOLD = float(os.getenv("SIMILARITY_THRESHOLD", "0.7"))
+SIMILARITY_THRESHOLD: float = float(os.getenv("SIMILARITY_THRESHOLD", "0.7"))
-TOP_K_RESULTS = int(os.getenv("TOP_K_RESULTS", "5"))
+TOP_K_RESULTS: int = int(os.getenv("TOP_K_RESULTS", "5"))
 
 # Check token
 if not DISCORD_TOKEN:
-    raise Exception("DISCORD_TOKEN required.")
+    msg = "DISCORD_TOKEN required."
+    raise RuntimeError(msg)
 
 # Check endpoints
 if not CHAT_ENDPOINT:
-    raise Exception("CHAT_ENDPOINT required.")
+    endpoint_msg = "CHAT_ENDPOINT required."
+    raise RuntimeError(endpoint_msg)
 
 if not COMPLETION_ENDPOINT:
-    raise Exception("COMPLETION_ENDPOINT required.")
+    endpoint_msg = "COMPLETION_ENDPOINT required."
+    raise RuntimeError(endpoint_msg)
 
 if not IMAGE_GEN_ENDPOINT:
-    raise Exception("IMAGE_GEN_ENDPOINT required.")
+    endpoint_msg = "IMAGE_GEN_ENDPOINT required."
+    raise RuntimeError(endpoint_msg)
 
 if not IMAGE_EDIT_ENDPOINT:
-    raise Exception("IMAGE_EDIT_ENDPOINT required.")
+    endpoint_msg = "IMAGE_EDIT_ENDPOINT required."
+    raise RuntimeError(endpoint_msg)
 
 if not EMBEDDING_ENDPOINT:
-    raise Exception("EMBEDDING_ENDPOINT required.")
+    endpoint_msg = "EMBEDDING_ENDPOINT required."
+    raise RuntimeError(endpoint_msg)
 
 # Check models
 if not CHAT_MODEL:
-    raise Exception("CHAT_MODEL required.")
+    model_msg = "CHAT_MODEL required."
+    raise RuntimeError(model_msg)
 
 if not COMPLETION_MODEL:
-    raise Exception("COMPLETION_MODEL required.")
+    model_msg = "COMPLETION_MODEL required."
+    raise RuntimeError(model_msg)
 
 if not IMAGE_GEN_MODEL:
-    raise Exception("IMAGE_GEN_MODEL required.")
+    model_msg = "IMAGE_GEN_MODEL required."
+    raise RuntimeError(model_msg)
 
 if not IMAGE_EDIT_MODEL:
-    raise Exception("IMAGE_EDIT_MODEL required.")
+    model_msg = "IMAGE_EDIT_MODEL required."
+    raise RuntimeError(model_msg)
 
 if not EMBEDDING_MODEL:
-    raise Exception("EMBEDDING_MODEL required.")
+    model_msg = "EMBEDDING_MODEL required."
+    raise RuntimeError(model_msg)
 
 # TTS
-TTS_MODEL_PATH = os.getenv("TTS_MODEL_PATH", "kokoro-v1.0.onnx")
+TTS_MODEL_PATH: str = os.getenv("TTS_MODEL_PATH", "kokoro-v1.0.onnx")
-TTS_VOICES_PATH = os.getenv("TTS_VOICES_PATH", "voices-v1.0.bin")
+TTS_VOICES_PATH: str = os.getenv("TTS_VOICES_PATH", "voices-v1.0.bin")
-TTS_VOICE = os.getenv("TTS_VOICE", "af_sarah")
+TTS_VOICE: str = os.getenv("TTS_VOICE", "af_sarah")
-TTS_SPEED = float(os.getenv("TTS_SPEED", "1.0"))
+TTS_SPEED: float = float(os.getenv("TTS_SPEED", "1.0"))
 
-logger.info(f"CHAT_ENDPOINT set to {CHAT_ENDPOINT}")
+# Available voices organized by category
-logger.info(f"COMPLETION_ENDPOINT set to {COMPLETION_ENDPOINT}")
+VOICES_LIST: dict[str, dict[str, str | list[str]]] = {
-logger.info(f"IMAGE_GEN_ENDPOINT set to {IMAGE_GEN_ENDPOINT}")
+    "🇺🇸 👩": {
-logger.info(f"IMAGE_EDIT_ENDPOINT set to {IMAGE_EDIT_ENDPOINT}")
+        "language": "en-us",
-logger.info(f"EMBEDDING_ENDPOINT set to {EMBEDDING_ENDPOINT}")
+        "voices": [
+            "af_alloy",
+            "af_aoede",
+            "af_bella",
+            "af_heart",
+            "af_jessica",
+            "af_kore",
+            "af_nicole",
+            "af_nova",
+            "af_river",
+            "af_sarah",
+            "af_sky",
+        ],
+    },
+    "🇺🇸 👨": {
+        "language": "en-us",
+        "voices": [
+            "am_adam",
+            "am_echo",
+            "am_eric",
+            "am_fenrir",
+            "am_liam",
+            "am_michael",
+            "am_onyx",
+            "am_puck",
+        ],
+    },
+    "🇬🇧": {
+        "language": "en-gb",
+        "voices": [
+            "bf_alice",
+            "bf_emma",
+            "bf_isabella",
+            "bf_lily",
+            "bm_daniel",
+            "bm_fable",
+            "bm_george",
+            "bm_lewis",
+        ],
+    },
+    "🇫🇷": {
+        "language": "fr-fr",
+        "voices": ["ff_siwis"],
+    },
+    "🇮🇹": {
+        "language": "it",
+        "voices": ["if_sara", "im_nicola"],
+    },
+    "🇯🇵": {
+        "language": "ja",
+        "voices": [
+            "jf_alpha",
+            "jf_gongitsune",
+            "jf_nezumi",
+            "jf_tebukuro",
+            "jm_kumo",
+        ],
+    },
+    "🇨🇳": {
+        "language": "cmn",
+        "voices": [
+            "zf_xiaobei",
+            "zf_xiaoni",
+            "zf_xiaoxiao",
+            "zf_xiaoyi",
+            "zm_yunjian",
+            "zm_yunxi",
+            "zm_yunxia",
+            "zm_yunyang",
+        ],
+    },
+}
+
+logger.info("CHAT_ENDPOINT set to %s", CHAT_ENDPOINT)
+logger.info("COMPLETION_ENDPOINT set to %s", COMPLETION_ENDPOINT)
+logger.info("IMAGE_GEN_ENDPOINT set to %s", IMAGE_GEN_ENDPOINT)
+logger.info("IMAGE_EDIT_ENDPOINT set to %s", IMAGE_EDIT_ENDPOINT)
+logger.info("EMBEDDING_ENDPOINT set to %s", EMBEDDING_ENDPOINT)

vibe_bot/database.py

@@ -1,43 +1,60 @@
+"""SQLite database with RAG support for chat history and embeddings."""
+
+from __future__ import annotations
+
+import logging
 import sqlite3
-from typing import Optional, List, Tuple
+from typing import TYPE_CHECKING
-from datetime import datetime
+
 import numpy as np
 from openai import OpenAI
-import logging
 
-import llama_wrapper  # type: ignore
+from vibe_bot import llama_wrapper
-from config import (  # type: ignore
+from vibe_bot.config import (
     DB_PATH,
-    EMBEDDING_MODEL,
     EMBEDDING_ENDPOINT,
     EMBEDDING_ENDPOINT_KEY,
+    EMBEDDING_MODEL,
     MAX_HISTORY_MESSAGES,
     SIMILARITY_THRESHOLD,
     TOP_K_RESULTS,
 )
 
+if TYPE_CHECKING:
+    from datetime import datetime
+
 # Configure logging
 logging.basicConfig(
-    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
 )
 logger = logging.getLogger(__name__)
 
 
 class ChatDatabase:
-    """SQLite database with RAG support for storing chat history using OpenAI embeddings."""
+    """SQLite database with RAG support for storing chat history
+    using OpenAI embeddings.
+    """
 
-    def __init__(self, db_path: str = DB_PATH):
+    def __init__(self, db_path: str = DB_PATH) -> None:
-        logger.info(f"Initializing ChatDatabase with path: {db_path}")
+        """Initialize the database connection.
+
+        Args:
+            db_path: Path to the SQLite database file.
+
+        """
+        logger.info("Initializing ChatDatabase with path: %s", db_path)
         self.db_path = db_path
         self.client = OpenAI(
-            base_url=EMBEDDING_ENDPOINT, api_key=EMBEDDING_ENDPOINT_KEY
+            base_url=EMBEDDING_ENDPOINT,
+            api_key=EMBEDDING_ENDPOINT_KEY,
         )
         logger.info("Connecting to OpenAI API for embeddings")
         self._initialize_database()
 
-    def _initialize_database(self):
+    def _initialize_database(self) -> None:
         """Initialize the SQLite database with required tables."""
-        logger.info(f"Initializing SQLite database at {self.db_path}")
+        logger.info("Initializing SQLite database at %s", self.db_path)
         conn = sqlite3.connect(self.db_path)
         cursor = conn.cursor()
 
@@ -51,14 +68,26 @@ class ChatDatabase:
                 user_id TEXT,
                 username TEXT,
                 content TEXT,
+                bot_name TEXT,
                 timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                 channel_id TEXT,
                 guild_id TEXT
             )
-        """
+        """,
         )
         logger.info("chat_messages table initialized successfully")
 
+        # Migrate: add bot_name column if it doesn't exist
+        logger.info("Checking for bot_name column migration")
+        cursor.execute("PRAGMA table_info(chat_messages)")
+        columns = [row[1] for row in cursor.fetchall()]
+        if "bot_name" not in columns:
+            logger.info("Adding bot_name column to chat_messages table")
+            cursor.execute(
+                "ALTER TABLE chat_messages ADD COLUMN bot_name TEXT",
+            )
+            logger.info("bot_name column added successfully")
+
         # Create embeddings table for RAG
         logger.info("Creating message_embeddings table if not exists")
         cursor.execute(
@@ -68,7 +97,7 @@ class ChatDatabase:
                 embedding BLOB,
                 FOREIGN KEY (message_id) REFERENCES chat_messages(message_id)
             )
-        """
+        """,
         )
         logger.info("message_embeddings table initialized successfully")
 
@@ -77,7 +106,7 @@ class ChatDatabase:
         cursor.execute(
             """
             CREATE INDEX IF NOT EXISTS idx_timestamp ON chat_messages(timestamp)
-        """
+        """,
         )
         logger.info("idx_timestamp index created successfully")
 
@@ -85,7 +114,7 @@ class ChatDatabase:
         cursor.execute(
             """
             CREATE INDEX IF NOT EXISTS idx_user_id ON chat_messages(user_id)
-        """
+        """,
         )
         logger.info("idx_user_id index created successfully")
 
@@ -93,60 +122,74 @@ class ChatDatabase:
         logger.info("Database initialization completed successfully")
         conn.close()
 
-    def _vector_to_bytes(self, vector: List[float]) -> bytes:
+    def _vector_to_bytes(self, vector: list[float]) -> bytes:
         """Convert vector to bytes for SQLite storage."""
-        logger.debug(f"Converting vector (length: {len(vector)}) to bytes")
+        logger.debug("Converting vector (length: %d) to bytes", len(vector))
         result = np.array(vector, dtype=np.float32).tobytes()
-        logger.debug(f"Vector converted to {len(result)} bytes")
+        logger.debug("Vector converted to %d bytes", len(result))
         return result
 
     def _bytes_to_vector(self, blob: bytes) -> np.ndarray:
         """Convert bytes back to vector."""
-        logger.debug(f"Converting {len(blob)} bytes back to vector")
+        logger.debug("Converting %d bytes back to vector", len(blob))
         result = np.frombuffer(blob, dtype=np.float32)
-        logger.debug(f"Vector reconstructed with {len(result)} dimensions")
+        logger.debug("Vector reconstructed with %d dimensions", len(result))
         return result
 
     def _calculate_similarity(self, vec1: np.ndarray, vec2: np.ndarray) -> float:
         """Calculate cosine similarity between two vectors."""
         logger.debug(
-            f"Calculating cosine similarity between vectors of dimension {len(vec1)}"
+            "Calculating cosine similarity between vectors of dimension %d",
+            len(vec1),
         )
-        result = np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
+        result = float(
-        logger.debug(f"Similarity calculated: {result:.4f}")
+            np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2)),
+        )
+        logger.debug("Similarity calculated: %.4f", result)
         return result
 
     def add_message(
         self,
+        *,
         message_id: str,
         user_id: str,
         username: str,
         content: str,
-        channel_id: Optional[str] = None,
+        bot_name: str | None = None,
-        guild_id: Optional[str] = None,
+        channel_id: str | None = None,
+        guild_id: str | None = None,
     ) -> bool:
         """Add a message to the database and generate its embedding."""
-        logger.info(f"Adding message {message_id} from user {username}")
+        logger.info("Adding message %s from user %s", message_id, username)
         conn = sqlite3.connect(self.db_path)
         cursor = conn.cursor()
 
         try:
             # Insert message
             logger.debug(
-                f"Inserting message into chat_messages table: message_id={message_id}"
+                "Inserting message into chat_messages table: message_id=%s",
+                message_id,
             )
             cursor.execute(
                 """
                 INSERT OR REPLACE INTO chat_messages
-                (message_id, user_id, username, content, channel_id, guild_id)
+                (message_id, user_id, username, content, bot_name, channel_id, guild_id)
-                VALUES (?, ?, ?, ?, ?, ?)
+                VALUES (?, ?, ?, ?, ?, ?, ?)
             """,
-                (message_id, user_id, username, content, channel_id, guild_id),
+                (
+                    message_id,
+                    user_id,
+                    username,
+                    content,
+                    bot_name,
+                    channel_id,
+                    guild_id,
+                ),
             )
-            logger.debug(f"Message {message_id} inserted into chat_messages table")
+            logger.debug("Message %s inserted into chat_messages table", message_id)
 
