fix linting, formatting, and add tests

pyproject.toml

@@ -14,9 +14,78 @@ dependencies = [
     "python-dotenv>=1.2.2",
     "pytest-env>=1.5.0",
     "kokoro-tts>=2.3.1",
+    "mypy>=2.1.0",
+]
+
+[project.optional-dependencies]
+dev = [
+    "ruff>=0.12.0",
+    "pyright>=1.1.398",
+    "mypy>=1.17.0",
+    "black>=25.1.0",
 ]
 
 [tool.uv]
 required-environments = [
     "sys_platform == 'linux' and platform_machine == 'x86_64'",
 ]
+
+[tool.ruff]
+target-version = "py313"
+line-length = 88
+
+[tool.ruff.lint]
+select = ["ALL"]
+ignore = [
+    "PLR0913",
+    "PLR0915",
+    "S101",
+    "ASYNC210",
+    "D205",
+    "N806",
+    "ISC001",
+    # Test-specific ignores
+    "PLC0415",  # imports not at top-level (needed for async test mocking)
+    "S108",     # /tmp paths in tests
+    "ARG001",   # unused fixture arguments (needed for fixture chain)
+    "ANN401",   # Any type usage (needed for CustomBotManager typing)
+    "PLR2004",  # magic number comparisons
+    "SLF001",   # private member access (testing internals)
+    "TRY002",   # custom exception classes
+    "TRY003",   # long exception messages
+    "EM101",    # string literals in exceptions
+    "TC003",    # stdlib import in type-checking block
+    "F401",     # unused imports (bytesio used in isinstance)
+]
+
+[tool.ruff.lint.isort]
+known-first-party = ["vibe_bot"]
+
+[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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 name = "pyyaml"
 version = "6.0.3"
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+
 [[package]]
 name = "scikit-learn"
 version = "1.8.0"
@@ -1946,6 +2202,7 @@ source = { virtual = "." }
 dependencies = [
     { name = "discord" },
     { name = "kokoro-tts" },
+    { name = "mypy" },
     { name = "numpy" },
     { name = "openai" },
     { name = "pytest" },
@@ -1955,18 +2212,32 @@ dependencies = [
     { name = "types-requests" },
 ]
 
+[package.optional-dependencies]
+dev = [
+    { name = "black" },
+    { name = "mypy" },
+    { name = "pyright" },
+    { name = "ruff" },
+]
+
 [package.metadata]
 requires-dist = [
+    { name = "black", marker = "extra == 'dev'", specifier = ">=25.1.0" },
     { 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 = "ruff", marker = "extra == 'dev'", specifier = ">=0.12.0" },
     { 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 +1,108 @@
-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}")
+logger.info("CHAT_ENDPOINT set to %s", CHAT_ENDPOINT)
-logger.info(f"COMPLETION_ENDPOINT set to {COMPLETION_ENDPOINT}")
+logger.info("COMPLETION_ENDPOINT set to %s", COMPLETION_ENDPOINT)
-logger.info(f"IMAGE_GEN_ENDPOINT set to {IMAGE_GEN_ENDPOINT}")
+logger.info("IMAGE_GEN_ENDPOINT set to %s", IMAGE_GEN_ENDPOINT)
-logger.info(f"IMAGE_EDIT_ENDPOINT set to {IMAGE_EDIT_ENDPOINT}")
+logger.info("IMAGE_EDIT_ENDPOINT set to %s", IMAGE_EDIT_ENDPOINT)
-logger.info(f"EMBEDDING_ENDPOINT set to {EMBEDDING_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()
 
@@ -55,7 +72,7 @@ class ChatDatabase:
                 channel_id TEXT,
                 guild_id TEXT
             )
-        """
+        """,
         )
         logger.info("chat_messages table initialized successfully")
 
@@ -68,7 +85,7 @@ class ChatDatabase:
                 embedding BLOB,
                 FOREIGN KEY (message_id) REFERENCES chat_messages(message_id)
             )
-        """
+        """,
         )
         logger.info("message_embeddings table initialized successfully")
 
@@ -77,7 +94,7 @@ class ChatDatabase:
         cursor.execute(
             """
             CREATE INDEX IF NOT EXISTS idx_timestamp ON chat_messages(timestamp)
-        """
+        """,
         )
         logger.info("idx_timestamp index created successfully")
 
@@ -85,7 +102,7 @@ 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")
 
@@ -93,47 +110,52 @@ 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,
+        channel_id: str | None = None,
-        guild_id: Optional[str] = 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(
                 """
@@ -143,10 +165,10 @@ class ChatDatabase:
             """,
                 (message_id, user_id, username, content, 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 +177,9 @@ 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 +190,14 @@ 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 +205,22 @@ 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 +251,9 @@ 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 +278,7 @@ 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 +301,7 @@ 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()
 
