human cleanup

vibe_bot/config.py

@@ -0,0 +1,85 @@
+from dotenv import load_dotenv
+import os
+import logging
+
+# Configure logging
+logging.basicConfig(
+    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", "")
+
+# Endpoints
+CHAT_ENDPOINT = os.getenv("CHAT_ENDPOINT", "")
+COMPLETION_ENDPOINT = os.getenv("COMPLETION_ENDPOINT", "")
+IMAGE_GEN_ENDPOINT = os.getenv("IMAGE_GEN_ENDPOINT", "")
+IMAGE_EDIT_ENDPOINT = os.getenv("IMAGE_EDIT_ENDPOINT", "")
+EMBEDDING_ENDPOINT = os.getenv("EMBEDDING_ENDPOINT", "")
+MAX_COMPLETION_TOKENS = int(os.getenv("MAX_COMPLETION_TOKENS", "1000"))
+
+# API Keys
+CHAT_ENDPOINT_KEY = os.getenv("CHAT_ENDPOINT_KEY", "placeholder")
+COMPLETION_ENDPOINT_KEY = os.getenv("COMPLETION_ENDPOINT_KEY", "placeholder")
+IMAGE_GEN_ENDPOINT_KEY = os.getenv("IMAGE_GEN_ENDPOINT_KEY", "placeholder")
+IMAGE_EDIT_ENDPOINT_KEY = os.getenv("IMAGE_EDIT_ENDPOINT_KEY", "placeholder")
+EMBEDDING_ENDPOINT_KEY = os.getenv("EMBEDDING_ENDPOINT_KEY", "placeholder")
+
+# Models
+CHAT_MODEL = os.getenv("CHAT_MODEL", "")
+COMPLETION_MODEL = os.getenv("COMPLETION_MODEL", "")
+IMAGE_GEN_MODEL = os.getenv("IMAGE_GEN_MODEL", "")
+IMAGE_EDIT_MODEL = os.getenv("IMAGE_EDIT_MODEL", "")
+EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL", "")
+
+# Database and embeddings
+DB_PATH = os.getenv("DB_PATH", "chat_history.db")
+EMBEDDING_DIMENSION = 2048
+MAX_HISTORY_MESSAGES = int(os.getenv("MAX_HISTORY_MESSAGES", "1000"))
+SIMILARITY_THRESHOLD = float(os.getenv("SIMILARITY_THRESHOLD", "0.7"))
+TOP_K_RESULTS = int(os.getenv("TOP_K_RESULTS", "5"))
+
+# Check token
+if not DISCORD_TOKEN:
+    raise Exception("DISCORD_TOKEN required.")
+
+# Check endpoints
+if not CHAT_ENDPOINT:
+    raise Exception("CHAT_ENDPOINT required.")
+
+if not COMPLETION_ENDPOINT:
+    raise Exception("COMPLETION_ENDPOINT required.")
+
+if not IMAGE_GEN_ENDPOINT:
+    raise Exception("IMAGE_GEN_ENDPOINT required.")
+
+if not IMAGE_EDIT_ENDPOINT:
+    raise Exception("IMAGE_EDIT_ENDPOINT required.")
+
+if not EMBEDDING_ENDPOINT:
+    raise Exception("EMBEDDING_ENDPOINT required.")
+
+# Check models
+if not CHAT_MODEL:
+    raise Exception("CHAT_MODEL required.")
+
+if not COMPLETION_MODEL:
+    raise Exception("COMPLETION_MODEL required.")
+
+if not IMAGE_GEN_MODEL:
+    raise Exception("IMAGE_GEN_MODEL required.")
+
+if not IMAGE_EDIT_MODEL:
+    raise Exception("IMAGE_EDIT_MODEL required.")
+
+if not EMBEDDING_MODEL:
+    raise Exception("EMBEDDING_MODEL required.")
+
+logger.info(f"CHAT_ENDPOINT set to {CHAT_ENDPOINT}")
+logger.info(f"COMPLETION_ENDPOINT set to {COMPLETION_ENDPOINT}")
+logger.info(f"IMAGE_GEN_ENDPOINT set to {IMAGE_GEN_ENDPOINT}")
+logger.info(f"IMAGE_EDIT_ENDPOINT set to {IMAGE_EDIT_ENDPOINT}")
+logger.info(f"EMBEDDING_ENDPOINT set to {EMBEDDING_ENDPOINT}")

vibe_bot/database.py

@@ -1,32 +1,27 @@
 import sqlite3
-import os
 from typing import Optional, List, Tuple
 from datetime import datetime
 import numpy as np
 from openai import OpenAI
 import logging
 
+import llama_wrapper  # type: ignore
+from config import (  # type: ignore
+    DB_PATH,
+    EMBEDDING_MODEL,
+    EMBEDDING_ENDPOINT,
+    EMBEDDING_ENDPOINT_KEY,
+    MAX_HISTORY_MESSAGES,
+    SIMILARITY_THRESHOLD,
+    TOP_K_RESULTS,
+)
+
 # Configure logging
 logging.basicConfig(
-    level=logging.INFO,
+    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
 )
 logger = logging.getLogger(__name__)
 