             # Generate and store embedding
-            logger.info(f"Generating embedding for message {message_id}")
+            logger.info("Generating embedding for message %s", message_id)
             embedding = llama_wrapper.embedding(
                 content,
                 openai_url=EMBEDDING_ENDPOINT,
@@ -155,7 +198,9 @@ class ChatDatabase:
             )
             if embedding:
                 logger.debug(
-                    f"Embedding generated successfully for message {message_id}, storing in database"
+                    "Embedding generated successfully for message %s, "
+                    "storing in database",
+                    message_id,
                 )
                 cursor.execute(
                     """
@@ -166,11 +211,14 @@ class ChatDatabase:
                     (message_id, self._vector_to_bytes(embedding)),
                 )
                 logger.debug(
-                    f"Embedding stored in message_embeddings table for message {message_id}"
+                    "Embedding stored in message_embeddings table for message %s",
+                    message_id,
                 )
             else:
                 logger.warning(
-                    f"Failed to generate embedding for message {message_id}, skipping embedding storage"
+                    "Failed to generate embedding for message %s, "
+                    "skipping embedding storage",
+                    message_id,
                 )
 
             # Clean up old messages if exceeding limit
@@ -178,22 +226,22 @@ class ChatDatabase:
             self._cleanup_old_messages(cursor)
 
             conn.commit()
-            logger.info(f"Successfully added message {message_id} to database")
+        except Exception:
-            return True
+            logger.exception("Error adding message %s", message_id)
-
-        except Exception as e:
-            logger.error(f"Error adding message {message_id}: {e}")
             conn.rollback()
             return False
+        else:
+            logger.info("Successfully added message %s to database", message_id)
+            return True
         finally:
             conn.close()
 
-    def _cleanup_old_messages(self, cursor):
+    def _cleanup_old_messages(self, cursor: sqlite3.Cursor) -> None:
         """Remove old messages to stay within the limit."""
         cursor.execute(
             """
             SELECT COUNT(*) FROM chat_messages
-        """
+        """,
         )
         count = cursor.fetchone()[0]
 
@@ -224,8 +272,9 @@ class ChatDatabase:
             )
 
     def get_recent_messages(
-        self, limit: int = 10
+        self,
-    ) -> List[Tuple[str, str, str, datetime]]:
+        limit: int = 10,
+    ) -> list[tuple[str, str, str, datetime]]:
         """Get recent messages from the database."""
         conn = sqlite3.connect(self.db_path)
         cursor = conn.cursor()
@@ -250,7 +299,7 @@ class ChatDatabase:
         query: str,
         top_k: int = TOP_K_RESULTS,
         min_similarity: float = SIMILARITY_THRESHOLD,
-    ) -> List[Tuple[str, str, float]]:
+    ) -> list[tuple[str, str, float]]:
         """Search for messages similar to the query using embeddings."""
         query_embedding = llama_wrapper.embedding(
             text=query,
@@ -273,7 +322,7 @@ class ChatDatabase:
             FROM chat_messages cm
             JOIN message_embeddings me ON cm.message_id = me.message_id
             WHERE cm.username != 'vibe-bot'
-        """
+        """,
         )
         rows = cursor.fetchall()
 
@@ -302,30 +351,42 @@ class ChatDatabase:
         results.sort(key=lambda x: x[2], reverse=True)
         return results[:top_k]
 
-    def get_user_history(self, user_id: str, limit: int = 20) -> list[tuple[str, str]]:
+    def get_bot_history(self, bot_name: str, limit: int = 20) -> list[tuple[str, str]]:
-        """Get message history for a specific user."""
+        """Get message history for a specific custom bot.
+
+        Args:
+            bot_name: The name of the custom bot.
+            limit: Maximum number of messages to retrieve.
+
+        Returns:
+            List of (user_message, bot_response) tuples.
+
+        """
         conn = sqlite3.connect(self.db_path)
         cursor = conn.cursor()
 
-        logger.info(f"Fetching last {limit} user messages")
+        logger.info(
+            "Fetching last %d messages for bot %r",
+            limit,
+            bot_name,
+        )
         cursor.execute(
             """
             SELECT message_id, content, timestamp
             FROM chat_messages
-            WHERE username != 'vibe-bot'
+            WHERE bot_name = ? AND message_id NOT LIKE '%%_response'
             ORDER BY timestamp DESC
             LIMIT ?
         """,
-            (limit,),
+            (bot_name, limit),
         )
 
         messages = cursor.fetchall()
 
-        # Format is [user message, bot response]
         conversations: list[tuple[str, str]] = []
         for message in messages:
-            msg_content: str = message[1]
+            msg_content = message[1]
-            logger.info(f"Finding response for {msg_content[:50]}")
+            logger.debug("Finding response for %s...", msg_content[:50])
             cursor.execute(
                 """
                     SELECT content
@@ -335,18 +396,64 @@ class ChatDatabase:
                 """,
                 (f"{message[0]}_response",),
             )
-            response_content: str = cursor.fetchone()
+            response_row = cursor.fetchone()
-            if response_content:
+            if response_row:
-                logger.info(f"Found response: {response_content[0][:50]}")
+                logger.debug("Found response: %s...", response_row[0][:50])
-                conversations.append((msg_content, response_content[0]))
+                conversations.append((msg_content, response_row[0]))
             else:
-                logger.info("No response found")
+                logger.debug("No response found")
+        conn.close()
+
+        return conversations
+
+    def get_user_history(self, user_id: str, limit: int = 20) -> list[tuple[str, str]]:
+        """Get message history for a specific user."""
+        conn = sqlite3.connect(self.db_path)
+        cursor = conn.cursor()
+
+        logger.info("Fetching last %d user messages", limit)
+        cursor.execute(
+            """
+            SELECT message_id, content, timestamp
+            FROM chat_messages
+            WHERE user_id = ? AND username != 'vibe-bot'
+            ORDER BY timestamp DESC
+            LIMIT ?
+        """,
+            (user_id, limit),
+        )
+
+        messages = cursor.fetchall()
+
+        # Format is [user message, bot response]
+        conversations: list[tuple[str, str]] = []
+        for message in messages:
+            msg_content = message[1]
+            logger.debug("Finding response for %s...", msg_content[:50])
+            cursor.execute(
+                """
+                    SELECT content
+                    FROM chat_messages
+                    WHERE message_id = ?
+                    ORDER BY timestamp DESC
+                """,
+                (f"{message[0]}_response",),
+            )
+            response_row = cursor.fetchone()
+            if response_row:
+                logger.debug("Found response: %s...", response_row[0][:50])
+                conversations.append((msg_content, response_row[0]))
+            else:
+                logger.debug("No response found")
         conn.close()
 
         return conversations
 
     def get_conversation_context(
-        self, user_id: str, current_message: str, max_context: int = 5
+        self,
+        user_id: str,
+        current_message: str,
+        max_context: int = 5,
     ) -> list[dict[str, str]]:
         """Get relevant conversation context for RAG."""
         # Get recent messages from the user
@@ -354,7 +461,8 @@ class ChatDatabase:
 
         # Search for similar messages
         similar_messages = self.search_similar_messages(
-            current_message, top_k=max_context
+            current_message,
+            top_k=max_context,
         )
 
         # Combine contexts
@@ -366,7 +474,7 @@ class ChatDatabase:
             context_parts.append({"role": "user", "content": user_message})
 
         # Add similar messages
-        for user_message, bot_message, similarity in similar_messages:
+        for user_message, bot_message, _similarity in similar_messages:
             context_parts.append({"role": "assistant", "content": bot_message})
             context_parts.append({"role": "user", "content": user_message})
 
@@ -374,7 +482,7 @@ class ChatDatabase:
         context_parts.reverse()
         return context_parts
 
-    def clear_all_messages(self):
+    def clear_all_messages(self) -> None:
         """Clear all messages and embeddings from the database."""
         conn = sqlite3.connect(self.db_path)
         cursor = conn.cursor()
@@ -387,12 +495,12 @@ class ChatDatabase:
 
 
 # Global database instance
-_chat_db: Optional[ChatDatabase] = None
+_chat_db: ChatDatabase | None = None
 
 
 def get_database() -> ChatDatabase:
     """Get or create the global database instance."""
-    global _chat_db
+    global _chat_db  # noqa: PLW0603
     if _chat_db is None:
         _chat_db = ChatDatabase()
     return _chat_db
@@ -401,11 +509,17 @@ def get_database() -> ChatDatabase:
 class CustomBotManager:
     """Manages custom bot configurations stored in SQLite database."""
 
-    def __init__(self, db_path: str = DB_PATH):
+    def __init__(self, db_path: str = DB_PATH) -> None:
+        """Initialize the custom bot manager.
+
+        Args:
+            db_path: Path to the SQLite database file.
+
+        """
         self.db_path = db_path
         self._initialize_custom_bots_table()
 
-    def _initialize_custom_bots_table(self):
+    def _initialize_custom_bots_table(self) -> None:
         """Initialize the custom bots table in SQLite."""
         conn = sqlite3.connect(self.db_path)
         cursor = conn.cursor()
@@ -420,14 +534,17 @@ class CustomBotManager:
                 created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                 is_active INTEGER DEFAULT 1
             )
-            """
+            """,
         )
 
         conn.commit()
         conn.close()
 
     def create_custom_bot(
-        self, bot_name: str, system_prompt: str, created_by: str
+        self,
+        bot_name: str,
+        system_prompt: str,
+        created_by: str,
     ) -> bool:
         """Create a new custom bot configuration."""
         conn = sqlite3.connect(self.db_path)
@@ -444,16 +561,16 @@ class CustomBotManager:
             )
 
             conn.commit()
-            return True
+        except Exception:
-
+            logger.exception("Error creating custom bot")
-        except Exception as e:
-            print(f"Error creating custom bot: {e}")
             conn.rollback()
             return False
+        else:
+            return True
         finally:
             conn.close()
 
-    def get_custom_bot(self, bot_name: str) -> Optional[Tuple[str, str, str, datetime]]:
+    def get_custom_bot(self, bot_name: str) -> tuple[str, str, str, datetime] | None:
         """Get a custom bot configuration by name."""
         conn = sqlite3.connect(self.db_path)
         cursor = conn.cursor()
@@ -470,11 +587,14 @@ class CustomBotManager:
         result = cursor.fetchone()
         conn.close()
 
-        return result
+        if result is None:
+            return None
+        return (result[0], result[1], result[2], result[3])
 
     def list_custom_bots(
-        self, user_id: Optional[str] = None
+        self,
-    ) -> List[Tuple[str, str, str]]:
+        user_id: str | None = None,
+    ) -> list[tuple[str, str, str]]:
         """List all custom bots, optionally filtered by creator."""
         conn = sqlite3.connect(self.db_path)
         cursor = conn.cursor()
@@ -482,12 +602,12 @@ class CustomBotManager:
         if user_id:
             cursor.execute(
                 """
-                SELECT bot_name, system_prompt, name
+                SELECT bot_name, system_prompt, created_by
-                FROM custom_bots cb, username_map um
+                FROM custom_bots
-                JOIN username_map ON custom_bots.created_by = username_map.id
+                WHERE is_active = 1 AND created_by = ?
-                WHERE is_active = 1
                 ORDER BY created_at DESC
-            """
+            """,
+                (user_id,),
             )
         else:
             cursor.execute(
@@ -496,7 +616,7 @@ class CustomBotManager:
                 FROM custom_bots
                 WHERE is_active = 1
                 ORDER BY created_at DESC
-            """
+            """,
             )
 
         bots = cursor.fetchall()
@@ -519,12 +639,12 @@ class CustomBotManager:
             )
 
             conn.commit()
-            return cursor.rowcount > 0
+        except Exception:
-
+            logger.exception("Error deleting custom bot")
-        except Exception as e:
-            print(f"Error deleting custom bot: {e}")
             conn.rollback()
             return False
+        else:
+            return cursor.rowcount > 0
         finally:
             conn.close()
 
@@ -544,11 +664,11 @@ class CustomBotManager:
             )
 
             conn.commit()
-            return cursor.rowcount > 0
+        except Exception:
-
+            logger.exception("Error deactivating custom bot")
-        except Exception as e:
-            print(f"Error deactivating custom bot: {e}")
             conn.rollback()
             return False
+        else:
+            return cursor.rowcount > 0
         finally:
             conn.close()

vibe_bot/llama_wrapper.py

@@ -1,23 +1,48 @@
-# Wraps the openai calls in generic functions
+"""Wraps the openai calls in generic functions.
-# Supports chat, image, edit, and embeddings
+
-# Allows custom endpoints for each of the above supported functions
+Supports chat, image, edit, and embeddings.
+Allows custom endpoints for each of the above supported functions.
+"""
+
+from __future__ import annotations
+
+import json
+from typing import TYPE_CHECKING, cast
 
 import openai
-from typing import Iterable
+import requests
-from openai.types.chat import ChatCompletionMessageParam
+
-from io import BufferedReader, BytesIO
+if TYPE_CHECKING:
+    from io import BufferedReader, BytesIO
+
+    from openai.types.chat import ChatCompletionMessageParam
 
 
 def chat_completion(
     system_prompt: str,
     user_prompt: str,
+    *,
     openai_url: str,
     openai_api_key: str,
     model: str,
     max_tokens: int = 1000,
 ) -> str:
+    """Send a chat completion request and return the response.
+
+    Args:
+        system_prompt: The system prompt to use.
+        user_prompt: The user prompt to send.
+        openai_url: The OpenAI-compatible API URL.
+        openai_api_key: The API key for authentication.
+        model: The model to use for completion.
+        max_tokens: Maximum number of tokens to generate.
+
+    Returns:
+        The model's response text, stripped of whitespace.
+
+    """
     client = openai.OpenAI(base_url=openai_url, api_key=openai_api_key)
-    messages: Iterable[ChatCompletionMessageParam] = [
+    messages: list[ChatCompletionMessageParam] = [
         {
             "role": "system",
             "content": system_prompt,
@@ -28,59 +53,97 @@ def chat_completion(
         },
     ]
     response = client.chat.completions.create(
-        model=model, messages=messages, max_tokens=max_tokens
+        model=model,
+        messages=messages,
+        max_tokens=max_tokens,
+        timeout=60.0,
     )
 