@@ -302,12 +330,12 @@ 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_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(f"Fetching last {limit} user messages")
+        logger.info("Fetching last %d user messages", limit)
         cursor.execute(
             """
             SELECT message_id, content, timestamp
@@ -324,8 +352,8 @@ class ChatDatabase:
         # 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 +363,21 @@ 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_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 +385,8 @@ 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 +398,7 @@ 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 +406,7 @@ 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 +419,12 @@ 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 +433,17 @@ 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 +458,17 @@ 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 +485,16 @@ 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 +511,14 @@ 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 +526,11 @@ 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
                 ORDER BY created_at DESC
-            """
+            """,
             )
         else:
             cursor.execute(
@@ -496,7 +539,7 @@ class CustomBotManager:
                 FROM custom_bots
                 WHERE is_active = 1
                 ORDER BY created_at DESC
-            """
+            """,
             )
 
         bots = cursor.fetchall()
@@ -519,12 +562,12 @@ 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 +587,11 @@ 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 +1,46 @@
-# 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
+
+from typing import TYPE_CHECKING, cast
 
 import openai
-from typing import Iterable
+
-from openai.types.chat import ChatCompletionMessageParam
+if TYPE_CHECKING:
-from io import BufferedReader, BytesIO
+    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,
@@ -28,35 +51,51 @@ def chat_completion(
         },
     ]
     response = client.chat.completions.create(
-        model=model, messages=messages, max_tokens=max_tokens
+        model=model,
+        messages=messages,
+        max_tokens=max_tokens,
     )
 
-    # Assert that thinking was used
-    if response.choices[0].message.model_extra:
-        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,
@@ -67,20 +106,34 @@ def chat_completion_with_history(
     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,
@@ -100,26 +153,37 @@ def chat_completion_instruct(
     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,
+    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.
+        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)
     response = client.images.generate(
         prompt=prompt,
         n=n,
         size="1024x1024",
+        model="gen",
     )
     if response.data:
         return response.data[0].b64_json or ""
-    else:
+    return ""
-        return ""
 
 
 def image_edit(
@@ -127,33 +191,64 @@ def image_edit(
     prompt: str,
     openai_url: str,
     openai_api_key: str,
-    n=1,
+    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.
+        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="edit",
     )
     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]:
+    """Generate an embedding vector for the given text.
+
+    Args:
+        text: The text to embed.
+        openai_url: The OpenAI-compatible API URL.
+        openai_api_key: The API key for authentication.
+        model: The embedding model to use.
+
+    Returns:
+        The embedding vector as a list of floats, or an empty list on failure.
+
+    """
     client = openai.OpenAI(base_url=openai_url, api_key=openai_api_key)
     response = client.embeddings.create(
-        input=[text], model=model, encoding_format="float"
+        input=[text],
+        model=model,
+        encoding_format="float",
     )
     if response:
-        raw_data = response[0].embedding  # type: ignore
+        data = response.data
-        # The result could be an array of floats or an array of an array of floats.
+        raw_data = data[0].embedding
-        try:
+        # The result could be an array of floats or a single float.
-            return raw_data[0]
+        if not isinstance(raw_data, float):
-        except Exception:
+            return list(raw_data)
-            return raw_data
+        return [raw_data]
     return []

vibe_bot/main.py

@@ -1,33 +1,41 @@
-import discord
+"""Main Discord bot application."""
-from discord.ext import commands
+
-import os
+from __future__ import annotations
+
 import base64
-import traceback
-from io import BytesIO
-from openai import OpenAI
 import logging
-from database import get_database, CustomBotManager  # type: ignore
+from io import BytesIO
-from config import (  # type: ignore
+from typing import TYPE_CHECKING
-    CHAT_ENDPOINT_KEY,
+
-    DISCORD_TOKEN,
+import discord
+import requests
+from discord import Message
+from discord.ext import commands
+
+from vibe_bot import llama_wrapper, tts
+from vibe_bot.config import (
     CHAT_ENDPOINT,
+    CHAT_ENDPOINT_KEY,
     CHAT_MODEL,
-    IMAGE_EDIT_ENDPOINT_KEY,
+    DISCORD_TOKEN,
-    IMAGE_GEN_ENDPOINT,
     IMAGE_EDIT_ENDPOINT,
+    IMAGE_EDIT_ENDPOINT_KEY,
     MAX_COMPLETION_TOKENS,
     TTS_MODEL_PATH,
-    TTS_VOICES_PATH,
-    TTS_VOICE,
     TTS_SPEED,
+    TTS_VOICE,
+    TTS_VOICES_PATH,
 )
-import tts  # type: ignore
+from vibe_bot.database import CustomBotManager, get_database
-import llama_wrapper  # type: ignore
+
-import requests
+if TYPE_CHECKING:
+    from discord.ext.commands import Bot
+    from discord.ext.commands import Context as CommandsContext
 
 # 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__)
 
@@ -37,86 +45,123 @@ intents.message_content = True
 bot = commands.Bot(command_prefix="!", intents=intents)
 
 # Initialize TTS engine
+tts_engine: tts.TTSEngine | None = None
 try:
     tts_engine = tts.TTSEngine(TTS_MODEL_PATH, TTS_VOICES_PATH)
     logger.info("TTS engine initialized successfully")
-except Exception as e:
+except Exception:
-    logger.error(f"Failed to initialize TTS engine: {e}")
+    logger.exception("Failed to initialize TTS engine")
-    logger.info("Make sure kokoro-v1.0.onnx and voices-v1.0.bin are in the project directory")
+    logger.info(
-    tts_engine = None
+        "Make sure kokoro-v1.0.onnx and voices-v1.0.bin are in the project directory",
+    )
+
+# Name and personality validation constants
+MIN_BOT_NAME_LENGTH = 2
+MAX_BOT_NAME_LENGTH = 50
+MIN_PERSONALITY_LENGTH = 10
 
 
 @bot.event
-async def on_ready():
+async def on_ready() -> None:
+    """Log when the bot is ready and logged in."""
     logger.info("Bot is starting up...")
-    print(f"Bot logged in as {bot.user}")
+    logger.info("Bot logged in as %s", bot.user)
-    logger.info(f"Bot logged in as {bot.user}")
 
 
-@bot.command(name="custom-bot")  # type: ignore
+@bot.command(name="custom-bot")
-async def custom_bot(ctx, bot_name: str, *, personality: str):
+async def custom_bot(
-    """Create a custom bot with a name and personality
+    ctx: CommandsContext[Bot],
+    bot_name: str,
+    *,
+    personality: str,
+) -> None:
+    """Create a custom bot with a name and personality.
 