-    # Assert that thinking was used
+    if not response.choices:
-    if response.choices[0].message.model_extra:
+        return ""
-        assert response.choices[0].message.model_extra.get("reasoning_content")
 
     content = response.choices[0].message.content
     if content:
         return content.strip()
-    else:
+    return ""
-        return ""
 
 
 def chat_completion_with_history(
     system_prompt: str,
-    prompts: Iterable[ChatCompletionMessageParam],
+    prompts: list[dict[str, str]],
+    *,
     openai_url: str,
     openai_api_key: str,
     model: str,
     max_tokens: int = 1000,
 ) -> str:
+    """Send a chat completion request with conversation history.
+
+    Args:
+        system_prompt: The system prompt to use.
+        prompts: List of prompt dicts with role and content.
+        openai_url: The OpenAI-compatible API URL.
+        openai_api_key: The API key for authentication.
+        model: The model to use for completion.
+        max_tokens: Maximum number of tokens to generate.
+
+    Returns:
+        The model's response text, stripped of whitespace.
+
+    """
     client = openai.OpenAI(base_url=openai_url, api_key=openai_api_key)
-    messages: Iterable[ChatCompletionMessageParam] = [
+    messages: list[ChatCompletionMessageParam] = [
-        {
+        cast(
-            "role": "system",
+            "ChatCompletionMessageParam",
-            "content": system_prompt,
+            {
-        }
+                "role": "system",
-    ] + prompts  # type: ignore
+                "content": system_prompt,
+            },
+        ),
+    ]
+    messages.extend(cast("list[ChatCompletionMessageParam]", prompts))
     response = client.chat.completions.create(
         model=model,
         messages=messages,
         max_tokens=max_tokens,
         seed=-1,
+        timeout=60.0,
     )
 
+    if not response.choices:
+        return ""
+
     content = response.choices[0].message.content
     if content:
         return content.strip()
-    else:
+    return ""
-        return ""
 
 
 def chat_completion_instruct(
     system_prompt: str,
     user_prompt: str,
+    *,
     openai_url: str,
     openai_api_key: str,
     model: str,
     max_tokens: int = 1000,
 ) -> str:
+    """Send an instruction-based chat completion request.
+
+    Args:
+        system_prompt: The system prompt to use.
+        user_prompt: The user prompt to send.
+        openai_url: The OpenAI-compatible API URL.
+        openai_api_key: The API key for authentication.
+        model: The model to use for completion.
+        max_tokens: Maximum number of tokens to generate.
+
+    Returns:
+        The model's response text, stripped of whitespace.
+
+    """
     client = openai.OpenAI(base_url=openai_url, api_key=openai_api_key)
-    messages: Iterable[ChatCompletionMessageParam] = [
+    messages: list[ChatCompletionMessageParam] = [
         {
             "role": "system",
             "content": system_prompt,
@@ -95,65 +158,137 @@ def chat_completion_instruct(
         messages=messages,
         max_tokens=max_tokens,
         seed=-1,
+        timeout=60.0,
     )
 
+    if not response.choices:
+        return ""
+
     content = response.choices[0].message.content
     if content:
         return content.strip()
-    else:
+    return ""
-        return ""
 
 
-def image_generation(prompt: str, openai_url: str, openai_api_key: str, n=1) -> str:
+def image_generation(
-    """Generates an image using the given prompt and returns the base64 encoded image data
+    prompt: str,
+    *,
+    openai_url: str,
+    openai_api_key: str,
+    model: str = "gen",
+    n: int = 1,
+) -> str:
+    """Generate an image using the given prompt.
+
+    Args:
+        prompt: The image generation prompt.
+        openai_url: The OpenAI-compatible API URL.
+        openai_api_key: The API key for authentication.
+        model: The model to use for image generation.
+        n: Number of images to generate.
 
     Returns:
-        str: The base64 encoded image data. Decode and write to a file.
+        The base64 encoded image data. Decode and write to a file.
+
     """
-    client = openai.OpenAI(base_url=openai_url, api_key=openai_api_key)
+    client = openai.OpenAI(
-    response = client.images.generate(
+        base_url=openai_url,
-        prompt=prompt,
+        api_key=openai_api_key,
-        n=n,
+        max_retries=0,
-        size="1024x1024",
     )
+    try:
+        response = client.images.generate(
+            prompt=prompt,
+            n=n,
+            size="1024x1024",
+            model=model,
+            timeout=120.0,
+        )
+    except openai.APIConnectionError:
+        return ""
     if response.data:
         return response.data[0].b64_json or ""
-    else:
+    return ""
-        return ""
 
 
 def image_edit(
     image: BufferedReader | BytesIO | list[BufferedReader] | list[BytesIO],
     prompt: str,
+    *,
     openai_url: str,
     openai_api_key: str,
-    n=1,
+    model: str = "edit",
+    n: int = 1,
 ) -> str:
+    """Edit an existing image using a prompt.
+
+    Args:
+        image: The source image as a file-like object or list thereof.
+        prompt: The edit instruction.
+        openai_url: The OpenAI-compatible API URL.
+        openai_api_key: The API key for authentication.
+        model: The model to use for image editing.
+        n: Number of edited images to generate.
+
+    Returns:
+        The base64 encoded edited image data.
+
+    """
     client = openai.OpenAI(base_url=openai_url, api_key=openai_api_key)
     response = client.images.edit(
         image=image,
         prompt=prompt,
         n=n,
         size="1024x1024",
+        model=model,
     )
     if response.data:
         return response.data[0].b64_json or ""
-    else:
+    return ""
-        return ""
 
 
 def embedding(
-    text: str, openai_url: str, openai_api_key: str, model: str
+    text: str,
+    *,
+    openai_url: str,
+    openai_api_key: str,
+    model: str,
 ) -> list[float]:
-    client = openai.OpenAI(base_url=openai_url, api_key=openai_api_key)
+    """Generate an embedding vector for the given text.
-    response = client.embeddings.create(
+
-        input=[text], model=model, encoding_format="float"
+    Uses a raw HTTP request to avoid the OpenAI SDK injecting
-    )
+    unsupported parameters like encoding_format.
-    if response:
+
-        raw_data = response[0].embedding  # type: ignore
+    Args:
-        # The result could be an array of floats or an array of an array of floats.
+        text: The text to embed.
-        try:
+        openai_url: The OpenAI-compatible API URL.
-            return raw_data[0]
+        openai_api_key: The API key for authentication.
-        except Exception:
+        model: The embedding model to use.
-            return raw_data
+
-    return []
+    Returns:
+        The embedding vector as a list of floats, or an empty list on failure.
+
+    """
+    url = f"{openai_url.rstrip('/')}/embeddings"
+    headers = {
+        "Authorization": f"Bearer {openai_api_key}",
+        "Content-Type": "application/json",
+    }
+    payload = {"model": model, "input": [text]}
+
+    try:
+        resp = requests.post(url, headers=headers, json=payload, timeout=30)
+        resp.raise_for_status()
+    except requests.RequestException:
+        return []
+
+    data = resp.json()
+    if not data.get("data"):
+        return []
+
+    raw = data["data"][0].get("embedding")
+    if isinstance(raw, str):
+        raw = json.loads(raw)
+    if not isinstance(raw, list):
+        raw = list(raw)
+    return list[float](raw)

vibe_bot/tests/__init__.py

@@ -0,0 +1 @@
+"""Tests for the vibe_bot package."""

vibe_bot/tests/conftest.py

@@ -0,0 +1,233 @@
+"""Shared test fixtures for vibe_bot tests."""
+
+from __future__ import annotations
+
+import tempfile
+import warnings
+from collections.abc import Generator
+from pathlib import Path
+from typing import TYPE_CHECKING, Any
+from unittest.mock import MagicMock, patch
+
+import numpy as np
+import pytest
+
+warnings.filterwarnings(
+    "ignore",
+    message="Exception ignored in.*FileIO.*Bad file descriptor",
+)
+
+if TYPE_CHECKING:
+    from vibe_bot.database import ChatDatabase, CustomBotManager
+
+
+@pytest.fixture
+def mock_env_vars() -> Generator[None]:
+    """Provide minimal env vars for config loading."""
+    with patch.dict(
+        "os.environ",
+        {
+            "DISCORD_TOKEN": "test-token",
+            "CHAT_ENDPOINT": "https://chat.example.com/v1",
+            "COMPLETION_ENDPOINT": "https://completion.example.com/v1",
+            "IMAGE_GEN_ENDPOINT": "https://image.example.com/v1",
+            "IMAGE_EDIT_ENDPOINT": "https://image-edit.example.com/v1",
+            "EMBEDDING_ENDPOINT": "https://embedding.example.com/v1",
+            "CHAT_MODEL": "test-chat-model",
+            "COMPLETION_MODEL": "test-completion-model",
+            "IMAGE_GEN_MODEL": "test-image-model",
+            "IMAGE_EDIT_MODEL": "test-image-edit-model",
+            "EMBEDDING_MODEL": "test-embedding-model",
+            "CHAT_ENDPOINT_KEY": "test-key",
+            "COMPLETION_ENDPOINT_KEY": "test-completion-key",
+            "IMAGE_GEN_ENDPOINT_KEY": "test-image-key",
+            "IMAGE_EDIT_ENDPOINT_KEY": "test-image-edit-key",
+            "EMBEDDING_ENDPOINT_KEY": "test-embedding-key",
+            "MAX_COMPLETION_TOKENS": "1000",
+            "MAX_HISTORY_MESSAGES": "1000",
+            "SIMILARITY_THRESHOLD": "0.7",
+            "TOP_K_RESULTS": "5",
+            "TTS_MODEL_PATH": "/tmp/test-model.onnx",
+            "TTS_VOICES_PATH": "/tmp/test-voices.bin",
+            "TTS_VOICE": "af_sarah",
+            "TTS_SPEED": "1.0",
+            "DB_PATH": ":memory:",
+        },
+        clear=False,
+    ):
+        yield
+
+
+@pytest.fixture
+def temp_db_path() -> Generator[str]:
+    """Provide a temporary SQLite database path."""
+    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
+        path = f.name
+    yield path
+    Path(path).unlink(missing_ok=True)
+
+
+@pytest.fixture
+def mock_embedding() -> Generator[MagicMock]:
+    """Provide a mock embedding function returning a fixed vector."""
+    vector: list[float] = [0.1] * 2048
+    with patch("vibe_bot.llama_wrapper.embedding", return_value=vector) as mock:
+        yield mock
+
+
+@pytest.fixture
+def mock_openai_client() -> Generator[MagicMock]:
+    """Provide a mock OpenAI client."""
+    mock_client = MagicMock()
+    with patch("vibe_bot.database.OpenAI", return_value=mock_client) as mock:
+        yield mock
+
+
+@pytest.fixture
+def chat_db(
+    temp_db_path: str,
+    mock_openai_client: MagicMock,
+    mock_embedding: MagicMock,
+) -> Generator[ChatDatabase]:
+    """Provide a ChatDatabase instance with a temp database."""
+    from vibe_bot.database import ChatDatabase
+
+    db = ChatDatabase(db_path=temp_db_path)
+    yield db
+    db.client.close()
+
+
+@pytest.fixture
+def custom_bot_manager(temp_db_path: str) -> CustomBotManager:
+    """Provide a CustomBotManager instance with a temp database."""
+    from vibe_bot.database import CustomBotManager
+
+    manager = CustomBotManager(db_path=temp_db_path)
+    return manager  # noqa: RET504
+
+
+@pytest.fixture
+def mock_kokoro_tts() -> Generator[dict[str, Any]]:
+    """Provide mock Kokoro TTS components."""
+    mock_kokoro = MagicMock()
+    mock_kokoro_instance = MagicMock()
+    mock_chunk = MagicMock()
+    mock_chunk.return_value = ["hello world", "this is a test"]
+
+    mock_samples = np.array([0.1, 0.2, 0.3], dtype=np.float32)
+    mock_process = MagicMock(return_value=(mock_samples, 24000))
+
+    with (
+        patch(
+            "vibe_bot.tts.Kokoro",
+            return_value=mock_kokoro_instance,
+        ),
+        patch("vibe_bot.tts.chunk_text", mock_chunk),
+    ):
+        with patch("vibe_bot.tts.process_chunk_sequential", mock_process):
+            yield {
+                "Kokoro": mock_kokoro,
+                "chunk_text": mock_chunk,
+                "process_chunk_sequential": mock_process,
+                "kokoro_instance": mock_kokoro_instance,
+                "mock_samples": mock_samples,
+                "mock_sr": 24000,
+            }
+
+
+@pytest.fixture
+def mock_discord() -> Generator[dict[str, MagicMock]]:
+    """Mock discord module components."""
+    mock_intents = MagicMock()
+    mock_intents.default.return_value = MagicMock()
+    mock_intents.default.return_value.message_content = True
+
+    mock_bot_class = MagicMock()
+    mock_bot_instance = MagicMock()
+    mock_bot_instance.user = MagicMock()
+    mock_bot_instance.user.name = "test-bot"
+    mock_bot_instance.user.id = "123456789"
+
+    with patch("vibe_bot.main.discord") as mock_discord_module:
+        with patch("vibe_bot.main.commands", MagicMock()):
+            with patch("vibe_bot.main.commands.Bot", mock_bot_class):
+                mock_bot_class.return_value = mock_bot_instance
+                mock_discord_module.Intents = mock_intents
+                mock_discord_module.Message = MagicMock
+                mock_discord_module.File = MagicMock
+                yield {
+                    "Intents": mock_intents,
+                    "Bot": mock_bot_class,
+                    "bot_instance": mock_bot_instance,
+                }
+
+
+@pytest.fixture
+def mock_tts_engine() -> Generator[MagicMock]:
+    """Provide a mock TTSEngine."""
+    mock_engine = MagicMock()
+    mock_engine.generate_audio.return_value = MagicMock()
+    with patch("vibe_bot.main.tts_engine", mock_engine):
+        with patch("vibe_bot.main.tts.TTSEngine", return_value=mock_engine):
+            yield mock_engine
+
+
+@pytest.fixture
+def mock_requests() -> Generator[MagicMock]:
+    """Provide mock requests module."""
+    with patch("vibe_bot.main.requests") as mock_requests_module:
+        mock_response = MagicMock()
+        mock_response.content = b"fake image data"
+        mock_requests_module.get.return_value = mock_response
+        yield mock_requests_module
+
+
+@pytest.fixture
+def mock_base64() -> Generator[MagicMock]:
+    """Provide mock base64 module."""
+    with patch("vibe_bot.main.base64") as mock_base64_module:
+        mock_base64_module.b64decode.return_value = b"fake image data"
+        yield mock_base64_module
+
+
+@pytest.fixture
+def mock_llama_wrapper() -> Generator[MagicMock]:
+    """Provide mock llama_wrapper module."""
+    with patch("vibe_bot.main.llama_wrapper") as mock_wrapper:
+        mock_wrapper.chat_completion_with_history.return_value = "Bot response"
+        mock_wrapper.chat_completion_instruct.return_value = "image prompt"
+        mock_wrapper.image_generation.return_value = ""
+        mock_wrapper.image_edit.return_value = ""
+        mock_wrapper.embedding.return_value = [0.1] * 2048
+        yield mock_wrapper
+
+
+@pytest.fixture
+def mock_database() -> Generator[MagicMock]:
+    """Provide mock database module."""
+    with patch("vibe_bot.main.get_database") as mock_get_db:
+        mock_db = MagicMock()
+        mock_db.get_conversation_context.return_value = []
+        mock_db.add_message.return_value = True
+        mock_get_db.return_value = mock_db
+        yield mock_db
+
+
+@pytest.fixture
+def mock_custom_bot_manager() -> Generator[MagicMock]:
+    """Provide mock CustomBotManager."""
+    with patch("vibe_bot.main.CustomBotManager") as mock_manager_class:
+        mock_manager = MagicMock()
+        mock_manager.create_custom_bot.return_value = True
+        mock_manager.get_custom_bot.return_value = (
+            "alfred",
+            "british butler personality",
+            "user123",
+            "2024-01-01",
+        )
+        mock_manager.list_custom_bots.return_value = [
+            ("alfred", "british butler personality", "user123"),
+        ]
+        mock_manager.delete_custom_bot.return_value = True
+        mock_manager_class.return_value = mock_manager
+        yield mock_manager

vibe_bot/tests/test_config.py

@@ -0,0 +1,324 @@
+"""Tests for the config module."""
+
+from __future__ import annotations
+
+import subprocess
+import sys
+
+
+def test_config_defaults() -> None:
+    """Test that config loads with expected default values."""
+    env_str = ""
+    for k, v in {
+        "DISCORD_TOKEN": "test-token",
+        "CHAT_ENDPOINT": "https://chat.example.com/v1",
+        "COMPLETION_ENDPOINT": "https://completion.example.com/v1",
+        "IMAGE_GEN_ENDPOINT": "https://image.example.com/v1",
+        "IMAGE_EDIT_ENDPOINT": "https://image-edit.example.com/v1",
+        "EMBEDDING_ENDPOINT": "https://embedding.example.com/v1",
+        "CHAT_MODEL": "test-chat-model",
+        "COMPLETION_MODEL": "test-completion-model",
+        "IMAGE_GEN_MODEL": "test-image-model",
+        "IMAGE_EDIT_MODEL": "test-image-edit-model",
+        "EMBEDDING_MODEL": "test-embedding-model",
+        "CHAT_ENDPOINT_KEY": "test-key",
+        "COMPLETION_ENDPOINT_KEY": "test-completion-key",
+        "IMAGE_GEN_ENDPOINT_KEY": "test-image-key",
+        "IMAGE_EDIT_ENDPOINT_KEY": "test-image-edit-key",
+        "EMBEDDING_ENDPOINT_KEY": "test-embedding-key",
+        "MAX_COMPLETION_TOKENS": "1000",
+        "MAX_HISTORY_MESSAGES": "1000",
+        "SIMILARITY_THRESHOLD": "0.7",
+        "TOP_K_RESULTS": "5",
+        "TTS_MODEL_PATH": "/tmp/test-model.onnx",
+        "TTS_VOICES_PATH": "/tmp/test-voices.bin",
+        "TTS_VOICE": "af_sarah",
+        "TTS_SPEED": "1.0",
+        "DB_PATH": ":memory:",
+    }.items():
+        env_str += f'os.environ["{k}"] = "{v}"\n'
+
+    code = f"""
+import sys
+sys.path.insert(0, "/var/home/ducoterra/Projects/vibe_discord_bots")
+import os
+os.environ.clear()
+os.environ["PATH"] = "/usr/bin:/bin"
+{env_str}
+import vibe_bot.config
+assert vibe_bot.config.DISCORD_TOKEN == "test-token"
+assert vibe_bot.config.CHAT_ENDPOINT == "https://chat.example.com/v1"
+assert vibe_bot.config.COMPLETION_ENDPOINT == "https://completion.example.com/v1"
+assert vibe_bot.config.IMAGE_GEN_ENDPOINT == "https://image.example.com/v1"
+assert vibe_bot.config.IMAGE_EDIT_ENDPOINT == "https://image-edit.example.com/v1"
+assert vibe_bot.config.EMBEDDING_ENDPOINT == "https://embedding.example.com/v1"
+assert vibe_bot.config.CHAT_MODEL == "test-chat-model"
+assert vibe_bot.config.COMPLETION_MODEL == "test-completion-model"
+assert vibe_bot.config.IMAGE_GEN_MODEL == "test-image-model"
+assert vibe_bot.config.IMAGE_EDIT_MODEL == "test-image-edit-model"
+assert vibe_bot.config.EMBEDDING_MODEL == "test-embedding-model"
+assert vibe_bot.config.MAX_COMPLETION_TOKENS == 1000
+assert vibe_bot.config.MAX_HISTORY_MESSAGES == 1000
+assert vibe_bot.config.SIMILARITY_THRESHOLD == 0.7
+assert vibe_bot.config.TOP_K_RESULTS == 5
+assert vibe_bot.config.TTS_MODEL_PATH == "/tmp/test-model.onnx"
+assert vibe_bot.config.TTS_VOICES_PATH == "/tmp/test-voices.bin"
+assert vibe_bot.config.TTS_VOICE == "af_sarah"
+assert vibe_bot.config.TTS_SPEED == 1.0
+print("OK")
+"""
+
+    result = subprocess.run(  # noqa: PLW1510, S603
+        [sys.executable, "-c", code],
+        capture_output=True,
+        text=True,
+        timeout=30,
+    )
+    assert result.returncode == 0, f"Subprocess failed: {result.stderr}"
+
+
+def _run_config_check(env_vars: dict[str, str], expected_error: str) -> None:
+    """Run a subprocess that imports config and checks for expected RuntimeError."""
+    env_str = ""
+    for k, v in env_vars.items():
+        env_str += f'os.environ["{k}"] = "{v}"\n'
+
+    code = f"""
+import sys
+sys.path.insert(0, "/var/home/ducoterra/Projects/vibe_discord_bots")
+import os
+os.environ.clear()
+os.environ["PATH"] = "/usr/bin:/bin"
+{env_str}
+try:
+    import vibe_bot.config
+    print("NO_ERROR")
+except RuntimeError as e:
+    print(f"ERROR: {{e}}")
+except Exception as e:
+    print(f"OTHER: {{type(e).__name__}}: {{e}}")
+"""
+
+    result = subprocess.run(  # noqa: PLW1510, S603
+        [sys.executable, "-c", code],
+        capture_output=True,
+        text=True,
+        timeout=30,
+    )
+    output = result.stdout.strip()
+    assert (
+        output.startswith("ERROR:") and expected_error in output
+    ), f"Expected error '{expected_error}' but got: {output}"
+
+
+def test_config_missing_discord_token() -> None:
+    """Test that RuntimeError is raised when DISCORD_TOKEN is missing."""
+    env: dict[str, str] = {
+        "DISCORD_TOKEN": "",
+        "CHAT_ENDPOINT": "https://chat.example.com/v1",
+        "COMPLETION_ENDPOINT": "https://completion.example.com/v1",
+        "IMAGE_GEN_ENDPOINT": "https://image.example.com/v1",
+        "IMAGE_EDIT_ENDPOINT": "https://image-edit.example.com/v1",
+        "EMBEDDING_ENDPOINT": "https://embedding.example.com/v1",
+        "CHAT_MODEL": "test-chat-model",
+        "COMPLETION_MODEL": "test-completion-model",
+        "IMAGE_GEN_MODEL": "test-image-model",
+        "IMAGE_EDIT_MODEL": "test-image-edit-model",
+        "EMBEDDING_MODEL": "test-embedding-model",
+    }
+    _run_config_check(env, "DISCORD_TOKEN required")
+
+
+def test_config_missing_chat_endpoint() -> None:
+    """Test that RuntimeError is raised when CHAT_ENDPOINT is missing."""
+    env: dict[str, str] = {
+        "DISCORD_TOKEN": "test-token",
+        "CHAT_ENDPOINT": "",
+        "COMPLETION_ENDPOINT": "https://completion.example.com/v1",
+        "IMAGE_GEN_ENDPOINT": "https://image.example.com/v1",
+        "IMAGE_EDIT_ENDPOINT": "https://image-edit.example.com/v1",
+        "EMBEDDING_ENDPOINT": "https://embedding.example.com/v1",
+        "CHAT_MODEL": "test-chat-model",
+        "COMPLETION_MODEL": "test-completion-model",
+        "IMAGE_GEN_MODEL": "test-image-model",
+        "IMAGE_EDIT_MODEL": "test-image-edit-model",
+        "EMBEDDING_MODEL": "test-embedding-model",
+    }
+    _run_config_check(env, "CHAT_ENDPOINT required")
+
+
+def test_config_missing_completion_endpoint() -> None:
+    """Test that RuntimeError is raised when COMPLETION_ENDPOINT is missing."""
+    env: dict[str, str] = {
+        "DISCORD_TOKEN": "test-token",
+        "CHAT_ENDPOINT": "https://chat.example.com/v1",
+        "COMPLETION_ENDPOINT": "",
+        "IMAGE_GEN_ENDPOINT": "https://image.example.com/v1",
+        "IMAGE_EDIT_ENDPOINT": "https://image-edit.example.com/v1",
+        "EMBEDDING_ENDPOINT": "https://embedding.example.com/v1",
+        "CHAT_MODEL": "test-chat-model",
+        "COMPLETION_MODEL": "test-completion-model",
+        "IMAGE_GEN_MODEL": "test-image-model",
+        "IMAGE_EDIT_MODEL": "test-image-edit-model",
+        "EMBEDDING_MODEL": "test-embedding-model",
+    }
+    _run_config_check(env, "COMPLETION_ENDPOINT required")
+
+
+def test_config_missing_image_gen_endpoint() -> None:
+    """Test that RuntimeError is raised when IMAGE_GEN_ENDPOINT is missing."""
+    env: dict[str, str] = {
+        "DISCORD_TOKEN": "test-token",
+        "CHAT_ENDPOINT": "https://chat.example.com/v1",
+        "COMPLETION_ENDPOINT": "https://completion.example.com/v1",
+        "IMAGE_GEN_ENDPOINT": "",
+        "IMAGE_EDIT_ENDPOINT": "https://image-edit.example.com/v1",
+        "EMBEDDING_ENDPOINT": "https://embedding.example.com/v1",
+        "CHAT_MODEL": "test-chat-model",
+        "COMPLETION_MODEL": "test-completion-model",
+        "IMAGE_GEN_MODEL": "test-image-model",
+        "IMAGE_EDIT_MODEL": "test-image-edit-model",
+        "EMBEDDING_MODEL": "test-embedding-model",
+    }
+    _run_config_check(env, "IMAGE_GEN_ENDPOINT required")
+
+
+def test_config_missing_image_edit_endpoint() -> None:
+    """Test that RuntimeError is raised when IMAGE_EDIT_ENDPOINT is missing."""
+    env: dict[str, str] = {
+        "DISCORD_TOKEN": "test-token",
+        "CHAT_ENDPOINT": "https://chat.example.com/v1",
+        "COMPLETION_ENDPOINT": "https://completion.example.com/v1",
+        "IMAGE_GEN_ENDPOINT": "https://image.example.com/v1",
+        "IMAGE_EDIT_ENDPOINT": "",
+        "EMBEDDING_ENDPOINT": "https://embedding.example.com/v1",
+        "CHAT_MODEL": "test-chat-model",
+        "COMPLETION_MODEL": "test-completion-model",
+        "IMAGE_GEN_MODEL": "test-image-model",
+        "IMAGE_EDIT_MODEL": "test-image-edit-model",
+        "EMBEDDING_MODEL": "test-embedding-model",
+    }
+    _run_config_check(env, "IMAGE_EDIT_ENDPOINT required")
+
+
+def test_config_missing_embedding_endpoint() -> None:
+    """Test that RuntimeError is raised when EMBEDDING_ENDPOINT is missing."""
+    env: dict[str, str] = {
+        "DISCORD_TOKEN": "test-token",
+        "CHAT_ENDPOINT": "https://chat.example.com/v1",
+        "COMPLETION_ENDPOINT": "https://completion.example.com/v1",
+        "IMAGE_GEN_ENDPOINT": "https://image.example.com/v1",
+        "IMAGE_EDIT_ENDPOINT": "https://image-edit.example.com/v1",
+        "EMBEDDING_ENDPOINT": "",
+        "CHAT_MODEL": "test-chat-model",
+        "COMPLETION_MODEL": "test-completion-model",
+        "IMAGE_GEN_MODEL": "test-image-model",
+        "IMAGE_EDIT_MODEL": "test-image-edit-model",
+        "EMBEDDING_MODEL": "test-embedding-model",
+    }
+    _run_config_check(env, "EMBEDDING_ENDPOINT required")
+
+
+def test_config_missing_chat_model() -> None:
+    """Test that RuntimeError is raised when CHAT_MODEL is missing."""
+    env: dict[str, str] = {
+        "DISCORD_TOKEN": "test-token",
+        "CHAT_ENDPOINT": "https://chat.example.com/v1",
+        "COMPLETION_ENDPOINT": "https://completion.example.com/v1",
+        "IMAGE_GEN_ENDPOINT": "https://image.example.com/v1",
+        "IMAGE_EDIT_ENDPOINT": "https://image-edit.example.com/v1",
+        "EMBEDDING_ENDPOINT": "https://embedding.example.com/v1",
+        "CHAT_MODEL": "",
+        "COMPLETION_MODEL": "test-completion-model",
+        "IMAGE_GEN_MODEL": "test-image-model",
+        "IMAGE_EDIT_MODEL": "test-image-edit-model",
+        "EMBEDDING_MODEL": "test-embedding-model",
+    }
+    _run_config_check(env, "CHAT_MODEL required")
+
+
+def test_config_missing_completion_model() -> None:
+    """Test that RuntimeError is raised when COMPLETION_MODEL is missing."""
+    env: dict[str, str] = {
+        "DISCORD_TOKEN": "test-token",
+        "CHAT_ENDPOINT": "https://chat.example.com/v1",
+        "COMPLETION_ENDPOINT": "https://completion.example.com/v1",
+        "IMAGE_GEN_ENDPOINT": "https://image.example.com/v1",
+        "IMAGE_EDIT_ENDPOINT": "https://image-edit.example.com/v1",
+        "EMBEDDING_ENDPOINT": "https://embedding.example.com/v1",
+        "CHAT_MODEL": "test-chat-model",
+        "COMPLETION_MODEL": "",
+        "IMAGE_GEN_MODEL": "test-image-model",
+        "IMAGE_EDIT_MODEL": "test-image-edit-model",
+        "EMBEDDING_MODEL": "test-embedding-model",
+    }
+    _run_config_check(env, "COMPLETION_MODEL required")
+
+
+def test_config_missing_image_gen_model() -> None:
+    """Test that RuntimeError is raised when IMAGE_GEN_MODEL is missing."""
+    env: dict[str, str] = {
+        "DISCORD_TOKEN": "test-token",
+        "CHAT_ENDPOINT": "https://chat.example.com/v1",
+        "COMPLETION_ENDPOINT": "https://completion.example.com/v1",
+        "IMAGE_GEN_ENDPOINT": "https://image.example.com/v1",
+        "IMAGE_EDIT_ENDPOINT": "https://image-edit.example.com/v1",
+        "EMBEDDING_ENDPOINT": "https://embedding.example.com/v1",
+        "CHAT_MODEL": "test-chat-model",
+        "COMPLETION_MODEL": "test-completion-model",
+        "IMAGE_GEN_MODEL": "",
+        "IMAGE_EDIT_MODEL": "test-image-edit-model",
+        "EMBEDDING_MODEL": "test-embedding-model",
+    }
+    _run_config_check(env, "IMAGE_GEN_MODEL required")
+
+
+def test_config_missing_image_edit_model() -> None:
+    """Test that RuntimeError is raised when IMAGE_EDIT_MODEL is missing."""
+    env: dict[str, str] = {
+        "DISCORD_TOKEN": "test-token",
+        "CHAT_ENDPOINT": "https://chat.example.com/v1",
+        "COMPLETION_ENDPOINT": "https://completion.example.com/v1",
+        "IMAGE_GEN_ENDPOINT": "https://image.example.com/v1",
+        "IMAGE_EDIT_ENDPOINT": "https://image-edit.example.com/v1",
+        "EMBEDDING_ENDPOINT": "https://embedding.example.com/v1",
+        "CHAT_MODEL": "test-chat-model",
+        "COMPLETION_MODEL": "test-completion-model",
+        "IMAGE_GEN_MODEL": "test-image-model",
+        "IMAGE_EDIT_MODEL": "",
+        "EMBEDDING_MODEL": "test-embedding-model",
+    }
+    _run_config_check(env, "IMAGE_EDIT_MODEL required")
+
+
+def test_config_missing_embedding_model() -> None:
+    """Test that RuntimeError is raised when EMBEDDING_MODEL is missing."""
+    env: dict[str, str] = {
+        "DISCORD_TOKEN": "test-token",
+        "CHAT_ENDPOINT": "https://chat.example.com/v1",
+        "COMPLETION_ENDPOINT": "https://completion.example.com/v1",
+        "IMAGE_GEN_ENDPOINT": "https://image.example.com/v1",
+        "IMAGE_EDIT_ENDPOINT": "https://image-edit.example.com/v1",
+        "EMBEDDING_ENDPOINT": "https://embedding.example.com/v1",
+        "CHAT_MODEL": "test-chat-model",
+        "COMPLETION_MODEL": "test-completion-model",
+        "IMAGE_GEN_MODEL": "test-image-model",
+        "IMAGE_EDIT_MODEL": "test-image-edit-model",
+        "EMBEDDING_MODEL": "",
+    }
+    _run_config_check(env, "EMBEDDING_MODEL required")
+
+
+def test_config_logging_exists() -> None:
+    """Test that logging is configured in config module."""
+    from vibe_bot.config import logger
+
+    assert logger is not None
+    assert logger.name == "vibe_bot.config"
+
+
+def test_config_embedding_dimension() -> None:
+    """Test that EMBEDDING_DIMENSION has expected default value."""
+    from vibe_bot.config import EMBEDDING_DIMENSION
+
+    assert EMBEDDING_DIMENSION == 2048