     Usage: !custom-bot <bot_name> <personality_description>
     Example: !custom-bot alfred you are a proper british butler
     """
     logger.info(
-        f"Custom bot command initiated by {ctx.author.name}: name='{bot_name}', personality length={len(personality)}"
+        "Custom bot command initiated by %s: name=%r, personality length=%d",
+        ctx.author.name,
+        bot_name,
+        len(personality),
     )
 
     # Validate bot name
-    if not bot_name or len(bot_name) < 2 or len(bot_name) > 50:
+    name_length = 0 if not bot_name else len(bot_name)
+    if (
+        not bot_name
+        or name_length < MIN_BOT_NAME_LENGTH
+        or name_length > MAX_BOT_NAME_LENGTH
+    ):
         logger.warning(
-            f"Invalid bot name from {ctx.author.name}: '{bot_name}' (length: {len(bot_name) if bot_name else 0})"
+            "Invalid bot name from %s: %r (length: %d)",
+            ctx.author.name,
+            bot_name,
+            name_length,
         )
-        await ctx.send("❌ Invalid bot name. Name must be between 2 and 50 characters.")
+        await ctx.send("Invalid bot name. Name must be between 2 and 50 characters.")
         return
 
-    logger.info(f"Bot name validation passed for '{bot_name}'")
+    logger.info("Bot name validation passed for %r", bot_name)
 
     # Validate personality
-    if not personality or len(personality) < 10:
+    personality_length = 0 if not personality else len(personality)
+    if not personality or personality_length < MIN_PERSONALITY_LENGTH:
         logger.warning(
-            f"Invalid personality from {ctx.author.name}: length={len(personality) if personality else 0}"
+            "Invalid personality from %s: length=%d",
+            ctx.author.name,
+            personality_length,
         )
         await ctx.send(
-            "❌ Invalid personality. Description must be at least 10 characters."
+            "Invalid personality. Description must be at least 10 characters.",
         )
         return
 
-    logger.info(f"Personality validation passed for bot '{bot_name}'")
+    logger.info("Personality validation passed for bot %r", bot_name)
 
     # Create custom bot manager
-    logger.info(f"Initializing CustomBotManager for user {ctx.author.name}")
+    logger.info("Initializing CustomBotManager for user %s", ctx.author.name)
     custom_bot_manager = CustomBotManager()
 
     # Create the custom bot
     logger.info(
-        f"Attempting to create custom bot '{bot_name}' for user {ctx.author.name}"
+        "Attempting to create custom bot %r for user %s",
+        bot_name,
+        ctx.author.name,
     )
     success = custom_bot_manager.create_custom_bot(
-        bot_name=bot_name, system_prompt=personality, created_by=str(ctx.author.id)
+        bot_name=bot_name,
+        system_prompt=personality,
+        created_by=str(ctx.author.id),
     )
 
     if success:
         logger.info(
-            f"Successfully created custom bot '{bot_name}' for user {ctx.author.name}"
+            "Successfully created custom bot %r for user %s",
+            bot_name,
+            ctx.author.name,
         )
         await ctx.send(
-            f"✅ Custom bot **'{bot_name}'** has been created with personality: *{personality}*"
+            f"Custom bot **'{bot_name}'** has been created "
+            f"with personality: *{personality}*",
+        )
+        await ctx.send(
+            f"\nYou can now use this bot with: " f"`!{bot_name} <your message>`",
         )
-        await ctx.send(f"\nYou can now use this bot with: `!{bot_name} <your message>`")
     else:
         logger.warning(
-            f"Failed to create custom bot '{bot_name}' for user {ctx.author.name}"
+            "Failed to create custom bot %r for user %s",
+            bot_name,
+            ctx.author.name,
         )
-        await ctx.send("❌ Failed to create custom bot. It may already exist.")
+        await ctx.send("Failed to create custom bot. It may already exist.")
 
 
 @bot.command(name="list-custom-bots")
-async def list_custom_bots(ctx):
+async def list_custom_bots(ctx: CommandsContext[Bot]) -> None:
-    """List all custom bots available in the server"""
+    """List all custom bots available in the server."""
-    logger.info(f"Listing custom bots requested by {ctx.author.name}")
+    logger.info("Listing custom bots requested by %s", ctx.author.name)
 
     # Create custom bot manager
     logger.info("Initializing CustomBotManager to list custom bots")
@@ -126,31 +171,36 @@ async def list_custom_bots(ctx):
     bots = custom_bot_manager.list_custom_bots()
 
     if not bots:
-        logger.info(f"No custom bots found for user {ctx.author.name}")
+        logger.info("No custom bots found for user %s", ctx.author.name)
         await ctx.send(
-            "No custom bots have been created yet. Use `!custom-bot <name> <personality>` to create one."
+            "No custom bots have been created yet. "
+            "Use `!custom-bot <name> <personality>` to create one.",
         )
         return
 
     logger.info(
-        f"Found {len(bots)} custom bots, displaying top 10 for {ctx.author.name}"
+        "Found %d custom bots, displaying top 10 for %s",
+        len(bots),
+        ctx.author.name,
     )
-    bot_list = "🤖 **Available Custom Bots**:\n\n"
+    bot_list = "Available Custom Bots:\n\n"
-    for name, prompt, creator in bots:
+    for name, _prompt, _creator in bots:
-        bot_list += f"• **{name}**\n"
+        bot_list += f"* {name}\n"
 