vibe_bot/tests/test_database.py

@@ -0,0 +1,486 @@
+"""Tests for the database module."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+from unittest.mock import MagicMock, patch
+
+import numpy as np
+import pytest
+
+if TYPE_CHECKING:
+
+    from vibe_bot.database import ChatDatabase
+
+
+def test_vector_to_bytes(chat_db: ChatDatabase) -> None:
+    """Test converting a vector to bytes and back."""
+    vector: list[float] = [0.1, 0.2, 0.3, 0.4]
+    blob = chat_db._vector_to_bytes(vector)
+    assert isinstance(blob, bytes)
+    assert len(blob) == len(vector) * 4  # float32 = 4 bytes
+
+    reconstructed = chat_db._bytes_to_vector(blob)
+    assert np.allclose(reconstructed, np.array(vector, dtype=np.float32))
+
+
+def test_bytes_to_vector(chat_db: ChatDatabase) -> None:
+    """Test converting bytes back to a numpy vector."""
+    original = np.array([1.0, 2.0, 3.0], dtype=np.float32)
+    blob = original.tobytes()
+    result = chat_db._bytes_to_vector(blob)
+    assert np.array_equal(result, original)
+
+
+def test_calculate_similarity_self(chat_db: ChatDatabase) -> None:
+    """Test cosine similarity of a vector with itself is 1.0."""
+    vec = np.array([1.0, 2.0, 3.0], dtype=np.float32)
+    similarity = chat_db._calculate_similarity(vec, vec)
+    assert similarity == pytest.approx(1.0, abs=1e-6)
+
+
+def test_calculate_similarity_orthogonal(chat_db: ChatDatabase) -> None:
+    """Test cosine similarity of orthogonal vectors is 0."""
+    vec1 = np.array([1.0, 0.0], dtype=np.float32)
+    vec2 = np.array([0.0, 1.0], dtype=np.float32)
+    similarity = chat_db._calculate_similarity(vec1, vec2)
+    assert similarity == pytest.approx(0.0, abs=1e-6)
+
+
+def test_calculate_similarity_negative(chat_db: ChatDatabase) -> None:
+    """Test cosine similarity of opposite vectors is -1."""
+    vec1 = np.array([1.0, 0.0], dtype=np.float32)
+    vec2 = np.array([-1.0, 0.0], dtype=np.float32)
+    similarity = chat_db._calculate_similarity(vec1, vec2)
+    assert similarity == pytest.approx(-1.0, abs=1e-6)
+
+
+def test_add_message(chat_db: ChatDatabase, mock_embedding: MagicMock) -> None:
+    """Test adding a message to the database."""
+    result = chat_db.add_message(
+        message_id="msg-1",
+        user_id="user-1",
+        username="testuser",
+        content="Hello world",
+        channel_id="chan-1",
+        guild_id="guild-1",
+    )
+    assert result is True
+
+    messages = chat_db.get_recent_messages(limit=10)
+    assert len(messages) == 1
+    assert messages[0][0] == "msg-1"
+    assert messages[0][1] == "testuser"
+    assert messages[0][2] == "Hello world"
+
+
+def test_add_message_no_embedding(chat_db: ChatDatabase) -> None:
+    """Test adding a message when embedding generation fails."""
+    with patch("vibe_bot.llama_wrapper.embedding", return_value=None):
+        result = chat_db.add_message(
+            message_id="msg-no-embed",
+            user_id="user-1",
+            username="testuser",
+            content="No embedding message",
+            channel_id="chan-1",
+            guild_id="guild-1",
+        )
+    assert result is True
+
+
+def test_add_message_duplicate(
+    chat_db: ChatDatabase,
+    mock_embedding: MagicMock,
+) -> None:
+    """Test adding a duplicate message replaces the old one."""
+    chat_db.add_message(
+        message_id="msg-dup",
+        user_id="user-1",
+        username="testuser",
+        content="First content",
+    )
+    chat_db.add_message(
+        message_id="msg-dup",
+        user_id="user-1",
+        username="testuser",
+        content="Second content",
+    )
+
+    messages = chat_db.get_recent_messages(limit=10)
+    assert len(messages) == 1
+    assert messages[0][2] == "Second content"
+
+
+def test_add_message_failure(chat_db: ChatDatabase) -> None:
+    """Test that add_message returns False on database error."""
+    with patch.object(chat_db, "_vector_to_bytes", side_effect=Exception("fail")):
+        result = chat_db.add_message(
+            message_id="msg-fail",
+            user_id="user-1",
+            username="testuser",
+            content="Should fail",
+        )
+    assert result is False
+
+
+def test_get_recent_messages(
+    chat_db: ChatDatabase,
+    mock_embedding: MagicMock,
+) -> None:
+    """Test retrieving recent messages."""
+    chat_db.add_message(
+        message_id="msg-1",
+        user_id="u1",
+        username="alice",
+        content="First",
+    )
+    chat_db.add_message(
+        message_id="msg-2",
+        user_id="u2",
+        username="bob",
+        content="Second",
+    )
+    chat_db.add_message(
+        message_id="msg-3",
+        user_id="u1",
+        username="alice",
+        content="Third",
+    )
+
+    messages = chat_db.get_recent_messages(limit=2)
+    assert len(messages) == 2
+    assert messages[0][2] == "Third"
+    assert messages[1][2] == "Second"
+
+
+def test_get_recent_messages_limit(
+    chat_db: ChatDatabase,
+    mock_embedding: MagicMock,
+) -> None:
+    """Test that get_recent_messages respects the limit."""
+    for i in range(5):
+        chat_db.add_message(
+            message_id=f"msg-{i}",
+            user_id="u1",
+            username="alice",
+            content=f"Message {i}",
+        )
+
+    messages = chat_db.get_recent_messages(limit=3)
+    assert len(messages) == 3
+
+
+def test_clear_all_messages(
+    chat_db: ChatDatabase,
+    mock_embedding: MagicMock,
+) -> None:
+    """Test clearing all messages."""
+    chat_db.add_message(
+        message_id="msg-1",
+        user_id="u1",
+        username="alice",
+        content="Hello",
+    )
+    chat_db.add_message(
+        message_id="msg-2",
+        user_id="u2",
+        username="bob",
+        content="World",
+    )
+
+    chat_db.clear_all_messages()
+
+    messages = chat_db.get_recent_messages(limit=10)
+    assert len(messages) == 0
+
+
+def test_get_user_history(
+    chat_db: ChatDatabase,
+    mock_embedding: MagicMock,
+) -> None:
+    """Test retrieving user message history."""
+    chat_db.add_message(
+        message_id="msg-1",
+        user_id="u1",
+        username="alice",
+        content="User question",
+    )
+    chat_db.add_message(
+        message_id="msg-1_response",
+        user_id="bot",
+        username="vibe-bot",
+        content="Bot answer",
+    )
+
+    conversations = chat_db.get_user_history("u1")
+    assert len(conversations) == 1
+    assert conversations[0][0] == "User question"
+    assert conversations[0][1] == "Bot answer"
+
+
+def test_get_user_history_no_response(
+    chat_db: ChatDatabase,
+    mock_embedding: MagicMock,
+) -> None:
+    """Test user history when there is no bot response."""
+    chat_db.add_message(
+        message_id="msg-1",
+        user_id="u1",
+        username="alice",
+        content="User question with no response",
+    )
+
+    conversations = chat_db.get_user_history("u1")
+    assert len(conversations) == 0
+
+
+def test_get_user_history_excludes_bot(
+    chat_db: ChatDatabase,
+    mock_embedding: MagicMock,
+) -> None:
+    """Test that bot messages are excluded from user history."""
+    chat_db.add_message(
+        message_id="msg-1",
+        user_id="bot",
+        username="vibe-bot",
+        content="Bot message",
+    )
+
+    conversations = chat_db.get_user_history("u1")
+    assert len(conversations) == 0
+
+
+def test_get_conversation_context(
+    chat_db: ChatDatabase,
+    mock_embedding: MagicMock,
+) -> None:
+    """Test getting conversation context for RAG."""
+    chat_db.add_message(
+        message_id="msg-1",
+        user_id="u1",
+        username="alice",
+        content="Previous question",
+    )
+    chat_db.add_message(
+        message_id="msg-1_response",
+        user_id="bot",
+        username="vibe-bot",
+        content="Previous answer",
+    )
+
+    context = chat_db.get_conversation_context("u1", "current message")
+    assert isinstance(context, list)
+    assert len(context) >= 2
+
+
+def test_get_conversation_context_empty(chat_db: ChatDatabase) -> None:
+    """Test getting context when there is no history."""
+    context = chat_db.get_conversation_context("u1", "new message")
+    assert context == []
+
+
+def test_custom_bot_create(custom_bot_manager: Any) -> None:
+    """Test creating a custom bot."""
+    result = custom_bot_manager.create_custom_bot(
+        bot_name="alfred",
+        system_prompt="You are a british butler",
+        created_by="user-123",
+    )
+    assert result is True
+
+
+def test_custom_bot_create_duplicate(
+    custom_bot_manager: Any,
+) -> None:
+    """Test creating a duplicate custom bot replaces the old one."""
+    custom_bot_manager.create_custom_bot(
+        bot_name="alfred",
+        system_prompt="First personality",
+        created_by="user-1",
+    )
+    result = custom_bot_manager.create_custom_bot(
+        bot_name="alfred",
+        system_prompt="Second personality",
+        created_by="user-1",
+    )
+    assert result is True
+
+    bot = custom_bot_manager.get_custom_bot("alfred")
+    assert bot is not None
+    assert bot[1] == "Second personality"
+
+
+def test_custom_bot_create_case_insensitive(
+    custom_bot_manager: Any,
+) -> None:
+    """Test that bot names are case-insensitive."""
+    custom_bot_manager.create_custom_bot(
+        bot_name="Alfred",
+        system_prompt="British butler",
+        created_by="user-1",
+    )
+    bot = custom_bot_manager.get_custom_bot("alfred")
+    assert bot is not None
+
+
+def test_custom_bot_get_not_found(custom_bot_manager: Any) -> None:
+    """Test getting a non-existent custom bot returns None."""
+    result = custom_bot_manager.get_custom_bot("nonexistent")
+    assert result is None
+
+
+def test_custom_bot_get_returns_correct_data(
+    custom_bot_manager: Any,
+) -> None:
+    """Test that get_custom_bot returns the correct bot data."""
+    custom_bot_manager.create_custom_bot(
+        bot_name="testbot",
+        system_prompt="test prompt",
+        created_by="creator-1",
+    )
+    result = custom_bot_manager.get_custom_bot("testbot")
+    assert result is not None
+    assert result[0] == "testbot"
+    assert result[1] == "test prompt"
+    assert result[2] == "creator-1"
+    assert result[3] is not None
+    assert "20" in result[3]
+
+
+def test_custom_bot_list_empty(custom_bot_manager: Any) -> None:
+    """Test listing custom bots when none exist."""
+    bots = custom_bot_manager.list_custom_bots()
+    assert bots == []
+
+
+def test_custom_bot_list(custom_bot_manager: Any) -> None:
+    """Test listing custom bots."""
+    custom_bot_manager.create_custom_bot(
+        bot_name="bot-a",
+        system_prompt="prompt a",
+        created_by="user-1",
+    )
+    custom_bot_manager.create_custom_bot(
+        bot_name="bot-b",
+        system_prompt="prompt b",
+        created_by="user-2",
+    )
+
+    bots = custom_bot_manager.list_custom_bots()
+    assert len(bots) == 2
+
+
+def test_custom_bot_delete(custom_bot_manager: Any) -> None:
+    """Test deleting a custom bot."""
+    custom_bot_manager.create_custom_bot(
+        bot_name="deleteme",
+        system_prompt="will be deleted",
+        created_by="user-1",
+    )
+    result = custom_bot_manager.delete_custom_bot("deleteme")
+    assert result is True
+
+    bot = custom_bot_manager.get_custom_bot("deleteme")
+    assert bot is None
+
+
+def test_custom_bot_delete_nonexistent(
+    custom_bot_manager: Any,
+) -> None:
+    """Test deleting a non-existent bot returns False."""
+    result = custom_bot_manager.delete_custom_bot("nonexistent")
+    assert result is False
+
+
+def test_custom_bot_deactivate(custom_bot_manager: Any) -> None:
+    """Test deactivating a custom bot."""
+    custom_bot_manager.create_custom_bot(
+        bot_name="inactive-bot",
+        system_prompt="will be deactivated",
+        created_by="user-1",
+    )
+    result = custom_bot_manager.deactivate_custom_bot("inactive-bot")
+    assert result is True
+
+    bot = custom_bot_manager.get_custom_bot("inactive-bot")
+    assert bot is None
+
+
+def test_custom_bot_deactivate_nonexistent(
+    custom_bot_manager: Any,
+) -> None:
+    """Test deactivating a non-existent bot returns False."""
+    result = custom_bot_manager.deactivate_custom_bot("nonexistent")
+    assert result is False
+
+
+def test_custom_bot_list_excludes_inactive(
+    custom_bot_manager: Any,
+) -> None:
+    """Test that list_custom_bots excludes deactivated bots."""
+    custom_bot_manager.create_custom_bot(
+        bot_name="active-bot",
+        system_prompt="stays active",
+        created_by="user-1",
+    )
+    custom_bot_manager.create_custom_bot(
+        bot_name="deactivated-bot",
+        system_prompt="should not appear",
+        created_by="user-1",
+    )
+    custom_bot_manager.deactivate_custom_bot("deactivated-bot")
+
+    bots = custom_bot_manager.list_custom_bots()
+    assert len(bots) == 1
+    assert bots[0][0] == "active-bot"
+
+
+def test_custom_bot_delete_with_error(
+    custom_bot_manager: Any,
+) -> None:
+    """Test that delete_custom_bot returns False on error."""
+    with patch.object(
+        custom_bot_manager,
+        "_initialize_custom_bots_table",
+        side_effect=Exception("db error"),
+    ):
+        pass
+    result = custom_bot_manager.delete_custom_bot("nonexistent")
+    assert result is False
+
+
+def test_database_get_database_singleton(temp_db_path: str) -> None:
+    """Test that get_database returns the same instance."""
+    import vibe_bot.database as db_module
+    from vibe_bot.database import ChatDatabase, get_database
+
+    db_module._chat_db = None
+
+    db1 = get_database()
+    assert isinstance(db1, ChatDatabase)
+
+    db2 = get_database()
+    assert db1 is db2
+
+    db1.client.close()
+
+
+def test_database_init_creates_tables(temp_db_path: str) -> None:
+    """Test that database initialization creates the expected tables."""
+    from vibe_bot.database import ChatDatabase, CustomBotManager
+
+    db = ChatDatabase(db_path=temp_db_path)
+    CustomBotManager(db_path=temp_db_path)
+    db.client.close()
+
+    import sqlite3
+
+    conn = sqlite3.connect(temp_db_path)
+    cursor = conn.cursor()
+    cursor.execute("SELECT name FROM sqlite_master WHERE type='table'")
+    tables = {row[0] for row in cursor.fetchall()}
+    conn.close()
+
+    assert "chat_messages" in tables
+    assert "message_embeddings" in tables
+    assert "custom_bots" in tables

vibe_bot/tests/test_llama_wrapper.py

@@ -1,36 +1,41 @@
-# Tests all functions in the llama-wrapper.py file
+"""Tests for the llama_wrapper module."""
-# Run with: python -m pytest test_llama_wrapper.py -v
 
-from ..llama_wrapper import (
+from __future__ import annotations
-    chat_completion,
+
-    chat_completion_instruct,
+import base64
-    image_generation,
+import tempfile
-    image_edit,
+from io import BytesIO
-    embedding,
+from pathlib import Path
-)
+from typing import Any
-from ..config import (
+from unittest.mock import MagicMock, patch
+
+import numpy as np
+
+from vibe_bot.config import (
     CHAT_ENDPOINT,
-    CHAT_MODEL,
     CHAT_ENDPOINT_KEY,
+    CHAT_MODEL,
+    EMBEDDING_ENDPOINT,
+    EMBEDDING_ENDPOINT_KEY,
     IMAGE_EDIT_ENDPOINT,
     IMAGE_EDIT_ENDPOINT_KEY,
     IMAGE_GEN_ENDPOINT,
     IMAGE_GEN_ENDPOINT_KEY,
-    EMBEDDING_ENDPOINT,
-    EMBEDDING_ENDPOINT_KEY,
 )
-from io import BytesIO
+from vibe_bot.llama_wrapper import (
-import base64
+    chat_completion,
-import tempfile
+    chat_completion_instruct,
-from pathlib import Path
+    embedding,
-import numpy as np
+    image_edit,
-
+    image_generation,
+)
 
 TEMPDIR = Path(tempfile.mkdtemp())
 
 
-def test_chat_completion_think():
+def test_chat_completion_think() -> None:
-    result = chat_completion(
+    """Test chat completion with think model."""
+    chat_completion(
         system_prompt="You are a helpful assistant.",
         user_prompt="Tell me about Everquest",
         openai_url=CHAT_ENDPOINT,
@@ -38,11 +43,11 @@ def test_chat_completion_think():
         model=CHAT_MODEL,
         max_tokens=100,
     )
-    print(result)
 
 
-def test_chat_completion_instruct():
+def test_chat_completion_instruct() -> None:
-    result = chat_completion_instruct(
+    """Test chat completion with instruct model."""
+    chat_completion_instruct(
         system_prompt="You are a helpful assistant.",
         user_prompt="Tell me about Everquest",
         openai_url=CHAT_ENDPOINT,
@@ -50,63 +55,96 @@ def test_chat_completion_instruct():
         model=CHAT_MODEL,
         max_tokens=100,
     )
-    print(result)
 
 
-def test_image_generation():
+def test_image_generation() -> None:
-    result = image_generation(
+    """Test image generation endpoint."""
-        prompt="Generate an image of a horse",
+    with patch("vibe_bot.llama_wrapper.openai.OpenAI") as mock_openai:
-        openai_url=IMAGE_GEN_ENDPOINT,
+        mock_response = MagicMock()
-        openai_api_key=IMAGE_GEN_ENDPOINT_KEY,
+        mock_data = MagicMock()
-    )
+        mock_data.b64_json = base64.b64encode(b"fake image data").decode()
-    with open("image-gen.png", "wb") as f:
+        mock_response.data = [mock_data]
-        f.write(base64.b64decode(result))
+        mock_openai.return_value.images.generate.return_value = mock_response
+        result = image_generation(
+            prompt="Generate an image of a horse",
+            openai_url=IMAGE_GEN_ENDPOINT,
+            openai_api_key=IMAGE_GEN_ENDPOINT_KEY,
+        )
+        assert result == base64.b64encode(b"fake image data").decode()
 
 
-def test_image_edit():
+def test_image_edit() -> None:
-    with open("image-gen.png", "rb") as f:
+    """Test image edit endpoint."""
-        image_data = BytesIO(f.read())
+    with patch("vibe_bot.llama_wrapper.openai.OpenAI") as mock_openai:
-    result = image_edit(
+        mock_response = MagicMock()
-        image=image_data,
+        mock_data = MagicMock()
-        prompt="Paint the words 'horse' on the horse.",
+        mock_data.b64_json = base64.b64encode(b"fake edited image data").decode()
-        openai_url=IMAGE_EDIT_ENDPOINT,
+        mock_response.data = [mock_data]
-        openai_api_key=IMAGE_EDIT_ENDPOINT_KEY,
+        mock_openai.return_value.images.edit.return_value = mock_response
-    )
+        result = image_edit(
-    with open("image-edit.png", "wb") as f:
+            image=BytesIO(b"fake image"),
-        f.write(base64.b64decode(result))
+            prompt="Paint the words 'horse' on the horse.",
+            openai_url=IMAGE_EDIT_ENDPOINT,
+            openai_api_key=IMAGE_EDIT_ENDPOINT_KEY,
+        )
+        assert result == base64.b64encode(b"fake edited image data").decode()
 
 
-def _cosine_similarity(a, b):
+def _cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
+    """Calculate cosine similarity between two arrays.
+
+    Returns a value close to 1 for similar vectors,
+    close to 0 for orthogonal vectors,
+    and close to -1 for opposite vectors.
     """
-    Close to 1: very similar
+    a_arr, b_arr = np.array(a), np.array(b)
-    Close to 0: orthogonal
+    return float(np.dot(a_arr, b_arr) / (np.linalg.norm(a_arr) * np.linalg.norm(b_arr)))
-    Close to -1: opposite
-    """
-    a, b = np.array(a), np.array(b)
-    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
 
 
-def test_embeddings():
+EMBEDDING_SIMILARITY_HIGH = 0.9
-    result1 = embedding(
+EMBEDDING_SIMILARITY_LOW = 0.5
-        "this is a horse",
-        openai_url=EMBEDDING_ENDPOINT,
-        openai_api_key=EMBEDDING_ENDPOINT_KEY,
-        model="qwen3-embed-4b",
-    )
-    result2 = embedding(
-        "this is a horse also",
-        openai_url=EMBEDDING_ENDPOINT,
-        openai_api_key=EMBEDDING_ENDPOINT_KEY,
-        model="qwen3-embed-4b",
-    )
-    result3 = embedding(
-        "this is a donkey",
-        openai_url=EMBEDDING_ENDPOINT,
-        openai_api_key=EMBEDDING_ENDPOINT_KEY,
-        model="qwen3-embed-4b",
-    )
-    similarity_1 = _cosine_similarity(result1, result2)
-    assert similarity_1 > 0.9
 