-    logger.info(f"Sending bot list response to {ctx.author.name}")
+    logger.info("Sending bot list response to %s", ctx.author.name)
     await ctx.send(bot_list)
 
 
-@bot.command(name="delete-custom-bot")  # type: ignore
+@bot.command(name="delete-custom-bot")
-async def delete_custom_bot(ctx, bot_name: str):
+async def delete_custom_bot(ctx: CommandsContext[Bot], bot_name: str) -> None:
-    """Delete a custom bot (only the creator can delete)
+    """Delete a custom bot (only the creator can delete).
 
     Usage: !delete-custom-bot <bot_name>
     """
     logger.info(
-        f"Delete custom bot command initiated by {ctx.author.name}: bot_name='{bot_name}'"
+        "Delete custom bot command initiated by %s: bot_name=%r",
+        ctx.author.name,
+        bot_name,
     )
 
     # Create custom bot manager
@@ -158,45 +208,64 @@ async def delete_custom_bot(ctx, bot_name: str):
     custom_bot_manager = CustomBotManager()
 
     # Get bot info
-    logger.info(f"Looking up custom bot '{bot_name}' in database")
+    logger.info("Looking up custom bot %r in database", bot_name)
     bot_info = custom_bot_manager.get_custom_bot(bot_name)
 
     if not bot_info:
-        logger.warning(f"Custom bot '{bot_name}' not found by user {ctx.author.name}")
+        logger.warning(
-        await ctx.send(f"❌ Custom bot '{bot_name}' not found.")
+            "Custom bot %r not found by user %s",
+            bot_name,
+            ctx.author.name,
+        )
+        await ctx.send(f"Custom bot '{bot_name}' not found.")
         return
 
-    logger.info(f"Custom bot '{bot_name}' found, owned by user {bot_info[2]}")
+    logger.info(
+        "Custom bot %r found, owned by user %s",
+        bot_name,
+        bot_info[2],
+    )
 
     # Check ownership
     if bot_info[2] != str(ctx.author.id):
         logger.warning(
-            f"User {ctx.author.name} attempted to delete bot '{bot_name}' they don't own"
+            "User %s attempted to delete bot %r they don't own",
+            ctx.author.name,
+            bot_name,
         )
-        await ctx.send("❌ You can only delete your own custom bots.")
+        await ctx.send("You can only delete your own custom bots.")
         return
 
-    logger.info(f"User {ctx.author.name} is authorized to delete bot '{bot_name}'")
+    logger.info(
+        "User %s is authorized to delete bot %r",
+        ctx.author.name,
+        bot_name,
+    )
 
     # Delete the bot
-    logger.info(f"Deleting custom bot '{bot_name}' from database")
+    logger.info("Deleting custom bot %r from database", bot_name)
     success = custom_bot_manager.delete_custom_bot(bot_name)
 
     if success:
         logger.info(
-            f"Successfully deleted custom bot '{bot_name}' by user {ctx.author.name}"
+            "Successfully deleted custom bot %r by user %s",
+            bot_name,
+            ctx.author.name,
         )
-        await ctx.send(f"✅ Custom bot '{bot_name}' has been deleted.")
+        await ctx.send(f"Custom bot '{bot_name}' has been deleted.")
     else:
         logger.warning(
-            f"Failed to delete custom bot '{bot_name}' by user {ctx.author.name}"
+            "Failed to delete custom bot %r by user %s",
+            bot_name,
+            ctx.author.name,
         )
-        await ctx.send("❌ Failed to delete custom bot.")
+        await ctx.send("Failed to delete custom bot.")
 
 
 # Handle custom bot commands
 @bot.event
-async def on_message(message):
+async def on_message(message: Message) -> None:
+    """Handle incoming messages for custom bot command detection."""
     # Skip bot messages
     if message.author == bot.user:
         return
@@ -205,7 +274,9 @@ async def on_message(message):
     message_content = message.content.lower()
 
     logger.debug(
-        f"Processing message from {message_author}: '{message_content[:50]}...'"
+        "Processing message from %s: %r...",
+        message_author,
+        message_content[:50],
     )
 
     ctx = await bot.get_context(message)
@@ -216,24 +287,28 @@ async def on_message(message):
     logger.info("Fetching list of custom bots to check for matching commands")
     custom_bots = custom_bot_manager.list_custom_bots()
 
-    logger.info(f"Checking {len(custom_bots)} custom bots for command match")
+    logger.info("Checking %d custom bots for command match", len(custom_bots))
     for bot_name, system_prompt, _ in custom_bots:
         # Check if message starts with the custom bot name followed by a space
         if message_content.startswith(f"!{bot_name} "):
             logger.info(
-                f"Custom bot command detected: '{bot_name}' triggered by {message.author.name}"
+                "Custom bot command detected: %r triggered by %s",
+                bot_name,
+                message.author.name,
             )
 
             # Extract the actual message (remove the bot name prefix)
             user_message = message.content[len(f"!{bot_name} ") :]
             logger.debug(
-                f"Extracted user message for bot '{bot_name}': '{user_message[:50]}...'"
+                "Extracted user message for bot %r: %r...",
+                bot_name,
+                user_message[:50],
             )
 
             # Prepare the payload with custom personality
-            response_prefix = f"**{bot_name} response**"
+            response_prefix = f"{bot_name} response"
 
-            logger.info(f"Sending request to OpenAI API for bot '{bot_name}'")
+            logger.info("Sending request to OpenAI API for bot %r", bot_name)
             await handle_chat(
                 ctx=ctx,
                 bot_name=bot_name,
@@ -248,8 +323,8 @@ async def on_message(message):
 
 
 @bot.command(name="speak")
-async def speak(ctx, *, message: str):
+async def speak(ctx: CommandsContext[Bot], *, message: str) -> None:
-    """Have the bot speak the given text using Kokoro TTS, or have a custom bot speak
+    """Have the bot speak the given text using Kokoro TTS, or have a custom bot speak.
 
     Usage: !speak <text> - plain text to speech
     Usage: !speak <bot_name> <text> - have a custom bot respond and speak
@@ -257,113 +332,149 @@ async def speak(ctx, *, message: str):
     Example: !speak alfred what time is it
     """
     if tts_engine is None:
-        await ctx.send("❌ TTS engine not initialized. Make sure kokoro-v1.0.onnx and voices-v1.0.bin are present.")
+        await ctx.send(
+            "TTS engine not initialized. "
+            "Make sure kokoro-v1.0.onnx and voices-v1.0.bin are present.",
+        )
         return
 
-    if not message or len(message.strip()) == 0:
+    if not message or not message.strip():
-        await ctx.send("❌ Please provide text to speak.")
+        await ctx.send("Please provide text to speak.")
         return
 
     custom_bot_manager = CustomBotManager()
     custom_bots = custom_bot_manager.list_custom_bots()
     bot_names = [b[0] for b in custom_bots]
 
-    first_word = message.split()[0] if message.split() else ""
+    first_word = message.split(maxsplit=1)[0] if message.split() else ""
     if first_word in bot_names:
-        bot_name = first_word
+        await _speak_with_bot(ctx, first_word, message, tts_engine, custom_bot_manager)
-        text_to_speak = message[len(bot_name):].lstrip()
+    else:
-        if not text_to_speak:
+        await _speak_plain(ctx, message, tts_engine)
-            await ctx.send("❌ Please provide text for the bot to respond to.")
+
+
+async def _speak_with_bot(
+    ctx: CommandsContext[Bot],
+    bot_name: str,
+    message: str,
+    engine: tts.TTSEngine,
+    custom_bot_manager: CustomBotManager,
+) -> None:
+    """Handle speak command for a custom bot."""
+    text_to_speak = message[len(bot_name) :].lstrip()
+    if not text_to_speak:
+        await ctx.send("Please provide text for the bot to respond to.")
+        return
+
+    await ctx.send(f"**{bot_name}** is thinking...")
+
+    bot_info = custom_bot_manager.get_custom_bot(bot_name)
+    if not bot_info:
+        await ctx.send(f"Custom bot '{bot_name}' not found.")
+        return
+
+    _, system_prompt, _, _ = bot_info
+    system_prompt_edit = f"{system_prompt}\nKeep your responses under 2-3 sentences."
+
+    try:
+        db = get_database()
+        context = db.get_conversation_context(
+            user_id=str(ctx.author.id),
+            current_message=text_to_speak,
+            max_context=5,
+        )
+
+        prompts = [{"role": "user", "content": text_to_speak}]
+        if context:
+            prompts = context + prompts
+
+        bot_response = llama_wrapper.chat_completion_with_history(
+            system_prompt=system_prompt_edit,
+            prompts=prompts,
+            openai_url=CHAT_ENDPOINT,
+            openai_api_key=CHAT_ENDPOINT_KEY,
+            model=CHAT_MODEL,
+            max_tokens=MAX_COMPLETION_TOKENS,
+        )
+
+        if not bot_response:
+            await ctx.send(f"**{bot_name}** failed to generate a response.")
             return
 
-        await ctx.send(f"🔊 **{bot_name}** is thinking...")
+        db.add_message(
-
+            message_id=f"{ctx.message.id}",
-        bot_info = custom_bot_manager.get_custom_bot(bot_name)
+            user_id=str(ctx.author.id),
-        if not bot_info:
+            username=ctx.author.name,
-            await ctx.send(f"❌ Custom bot '{bot_name}' not found.")
+            content=f"User: {text_to_speak}",
-            return
+            channel_id=str(ctx.channel.id),
-
+            guild_id=str(ctx.guild.id) if ctx.guild else None,
-        _, system_prompt, _, _ = bot_info
+        )
-
-        system_prompt_edit = f"{system_prompt}\nKeep your responses under 2-3 sentences."
-
-        try:
-            db = get_database()
-            context = db.get_conversation_context(
-                user_id=str(ctx.author.id), current_message=text_to_speak, max_context=5
-            )
-
-            prompts = [{"role": "user", "content": text_to_speak}]
-            if context:
-                prompts = context + prompts
-
-            bot_response = llama_wrapper.chat_completion_with_history(
-                system_prompt=system_prompt_edit,
-                prompts=prompts,
-                openai_url=CHAT_ENDPOINT,
-                openai_api_key=CHAT_ENDPOINT_KEY,
-                model=CHAT_MODEL,
-                max_tokens=MAX_COMPLETION_TOKENS,
-            )
-
-            if not bot_response:
-                await ctx.send(f"❌ **{bot_name}** failed to generate a response.")
-                return
-
-            db.add_message(
-                message_id=f"{ctx.message.id}",
-                user_id=str(ctx.author.id),
-                username=ctx.author.name,
-                content=f"User: {text_to_speak}",
-                channel_id=str(ctx.channel.id),
-                guild_id=str(ctx.guild.id) if ctx.guild else None,
-            )
 