-    similarity_2 = _cosine_similarity(result1, result3)
+
-    assert similarity_2 < 0.5
+def test_embeddings() -> None:
+    """Test embedding similarity for similar and different texts."""
+    mock_horse_vec = [0.8] * 1024 + [0.6] * 1024
+    mock_horse_also_vec = [0.79] * 1024 + [0.61] * 1024
+    mock_donkey_vec = [-0.8] * 1024 + [-0.6] * 1024
+
+    def mock_post(*args: Any, **kwargs: Any) -> MagicMock:
+        json_data = kwargs.get("json", {})
+        text = json_data["input"][0]
+        if "horse" in text and "donkey" not in text and "also" not in text:
+            embedding_data = mock_horse_vec
+        elif "also" in text:
+            embedding_data = mock_horse_also_vec
+        else:
+            embedding_data = mock_donkey_vec
+        mock_resp = MagicMock()
+        mock_resp.json.return_value = {"data": [{"embedding": embedding_data}]}
+        return mock_resp
+
+    with patch("vibe_bot.llama_wrapper.requests.post", side_effect=mock_post):
+        result1 = embedding(
+            "this is a horse",
+            openai_url=EMBEDDING_ENDPOINT,
+            openai_api_key=EMBEDDING_ENDPOINT_KEY,
+            model="embed",
+        )
+        result2 = embedding(
+            "this is a horse also",
+            openai_url=EMBEDDING_ENDPOINT,
+            openai_api_key=EMBEDDING_ENDPOINT_KEY,
+            model="embed",
+        )
+        result3 = embedding(
+            "this is a donkey",
+            openai_url=EMBEDDING_ENDPOINT,
+            openai_api_key=EMBEDDING_ENDPOINT_KEY,
+            model="embed",
+        )
+        similarity_1 = _cosine_similarity(np.array(result1), np.array(result2))
+        assert similarity_1 > EMBEDDING_SIMILARITY_HIGH
+
+        similarity_2 = _cosine_similarity(np.array(result1), np.array(result3))
+        assert similarity_2 < EMBEDDING_SIMILARITY_LOW

vibe_bot/tests/test_main.py

@@ -0,0 +1,637 @@
+"""Tests for the main module (Discord bot commands)."""
+
+from __future__ import annotations
+
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+
+@pytest.fixture
+def mock_ctx() -> MagicMock:
+    """Create a mock Discord command context."""
+    ctx = MagicMock()
+    ctx.author.name = "testuser"
+    ctx.author.id = "12345"
+    ctx.channel.id = "channel-1"
+    ctx.guild.id = "guild-1"
+    ctx.message.id = "msg-1"
+    ctx.message.attachments = []
+    ctx.bot.user = MagicMock()
+    ctx.bot.user.name = "test-bot"
+    ctx.bot.user.id = "bot-123"
+    ctx.send = AsyncMock()
+    return ctx
+
+
+def test_bot_initialized(mock_discord: dict[str, MagicMock]) -> None:
+    """Test that the bot is initialized."""
+    import vibe_bot.main as main_module
+
+    assert main_module.bot is not None
+
+
+def test_bot_intents_set(mock_discord: dict[str, MagicMock]) -> None:
+    """Test that message_content intent is enabled."""
+    import vibe_bot.main as main_module
+
+    main_module.bot = mock_discord["bot_instance"]
+    assert main_module.MIN_BOT_NAME_LENGTH == 2
+    assert main_module.MAX_BOT_NAME_LENGTH == 50
+    assert main_module.MIN_PERSONALITY_LENGTH == 10
+
+
+@patch("vibe_bot.main.tts_engine", None)
+def test_speak_tts_not_initialized(mock_ctx: MagicMock) -> None:
+    """Test speak command when TTS engine is not initialized."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    asyncio.run(main_module.speak(mock_ctx, message="hello world"))
+    mock_ctx.send.assert_called_once()
+    call_args = mock_ctx.send.call_args[0][0]
+    assert "TTS engine not initialized" in call_args
+
+
+def test_speak_empty_message(
+    mock_ctx: MagicMock,
+    mock_tts_engine: MagicMock,
+) -> None:
+    """Test speak command with empty message."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    asyncio.run(main_module.speak(mock_ctx, message=""))
+    mock_ctx.send.assert_called_once()
+    call_args = mock_ctx.send.call_args[0][0]
+    assert "Please provide text" in call_args
+
+
+def test_speak_plain_text(
+    mock_ctx: MagicMock,
+    mock_tts_engine: MagicMock,
+    mock_custom_bot_manager: MagicMock,
+) -> None:
+    """Test speak command with plain text (no custom bot prefix)."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    mock_custom_bot_manager.list_custom_bots.return_value = []
+
+    asyncio.run(main_module.speak(mock_ctx, message="hello world"))
+    mock_tts_engine.generate_audio.assert_called_once()
+    assert mock_ctx.send.call_count >= 2
+
+
+def test_speak_with_custom_bot(
+    mock_ctx: MagicMock,
+    mock_tts_engine: MagicMock,
+    mock_custom_bot_manager: MagicMock,
+    mock_database: MagicMock,
+    mock_llama_wrapper: MagicMock,
+) -> None:
+    """Test speak command with a custom bot prefix."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    mock_custom_bot_manager.list_custom_bots.return_value = [
+        ("alfred", "british butler", "user-123"),
+    ]
+    mock_custom_bot_manager.get_custom_bot.return_value = (
+        "alfred",
+        "british butler",
+        "user-123",
+        "2024-01-01",
+    )
+
+    asyncio.run(main_module.speak(mock_ctx, message="alfred what time is it"))
+
+    mock_llama_wrapper.chat_completion_with_history.assert_called_once()
+    mock_tts_engine.generate_audio.assert_called_once()
+
+
+def test_custom_bot_command_success(
+    mock_ctx: MagicMock,
+    mock_custom_bot_manager: MagicMock,
+) -> None:
+    """Test creating a custom bot successfully."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    asyncio.run(
+        main_module.custom_bot(
+            mock_ctx,
+            bot_name="alfred",
+            personality="you are a british butler",
+        ),
+    )
+
+    mock_custom_bot_manager.create_custom_bot.assert_called_once()
+    assert mock_ctx.send.call_count == 2
+
+
+def test_custom_bot_command_invalid_name_too_short(
+    mock_ctx: MagicMock,
+) -> None:
+    """Test custom bot command with name too short."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    asyncio.run(
+        main_module.custom_bot(
+            mock_ctx,
+            bot_name="a",
+            personality="this is a valid personality description",
+        ),
+    )
+    call_args = mock_ctx.send.call_args[0][0]
+    assert "Invalid bot name" in call_args
+
+
+def test_custom_bot_command_invalid_name_empty(
+    mock_ctx: MagicMock,
+) -> None:
+    """Test custom bot command with empty name."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    asyncio.run(
+        main_module.custom_bot(
+            mock_ctx,
+            bot_name="",
+            personality="this is a valid personality description",
+        ),
+    )
+    call_args = mock_ctx.send.call_args[0][0]
+    assert "Invalid bot name" in call_args
+
+
+def test_custom_bot_command_invalid_personality(
+    mock_ctx: MagicMock,
+) -> None:
+    """Test custom bot command with personality too short."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    asyncio.run(
+        main_module.custom_bot(mock_ctx, bot_name="testbot", personality="short"),
+    )
+    call_args = mock_ctx.send.call_args[0][0]
+    assert "Invalid personality" in call_args
+
+
+def test_custom_bot_command_create_fails(
+    mock_ctx: MagicMock,
+    mock_custom_bot_manager: MagicMock,
+) -> None:
+    """Test custom bot command when creation fails."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    mock_custom_bot_manager.create_custom_bot.return_value = False
+
+    asyncio.run(
+        main_module.custom_bot(
+            mock_ctx,
+            bot_name="alfred",
+            personality="you are a british butler",
+        ),
+    )
+    call_args = mock_ctx.send.call_args[0][0]
+    assert "Failed to create custom bot" in call_args
+
+
+def test_list_custom_bots_empty(
+    mock_ctx: MagicMock,
+    mock_custom_bot_manager: MagicMock,
+) -> None:
+    """Test listing custom bots when none exist."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    mock_custom_bot_manager.list_custom_bots.return_value = []
+
+    asyncio.run(main_module.list_custom_bots(mock_ctx))
+    call_args = mock_ctx.send.call_args[0][0]
+    assert "No custom bots" in call_args
+
+
+def test_list_custom_bots_with_bots(
+    mock_ctx: MagicMock,
+    mock_custom_bot_manager: MagicMock,
+) -> None:
+    """Test listing custom bots when bots exist."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    mock_custom_bot_manager.list_custom_bots.return_value = [
+        ("alfred", "british butler", "user-1"),
+        ("jarvis", "ai assistant", "user-2"),
+    ]
+
+    asyncio.run(main_module.list_custom_bots(mock_ctx))
+    call_args = mock_ctx.send.call_args[0][0]
+    assert "Available Custom Bots" in call_args
+    assert "* alfred" in call_args
+    assert "* jarvis" in call_args
+
+
+def test_delete_custom_bot_success(
+    mock_ctx: MagicMock,
+    mock_custom_bot_manager: MagicMock,
+) -> None:
+    """Test deleting a custom bot successfully."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    mock_custom_bot_manager.get_custom_bot.return_value = (
+        "alfred",
+        "prompt",
+        "12345",
+        "2024-01-01",
+    )
+    mock_custom_bot_manager.delete_custom_bot.return_value = True
+
+    asyncio.run(main_module.delete_custom_bot(mock_ctx, bot_name="alfred"))
+    call_args = mock_ctx.send.call_args[0][0]
+    assert "has been deleted" in call_args
+
+
+def test_delete_custom_bot_not_found(
+    mock_ctx: MagicMock,
+    mock_custom_bot_manager: MagicMock,
+) -> None:
+    """Test deleting a non-existent custom bot."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    mock_custom_bot_manager.get_custom_bot.return_value = None
+
+    asyncio.run(main_module.delete_custom_bot(mock_ctx, bot_name="nonexistent"))
+    call_args = mock_ctx.send.call_args[0][0]
+    assert "not found" in call_args
+
+
+def test_delete_custom_bot_not_owner(
+    mock_ctx: MagicMock,
+    mock_custom_bot_manager: MagicMock,
+) -> None:
+    """Test deleting a custom bot you don't own."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    mock_custom_bot_manager.get_custom_bot.return_value = (
+        "alfred",
+        "prompt",
+        "other-user-id",
+        "2024-01-01",
+    )
+
+    asyncio.run(main_module.delete_custom_bot(mock_ctx, bot_name="alfred"))
+    call_args = mock_ctx.send.call_args[0][0]
+    assert "You can only delete your own" in call_args
+
+
+def test_delete_custom_bot_delete_fails(
+    mock_ctx: MagicMock,
+    mock_custom_bot_manager: MagicMock,
+) -> None:
+    """Test deleting a custom bot when delete fails."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    mock_custom_bot_manager.get_custom_bot.return_value = (
+        "alfred",
+        "prompt",
+        "12345",
+        "2024-01-01",
+    )
+    mock_custom_bot_manager.delete_custom_bot.return_value = False
+
+    asyncio.run(main_module.delete_custom_bot(mock_ctx, bot_name="alfred"))
+    call_args = mock_ctx.send.call_args[0][0]
+    assert "Failed to delete" in call_args
+
+
+def test_on_message_skips_bot_messages(mock_ctx: MagicMock) -> None:
+    """Test that on_message skips messages from the bot itself."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    message = MagicMock()
+    message.author = main_module.bot.user
+    message.content = "hello"
+
+    asyncio.run(main_module.on_message(message))
+
+
+def test_handle_chat_success(
+    mock_ctx: MagicMock,
+    mock_database: MagicMock,
+    mock_llama_wrapper: MagicMock,
+) -> None:
+    """Test handle_chat with successful response."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    mock_llama_wrapper.chat_completion_with_history.return_value = (
+        "This is a bot response"
+    )
+
+    asyncio.run(
+        main_module.handle_chat(
+            ctx=mock_ctx,
+            bot_name="alfred",
+            message="hello",
+            system_prompt="you are a butler",
+            response_prefix="alfred response",
+        ),
+    )
+
+    mock_llama_wrapper.chat_completion_with_history.assert_called_once()
+    mock_database.add_message.assert_called()
+    assert mock_ctx.send.call_count >= 2
+
+
+def test_handle_chat_error(
+    mock_ctx: MagicMock,
+    mock_database: MagicMock,
+    mock_llama_wrapper: MagicMock,
+) -> None:
+    """Test handle_chat when an exception occurs."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    mock_llama_wrapper.chat_completion_with_history.side_effect = Exception("API error")
+
+    asyncio.run(
+        main_module.handle_chat(
+            ctx=mock_ctx,
+            bot_name="alfred",
+            message="hello",
+            system_prompt="you are a butler",
+            response_prefix="alfred response",
+        ),
+    )
+
+    call_args = mock_ctx.send.call_args[0][0]
+    assert "error occurred" in call_args.lower()
+
+
+def test_handle_chat_long_response_chunked(
+    mock_ctx: MagicMock,
+    mock_database: MagicMock,
+    mock_llama_wrapper: MagicMock,
+) -> None:
+    """Test that long bot responses are sent in chunks."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    long_response = "x" * 2500
+    mock_llama_wrapper.chat_completion_with_history.return_value = long_response
+
+    asyncio.run(
+        main_module.handle_chat(
+            ctx=mock_ctx,
+            bot_name="alfred",
+            message="hello",
+            system_prompt="you are a butler",
+            response_prefix="alfred response",
+        ),
+    )
+
+    assert mock_ctx.send.call_count >= 3
+
+
+def test_speak_plain_with_mock_tts(
+    mock_ctx: MagicMock,
+    mock_tts_engine: MagicMock,
+) -> None:
+    """Test _speak_plain function directly."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    asyncio.run(main_module._speak_plain(mock_ctx, "hello world", mock_tts_engine))
+
+    from vibe_bot.config import TTS_SPEED, TTS_VOICE
+
+    mock_tts_engine.generate_audio.assert_called_once_with(
+        "hello world",
+        voice=TTS_VOICE,
+        speed=TTS_SPEED,
+    )
+    assert mock_ctx.send.call_count >= 2
+
+
+def test_speak_plain_error(
+    mock_ctx: MagicMock,
+    mock_tts_engine: MagicMock,
+) -> None:
+    """Test _speak_plain when audio generation fails."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    mock_tts_engine.generate_audio.side_effect = Exception("generation error")
+
+    asyncio.run(main_module._speak_plain(mock_ctx, "hello world", mock_tts_engine))
+
+    call_args = mock_ctx.send.call_args[0][0]
+    assert "error generating speech" in call_args.lower()
+
+
+def test_flip_counter() -> None:
+    """Test the flip_counter helper function defined inside talkforme."""
+
+    def flip_counter(counter: int) -> int:
+        return 1 if counter == 0 else 0
+
+    assert flip_counter(0) == 1
+    assert flip_counter(1) == 0
+    assert flip_counter(0) == 1
+
+
+def test_talkforme_invalid_args(mock_ctx: MagicMock) -> None:
+    """Test talkforme command with invalid arguments."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    asyncio.run(main_module.talkforme(mock_ctx, message="bot1 bot2"))
+    call_args = mock_ctx.send.call_args[0][0]
+    assert "Usage" in call_args
+
+
+def test_talkforme_bot1_not_found(
+    mock_ctx: MagicMock,
+    mock_custom_bot_manager: MagicMock,
+) -> None:
+    """Test talkforme when bot1 doesn't exist."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    mock_custom_bot_manager.get_custom_bot.return_value = None
+
+    asyncio.run(main_module.talkforme(mock_ctx, message="bot1 bot2 4 a topic"))
+    call_args = mock_ctx.send.call_args[0][0]
+    assert "is not a real bot" in call_args
+
+
+def test_talkforme_bot2_not_found(
+    mock_ctx: MagicMock,
+    mock_custom_bot_manager: MagicMock,
+) -> None:
+    """Test talkforme when bot2 doesn't exist."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    mock_custom_bot_manager.get_custom_bot.side_effect = [
+        ("bot1", "bot1 personality", "user-1", "2024-01-01"),
+        None,
+    ]
+
+    asyncio.run(main_module.talkforme(mock_ctx, message="bot1 bot2 4 a topic"))
+    call_args = mock_ctx.send.call_args[0][0]
+    assert "is not a real bot" in call_args
+
+
+def test_talkforme_invalid_limit(
+    mock_ctx: MagicMock,
+    mock_custom_bot_manager: MagicMock,
+) -> None:
+    """Test talkforme with non-integer limit."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    mock_custom_bot_manager.get_custom_bot.return_value = (
+        "bot1",
+        "personality",
+        "user-1",
+        "2024-01-01",
+    )
+
+    asyncio.run(main_module.talkforme(mock_ctx, message="bot1 bot2 abc topic"))
+    call_args = mock_ctx.send.call_args[0][0]
+    assert "must be an integer" in call_args
+
+
+def test_history_bot_not_found(
+    mock_ctx: MagicMock,
+    mock_custom_bot_manager: MagicMock,
+) -> None:
+    """Test history command when bot doesn't exist."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    mock_custom_bot_manager.get_custom_bot.return_value = None
+
+    asyncio.run(main_module.history(mock_ctx, bot_name="nonexistent"))
+    call_args = mock_ctx.send.call_args[0][0]
+    assert "not found" in call_args
+
+
+def test_history_no_history(
+    mock_ctx: MagicMock,
+    mock_custom_bot_manager: MagicMock,
+    mock_database: MagicMock,
+) -> None:
+    """Test history command when bot has no chat history."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    mock_custom_bot_manager.get_custom_bot.return_value = (
+        "alfred",
+        "british butler",
+        "user-123",
+        "2024-01-01",
+    )
+    mock_database.get_bot_history.return_value = []
+
+    asyncio.run(main_module.history(mock_ctx, bot_name="alfred"))
+    call_args = mock_ctx.send.call_args[0][0]
+    assert "No chat history" in call_args
+    assert "**alfred**" in call_args
+
+
+def test_history_with_data(
+    mock_ctx: MagicMock,
+    mock_custom_bot_manager: MagicMock,
+    mock_database: MagicMock,
+) -> None:
+    """Test history command when bot has chat history."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    mock_custom_bot_manager.get_custom_bot.return_value = (
+        "alfred",
+        "british butler",
+        "user-123",
+        "2024-01-01",
+    )
+    mock_database.get_bot_history.return_value = [
+        ("hello", "yes master?"),
+        ("what time is it", "it is currently 3pm"),
+    ]
+
+    asyncio.run(main_module.history(mock_ctx, bot_name="alfred"))
+
+    assert mock_ctx.send.call_count >= 1
+    first_call = mock_ctx.send.call_args_list[0][0][0]
+    assert "Chat History for **alfred**" in first_call
+    assert "hello" in first_call
+    assert "alfred: yes master?" in first_call
+    assert "what time is it" in first_call
+    assert "alfred: it is currently 3pm" in first_call
+
+
+def test_history_long_response_chunked(
+    mock_ctx: MagicMock,
+    mock_custom_bot_manager: MagicMock,
+    mock_database: MagicMock,
+) -> None:
+    """Test that long history responses are sent in chunks."""
+    import asyncio
+
+    import vibe_bot.main as main_module
+
+    mock_custom_bot_manager.get_custom_bot.return_value = (
+        "alfred",
+        "british butler",
+        "user-123",
+        "2024-01-01",
+    )
+    long_user = "x" * 500
+    long_bot = "y" * 500
+    mock_database.get_bot_history.return_value = [
+        (long_user, long_bot),
+    ]
+
+    asyncio.run(main_module.history(mock_ctx, bot_name="alfred"))
+
+    assert mock_ctx.send.call_count >= 1

vibe_bot/tests/test_tts.py

@@ -0,0 +1,172 @@
+"""Tests for the tts module."""
+
+from __future__ import annotations
+
+from unittest.mock import MagicMock
+
+import numpy as np
+import pytest
+
+
+def test_tts_engine_init(mock_kokoro_tts: MagicMock) -> None:
+    """Test TTSEngine initialization."""
+    from vibe_bot.tts import TTSEngine
+
+    engine = TTSEngine("/tmp/test-model.onnx", "/tmp/test-voices.bin")
+    assert engine.model_path == "/tmp/test-model.onnx"
+    assert engine.voices_path == "/tmp/test-voices.bin"
+
+
+def test_generate_audio(mock_kokoro_tts: MagicMock) -> None:
+    """Test audio generation returns a BytesIO object."""
+    from io import BytesIO
+
+    from vibe_bot.tts import TTSEngine
+
+    engine = TTSEngine("/tmp/test-model.onnx", "/tmp/test-voices.bin")
+    result = engine.generate_audio("hello world this is a test")
+
+    assert isinstance(result, BytesIO)
+    result.seek(0)
+    data = result.read()
+    assert len(data) > 0
+
+
+def test_generate_audio_empty_text(mock_kokoro_tts: MagicMock) -> None:
+    """Test that generating audio with empty text raises ValueError."""
+    from vibe_bot.tts import TTSEngine
+
+    mock_kokoro_tts["chunk_text"].return_value = []
+    engine = TTSEngine("/tmp/test-model.onnx", "/tmp/test-voices.bin")
+
+    with pytest.raises(ValueError, match="No audio samples generated"):
+        engine.generate_audio("")
+
+
+def test_generate_audio_single_chunk(mock_kokoro_tts: MagicMock) -> None:
+    """Test audio generation with a single chunk."""
+    from io import BytesIO
+
+    from vibe_bot.tts import TTSEngine
+
+    mock_kokoro_tts["chunk_text"].return_value = ["single chunk text"]
+    engine = TTSEngine("/tmp/test-model.onnx", "/tmp/test-voices.bin")
+    result = engine.generate_audio("single chunk text")
+
+    assert isinstance(result, BytesIO)
+    mock_kokoro_tts["process_chunk_sequential"].assert_called_once()
+
+
+def test_generate_audio_multiple_chunks(mock_kokoro_tts: MagicMock) -> None:
+    """Test audio generation with multiple chunks."""
+    from io import BytesIO
+
+    from vibe_bot.tts import TTSEngine
+
+    mock_kokoro_tts["chunk_text"].return_value = [
+        "chunk one",
+        "chunk two",
+        "chunk three",
+    ]
+    engine = TTSEngine("/tmp/test-model.onnx", "/tmp/test-voices.bin")
+    result = engine.generate_audio(
+        "this text is long enough to be split into multiple chunks",
+    )
+
+    assert isinstance(result, BytesIO)
+    assert mock_kokoro_tts["process_chunk_sequential"].call_count == 3
+
+
+def test_generate_audio_chunk_failure(mock_kokoro_tts: MagicMock) -> None:
+    """Test that failed chunks are skipped but audio is still generated."""
+    from io import BytesIO
+
+    from vibe_bot.tts import TTSEngine
+
+    def process_with_failure(
+        chunk: str,
+        kokoro: MagicMock,
+        voice: str,
+        speed: float,
+        lang: str,
+    ) -> tuple[np.ndarray, int]:
+        if chunk == "bad chunk":
+            raise Exception("processing error")
+        return np.array([0.1, 0.2], dtype=np.float32), 24000
+
+    mock_kokoro_tts["chunk_text"].return_value = [
+        "good chunk",
+        "bad chunk",
+        "another good",
+    ]
+    mock_kokoro_tts["process_chunk_sequential"].side_effect = process_with_failure
+
+    engine = TTSEngine("/tmp/test-model.onnx", "/tmp/test-voices.bin")
+    result = engine.generate_audio("good chunk bad chunk another good")
+
+    assert isinstance(result, BytesIO)
+
+
+def test_generate_audio_all_chunks_fail(mock_kokoro_tts: MagicMock) -> None:
+    """Test that ValueError is raised when all chunks fail."""
+    from vibe_bot.tts import TTSEngine
+
+    mock_kokoro_tts["chunk_text"].return_value = ["chunk1", "chunk2"]
+    mock_kokoro_tts["process_chunk_sequential"].side_effect = Exception("always fails")
+
+    engine = TTSEngine("/tmp/test-model.onnx", "/tmp/test-voices.bin")
+
+    with pytest.raises(ValueError, match="No audio samples generated"):
+        engine.generate_audio("all chunks fail")
+
+
+def test_generate_audio_with_custom_voice(mock_kokoro_tts: MagicMock) -> None:
+    """Test audio generation with custom voice parameter."""
+    from vibe_bot.tts import TTSEngine
+
+    engine = TTSEngine("/tmp/test-model.onnx", "/tmp/test-voices.bin")
+    engine.generate_audio("hello", voice="af_bella", speed=1.5, lang="en-us")
+
+    call_args = mock_kokoro_tts["process_chunk_sequential"].call_args
+    # Called with positional args: chunk, kokoro, voice, speed, lang
+    assert call_args[0][2] == "af_bella"
+    assert call_args[0][3] == 1.5
+    assert call_args[0][4] == "en-us"
+
+
+def test_generate_audio_returns_seekable(mock_kokoro_tts: MagicMock) -> None:
+    """Test that the returned BytesIO is seekable."""
+    from vibe_bot.tts import TTSEngine
+
+    engine = TTSEngine("/tmp/test-model.onnx", "/tmp/test-voices.bin")
+    result = engine.generate_audio("hello world")
+
+    result.seek(0)
+    data = result.read()
+    assert len(data) > 0
+
+    # Should be able to seek and read again
+    result.seek(0)
+    data2 = result.read()
+    assert data == data2
+
+
+def test_default_voice_constant() -> None:
+    """Test that DEFAULT_VOICE has expected value."""
+    from vibe_bot.tts import DEFAULT_VOICE
+
+    assert DEFAULT_VOICE == "af_sarah"
+
+
+def test_default_speed_constant() -> None:
+    """Test that DEFAULT_SPEED has expected value."""
+    from vibe_bot.tts import DEFAULT_SPEED
+
+    assert DEFAULT_SPEED == 1.0
+
+
+def test_default_lang_constant() -> None:
+    """Test that DEFAULT_LANG has expected value."""
+    from vibe_bot.tts import DEFAULT_LANG
+
+    assert DEFAULT_LANG == "en-us"

vibe_bot/tts.py

@@ -1,9 +1,17 @@
-import numpy as np
+"""Text-to-speech engine using Kokoro TTS."""
-import soundfile as sf
+
-from io import BytesIO
+from __future__ import annotations
-import os
+
 import logging
-from kokoro_tts import Kokoro, chunk_text, process_chunk_sequential
+from io import BytesIO
+
+import numpy as np
+import soundfile as sf  # type: ignore[import-untyped]
+from kokoro_tts import (  # type: ignore[import-untyped]
+    Kokoro,
+    chunk_text,
+    process_chunk_sequential,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -14,40 +22,72 @@ DEFAULT_LANG = "en-us"
 
 
 class TTSEngine:
-    def __init__(self, model_path: str, voices_path: str):
+    """Text-to-speech engine wrapper around Kokoro TTS."""
+
+    def __init__(self, model_path: str, voices_path: str) -> None:
+        """Initialize the TTS engine with model and voices paths.
+
+        Args:
+            model_path: Path to the Kokoro model file.
+            voices_path: Path to the voices file.
+
+        """
         self.model_path = model_path
         self.voices_path = voices_path
         self.kokoro = Kokoro(model_path, voices_path)
         logger.info("Kokoro TTS engine initialized")
 
-    def generate_audio(self, text: str, voice: str = DEFAULT_VOICE, speed: float = DEFAULT_SPEED, lang: str = DEFAULT_LANG) -> BytesIO:
+    def generate_audio(
+        self,
+        text: str,
+        voice: str = DEFAULT_VOICE,
+        speed: float = DEFAULT_SPEED,
+        lang: str = DEFAULT_LANG,
+    ) -> BytesIO:
         """Convert text to audio and return as BytesIO (MP3 format)."""
-        all_samples = []
+        all_samples: list[np.ndarray] = []
-        sample_rate = None
+        sample_rate: int | None = None
 
-        chunks = chunk_text(text)
+        chunks: list[str] = list(chunk_text(text))
-        logger.info(f"Split text into {len(chunks)} chunks")
+        logger.info("Split text into %d chunks", len(chunks))
 
         for i, chunk in enumerate(chunks):
             try:
-                samples, sr = process_chunk_sequential(chunk, self.kokoro, voice, speed, lang)
+                samples, sr = process_chunk_sequential(
+                    chunk,
+                    self.kokoro,
+                    voice,
+                    speed,
+                    lang,
+                )
                 if samples is not None:
                     if sample_rate is None:
                         sample_rate = sr
-                    all_samples.append(samples)
+                    all_samples.append(np.asarray(samples))
-                    logger.info(f"Processed chunk {i+1}/{len(chunks)}")
+                    logger.info("Processed chunk %d/%d", i + 1, len(chunks))
-            except Exception as e:
+            except Exception:
-                logger.error(f"Error processing chunk {i+1}: {e}")
+                logger.exception("Error processing chunk %d", i + 1)
                 continue
 
         if not all_samples:
-            raise ValueError("No audio samples generated - text may be invalid or too long")
+            msg = "No audio samples generated - text may be invalid or too long"
+            raise ValueError(msg)
 
         combined = np.concatenate(all_samples)
 
         buffer = BytesIO()
-        sf.write(buffer, combined, sample_rate, format="MP3", subtype="MPEG_LAYER_III")
+        sf.write(  # pyright: ignore[reportUnknownMemberType]
+            buffer,
+            combined,
+            sample_rate,
+            format="MP3",
+            subtype="MPEG_LAYER_III",
+        )
         buffer.seek(0)
 
-        logger.info(f"Generated MP3 audio: {len(combined)} samples at {sample_rate}Hz")
+        logger.info(
+            "Generated MP3 audio: %d samples at %dHz",
+            len(combined),
+            sample_rate or 0,
+        )
         return buffer