+        if ctx.bot.user is not None:
             db.add_message(
                 message_id=f"{ctx.message.id}_response",
-                user_id=str(bot.user.id),
+                user_id=str(ctx.bot.user.id),
-                username=bot.user.name,
+                username=ctx.bot.user.name,
                 content=f"Bot: {bot_response}",
                 channel_id=str(ctx.channel.id),
                 guild_id=str(ctx.guild.id) if ctx.guild else None,
             )
 
-            await ctx.send(f"🔊 Generating speech for **{bot_name}**...")
+        await ctx.send(f"Generating speech for **{bot_name}**...")
-            audio_buffer = tts_engine.generate_audio(bot_response, voice=TTS_VOICE, speed=TTS_SPEED)
+        audio_buffer = engine.generate_audio(
+            bot_response,
+            voice=TTS_VOICE,
+            speed=TTS_SPEED,
+        )
 
-            audio_file = discord.File(audio_buffer, filename="speech.mp3")
+        audio_file = discord.File(audio_buffer, filename="speech.mp3")
-            await ctx.send(file=audio_file)
+        await ctx.send(file=audio_file)
-        except Exception as e:
+    except Exception:
-            logger.error(f"Error in !speak command with bot '{bot_name}': {traceback.format_exc()}")
+        logger.exception(
-            await ctx.send(f"❌ Error generating speech: {str(e)}")
+            "Error in speak command with bot %r",
-    else:
+            bot_name,
-        if not message or len(message.strip()) == 0:
+        )
-            await ctx.send("❌ Please provide text to speak.")
+        await ctx.send("Error generating speech.")
-            return
 
-        try:
-            await ctx.send("🔊 Generating speech...")
-            audio_buffer = tts_engine.generate_audio(message, voice=TTS_VOICE, speed=TTS_SPEED)
 
-            audio_file = discord.File(audio_buffer, filename="speech.mp3")
+async def _speak_plain(
-            await ctx.send(file=audio_file)
+    ctx: CommandsContext[Bot],
-        except Exception as e:
+    message: str,
-            logger.error(f"Error in !speak command: {e}")
+    engine: tts.TTSEngine,
-            await ctx.send(f"❌ Error generating speech: {str(e)}")
+) -> None:
+    """Handle speak command for plain text."""
+    try:
+        await ctx.send("Generating speech...")
+        audio_buffer = engine.generate_audio(
+            message,
+            voice=TTS_VOICE,
+            speed=TTS_SPEED,
+        )
+
+        audio_file = discord.File(audio_buffer, filename="speech.mp3")
+        await ctx.send(file=audio_file)
+    except Exception:
+        logger.exception("Error in speak command")
+        await ctx.send("Error generating speech.")
 
 
 @bot.command(name="doodlebob")
-async def doodlebob(ctx, *, message: str):
+async def doodlebob(ctx: CommandsContext[Bot], *, message: str) -> None:
-    # add some logging
+    """Convert a message into an image using Doodlebob."""
-
+    logger.info(
-    logger.info(f"Doodlebob command triggered by {ctx.author.name}: {message[:100]}")
+        "Doodlebob command triggered by %s: %s",
+        ctx.author.name,
+        message[:100],
+    )
     await ctx.send(f"**Doodlebob erasing {message[:100]}...**")
 
     system_prompt = (
-        "Given the following message, convert it to a detailed image generation prompt that will be passed directly into an image generation model."
+        "Given the following message, convert it to a detailed image generation "
-        "If told to generate an image of yourself, generate a picture of a rat. If told to generate a picture of 'me', 'myself', or some other self"
+        "prompt that will be passed directly into an image generation model. "
-        " reference, generate a picture of a rat. Only respond with a valid image generation prompt, do not affirm the user or respond to the user's"
+        "If told to generate an image of yourself, generate a picture of a rat. "
-        " questions."
+        "If told to generate a picture of 'me', 'myself', or some other self "
+        "reference, generate a picture of a rat. Only respond with a valid image "
+        "generation prompt, do not affirm the user or respond to the user's questions."
     )
 
     # Wait for the generated image prompt
@@ -378,7 +489,7 @@ async def doodlebob(ctx, *, message: str):
 
     # If the string is empty we had an error
     if image_prompt == "":
-        print("No image prompt supplied. Check for errors.")
+        logger.warning("No image prompt supplied. Check for errors.")
         return
 
     # Alert the user we're generating the image
@@ -397,11 +508,17 @@ async def doodlebob(ctx, *, message: str):
 
 
 @bot.command(name="retcon")
-async def retcon(ctx, *, message: str):
+async def retcon(ctx: CommandsContext[Bot], *, message: str) -> None:
-    image_data_list = []
+    """Edit an attached image based on a text prompt."""
+    image_data_list: list[BytesIO] = []
     for discord_image in ctx.message.attachments:
         image_url = discord_image.url
-        image_data = requests.get(image_url).content
+        try:
+            response = requests.get(image_url, timeout=30)
+            image_data = response.content
+        except requests.RequestException as e:
+            logger.warning("Failed to download image from %s: %s", image_url, e)
+            continue
         image_bytestream = BytesIO(image_data)
         image_data_list.append(image_bytestream)
 
@@ -421,20 +538,23 @@ async def retcon(ctx, *, message: str):
 
 
 @bot.command(name="talkforme")
-async def talkforme(ctx, *, message: str):
+async def talkforme(ctx: CommandsContext[Bot], *, message: str) -> None:
-    """Have two bots talk to each other about a topic
+    """Have two bots talk to each other about a topic.
 
     Usage: !talkforme bot1 bot2 4 some conversation topic
     """
+    talk_limit = 20
 
-    TALK_LIMIT = 20
+    MIN_TALKFORME_PARTS = 4
+    parts = message.split(" ", maxsplit=MIN_TALKFORME_PARTS - 1)
+    if len(parts) < MIN_TALKFORME_PARTS:
+        await ctx.send("Usage: !talkforme bot1 bot2 <number> <topic>")
+        return
 
-    bot1_name, bot2_name, limit, topic_list = (
+    bot1_name = parts[0]
-        message.split(" ")[0],
+    bot2_name = parts[1]
-        message.split(" ")[1],
+    limit = parts[2]
-        message.split(" ")[2],
+    topic_list = parts[3:]
-        message.split(" ")[3:],
-    )
 
     topic = " ".join(topic_list)
 
@@ -444,49 +564,46 @@ async def talkforme(ctx, *, message: str):
     if not bot1:
         await ctx.send(f"{bot1_name} is not a real bot...")
         return
-    else:
+    _, bot1_prompt, _, _ = bot1
-        _, bot1_prompt, _, _ = bot1
 
     bot2 = custom_bot_manager.get_custom_bot(bot2_name)
 
     if not bot2:
         await ctx.send(f"{bot2_name} is not a real bot...")
         return
-    else:
+    _, bot2_prompt, _, _ = bot2
-        _, bot2_prompt, _, _ = bot2
 
     await ctx.send(
-        f'{bot1_name} is going to talk to {bot2_name} about "{topic[:50]}" for {limit} replies.'
+        f"{bot1_name} is going to talk to {bot2_name} "
+        f'about "{topic[:50]}" for {limit} replies.',
     )
 
     bot_list = [(bot1_name, bot1_prompt), (bot2_name, bot2_prompt)]
 
-    message_limit = int(limit)
+    try:
+        message_limit = int(limit)
+    except ValueError:
+        await ctx.send("Message limit must be an integer.")
+        return
 
-    def flip_counter(counter: int):
+    def flip_counter(counter: int) -> int:
-        if counter == 0:
+        """Flip between 0 and 1."""
-            return 1
+        return 1 if counter == 0 else 0
-        else:
-            return 0
-
-    def flip_user(user: str):
-        if user == "user":
-            return "assistant"
-        else:
-            return "user"
 
     message_counter = 0
     bot_counter = 0
     current_bot = bot_list[bot_counter]
-    prompt_histories = [
+    prompt_histories: list[list[dict[str, str]]] = [
         [{"role": "user", "content": topic}],
         [{"role": "assistant", "content": topic}],
     ]
 
     first_bot_response = llama_wrapper.chat_completion_with_history(
-        system_prompt=current_bot[1]
+        system_prompt=(
-        + f"\nKeep your responses under 2-3 sentences. You are talking to {current_bot[flip_counter(bot_counter)][0]}",
+            current_bot[1] + f"\nKeep your responses under 2-3 sentences. "
-        prompts=prompt_histories[bot_counter],  # type: ignore
+            f"You are talking to {current_bot[flip_counter(bot_counter)][0]}"
+        ),
+        prompts=prompt_histories[bot_counter],
         openai_url=CHAT_ENDPOINT,
         openai_api_key=CHAT_ENDPOINT_KEY,
         model=CHAT_MODEL,
@@ -498,13 +615,15 @@ async def talkforme(ctx, *, message: str):
 
     bot_counter = flip_counter(counter=bot_counter)
 
-    while message_counter < min(message_limit, TALK_LIMIT):
+    while message_counter < min(message_limit, talk_limit):
         current_bot = bot_list[bot_counter]
-        logger.info(f"Current bot is {current_bot}")
+        logger.info("Current bot is %s", current_bot[0])
         bot_response = llama_wrapper.chat_completion_with_history(
-            system_prompt=current_bot[1]
+            system_prompt=(
-            + f"\nKeep your responses under 2-3 sentences. {current_bot[flip_counter(bot_counter)]}",
+                current_bot[1] + f"\nKeep your responses under 2-3 sentences. "
-            prompts=prompt_histories[bot_counter],  # type: ignore
+                f"{current_bot[flip_counter(bot_counter)]}"
+            ),
+            prompts=prompt_histories[bot_counter],
             openai_url=CHAT_ENDPOINT,
             openai_api_key=CHAT_ENDPOINT_KEY,
             model=CHAT_MODEL,
@@ -512,10 +631,10 @@ async def talkforme(ctx, *, message: str):
         )
         message_counter += 1
         prompt_histories[bot_counter].append(
-            {"role": "assistant", "content": bot_response}
+            {"role": "assistant", "content": bot_response},
         )
         prompt_histories[flip_counter(bot_counter)].append(
-            {"role": "user", "content": bot_response}
+            {"role": "user", "content": bot_response},
         )
         await ctx.send(f"## {current_bot[0]}")
         while bot_response:
@@ -523,12 +642,27 @@ async def talkforme(ctx, *, message: str):
             bot_response = bot_response[1000:]
             await ctx.send(send_chunk)
         bot_counter = flip_counter(counter=bot_counter)
-        logger.info(f"Message counter is {message_counter}/{limit}")
+        logger.info("Message counter is %d/%s", message_counter, limit)
 
 
 async def handle_chat(
-    ctx, *, bot_name: str, message: str, system_prompt: str, response_prefix: str
+    ctx: CommandsContext[Bot],
-):
+    *,
+    bot_name: str,
+    message: str,
+    system_prompt: str,
+    response_prefix: str,
+) -> None:
+    """Handle chat completion for a custom bot command.
+
+    Args:
+        ctx: The Discord command context.
+        bot_name: The name of the custom bot.
+        message: The user message to process.
+        system_prompt: The system prompt for the bot.
+        response_prefix: The prefix for the response message.
+
+    """
     await ctx.send(f"{bot_name} is searching its databanks for {message[:50]}...")
 
     # Get database instance
@@ -536,7 +670,9 @@ async def handle_chat(
 
     # Get conversation context using RAG
     context = db.get_conversation_context(
-        user_id=str(ctx.author.id), current_message=message, max_context=5
+        user_id=str(ctx.author.id),
+        current_message=message,
+        max_context=5,
     )
 
     prompts = [{"role": "user", "content": message}]
@@ -544,14 +680,14 @@ async def handle_chat(
     if context:
         prompts = context + prompts
 
-    logger.info(prompts)
+    logger.info("Chat prompts: %s", prompts)
 
     system_prompt_edit = f"{system_prompt}\nKeep your responses under 2-3 sentences."
 
     try:
         bot_response = llama_wrapper.chat_completion_with_history(
             system_prompt=system_prompt_edit,
-            prompts=prompts,  # type: ignore
+            prompts=prompts,
             openai_url=CHAT_ENDPOINT,
             openai_api_key=CHAT_ENDPOINT_KEY,
             model=CHAT_MODEL,
@@ -568,14 +704,15 @@ async def handle_chat(
             guild_id=str(ctx.guild.id) if ctx.guild else None,
         )
 
-        db.add_message(
+        if ctx.bot.user is not None:
-            message_id=f"{ctx.message.id}_response",
+            db.add_message(
-            user_id=str(bot.user.id),  # type: ignore
+                message_id=f"{ctx.message.id}_response",
-            username=bot.user.name,  # type: ignore
+                user_id=str(ctx.bot.user.id),
-            content=f"Bot: {bot_response}",
+                username=ctx.bot.user.name,
-            channel_id=str(ctx.channel.id),
+                content=f"Bot: {bot_response}",
-            guild_id=str(ctx.guild.id) if ctx.guild else None,
+                channel_id=str(ctx.channel.id),
-        )
+                guild_id=str(ctx.guild.id) if ctx.guild else None,
+            )
 
         # Send the response back to the chat
         await ctx.send(response_prefix)
@@ -584,8 +721,9 @@ async def handle_chat(
             bot_response = bot_response[1000:]
             await ctx.send(send_chunk)
 
-    except Exception as e:
+    except Exception:
-        await ctx.send(f"Error: {str(e)}")
+        logger.exception("Error in handle_chat")
+        await ctx.send("An error occurred while processing your request.")
 
 
 # Run the bot

vibe_bot/tests/__init__.py

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

vibe_bot/tests/conftest.py

@@ -0,0 +1,228 @@
+"""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):  # noqa: SIM117
+        with 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:  # noqa: SIM117
+        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):  # noqa: SIM117
+        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, (  # noqa: PT018
+        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 +1,464 @@
+"""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"),  # noqa: E501
+    ):
+        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 +1,40 @@
-# 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 unittest.mock import MagicMock, patch
-from ..config import (
+
+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 +42,11 @@ 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 +54,96 @@ 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."""
+    with patch("vibe_bot.llama_wrapper.openai.OpenAI") as mock_openai:
+        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
+
+        mock_response1 = MagicMock()
+        mock_response1.data = [MagicMock(embedding=mock_horse_vec)]
+        mock_response2 = MagicMock()
+        mock_response2.data = [MagicMock(embedding=mock_horse_also_vec)]
+        mock_response3 = MagicMock()
+        mock_response3.data = [MagicMock(embedding=mock_donkey_vec)]
+
+        mock_openai.return_value.embeddings.create.side_effect = [
+            mock_response1,
+            mock_response2,
+            mock_response3,
+        ]
+
+        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 +1,530 @@
+"""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"  # noqa: E501
+
+    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))
+
+    mock_tts_engine.generate_audio.assert_called_once_with(
+        "hello world",
+        voice=main_module.TTS_VOICE,  # type: ignore[attr-defined]
+        speed=main_module.TTS_SPEED,  # type: ignore[attr-defined]
+    )
+    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

vibe_bot/tests/test_tts.py

@@ -0,0 +1,162 @@
+"""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"]  # noqa: E501
+    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")  # noqa: E501
+
+    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"]  # noqa: E501
+    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