-# Database configuration
-DB_PATH = os.getenv("DB_PATH", "chat_history.db")
-EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL", "qwen3-embed-4b")
-EMBEDDING_DIMENSION = 2048  # Default for qwen3-embed-4b
-MAX_HISTORY_MESSAGES = int(os.getenv("MAX_HISTORY_MESSAGES", "1000"))
-SIMILARITY_THRESHOLD = float(os.getenv("SIMILARITY_THRESHOLD", "0.7"))
-TOP_K_RESULTS = int(os.getenv("TOP_K_RESULTS", "5"))
-
-# OpenAI configuration
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "placeholder")
-OPENAI_API_EMBED_ENDPOINT = os.getenv(
-    "OPENAI_API_EMBED_ENDPOINT", "https://llama-embed.reeselink.com"
-)
-
 
 class ChatDatabase:
     """SQLite database with RAG support for storing chat history using OpenAI embeddings."""
@@ -34,7 +29,9 @@ class ChatDatabase:
     def __init__(self, db_path: str = DB_PATH):
         logger.info(f"Initializing ChatDatabase with path: {db_path}")
         self.db_path = db_path
-        self.client = OpenAI(base_url=OPENAI_API_EMBED_ENDPOINT, api_key=OPENAI_API_KEY)
+        self.client = OpenAI(
+            base_url=EMBEDDING_ENDPOINT, api_key=EMBEDDING_ENDPOINT_KEY
+        )
         logger.info("Connecting to OpenAI API for embeddings")
         self._initialize_database()
 
@@ -96,36 +93,6 @@ class ChatDatabase:
         logger.info("Database initialization completed successfully")
         conn.close()
 
-    def _generate_embedding(self, text: str) -> List[float]:
-        """Generate embedding for text using OpenAI API."""
-        logger.debug(f"Generating embedding for text (length: {len(text)})")
-        try:
-            logger.info(f"Calling OpenAI API to generate embedding with model: {EMBEDDING_MODEL}")
-            response = self.client.embeddings.create(
-                model=EMBEDDING_MODEL, input=text, encoding_format="float"
-            )
-            logger.debug("OpenAI API response received successfully")
-            
-            # The embedding is returned as a nested list: [[embedding_values]]
-            # We need to extract the inner list
-            embedding_data = response[0].embedding
-            if isinstance(embedding_data, list) and len(embedding_data) > 0:
-                # The first element might be the embedding array itself or a nested list
-                first_item = embedding_data[0]
-                if isinstance(first_item, list):
-                    # Handle nested structure: [[values]] -> [values]
-                    logger.debug("Extracted embedding from nested structure [[values]]")
-                    return first_item
-                else:
-                    # Handle direct structure: [values]
-                    logger.debug("Extracted embedding from direct structure [values]")
-                    return embedding_data
-            logger.warning("Embedding data is empty or invalid")
-            return []
-        except Exception as e:
-            logger.error(f"Error generating embedding: {e}")
-            return None
-
     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")
@@ -142,7 +109,9 @@ class ChatDatabase:
 
     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)}")
+        logger.debug(
+            f"Calculating cosine similarity between vectors of dimension {len(vec1)}"
+        )
         result = np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
         logger.debug(f"Similarity calculated: {result:.4f}")
         return result
@@ -163,7 +132,9 @@ class ChatDatabase:
 
         try:
             # Insert message
-            logger.debug(f"Inserting message into chat_messages table: message_id={message_id}")
+            logger.debug(
+                f"Inserting message into chat_messages table: message_id={message_id}"
+            )
             cursor.execute(
                 """
                 INSERT OR REPLACE INTO chat_messages 
@@ -176,9 +147,16 @@ class ChatDatabase:
 
             # Generate and store embedding
             logger.info(f"Generating embedding for message {message_id}")
-            embedding = self._generate_embedding(content)
+            embedding = llama_wrapper.embedding(
+                content,
+                openai_url=EMBEDDING_ENDPOINT,
+                openai_api_key=EMBEDDING_ENDPOINT_KEY,
+                model=EMBEDDING_MODEL,
+            )
             if embedding:
-                logger.debug(f"Embedding generated successfully for message {message_id}, storing in database")
+                logger.debug(
+                    f"Embedding generated successfully for message {message_id}, storing in database"
+                )
                 cursor.execute(
                     """
                     INSERT OR REPLACE INTO message_embeddings 
@@ -187,9 +165,13 @@ class ChatDatabase:
                 """,
                     (message_id, self._vector_to_bytes(embedding)),
                 )
-                logger.debug(f"Embedding stored in message_embeddings table for message {message_id}")
+                logger.debug(
+                    f"Embedding stored in message_embeddings table for message {message_id}"
+                )
             else:
-                logger.warning(f"Failed to generate embedding for message {message_id}, skipping embedding storage")
+                logger.warning(
+                    f"Failed to generate embedding for message {message_id}, skipping embedding storage"
+                )
 
             # Clean up old messages if exceeding limit
             logger.info("Checking if cleanup of old messages is needed")
@@ -268,9 +250,14 @@ class ChatDatabase:
         query: str,
         top_k: int = TOP_K_RESULTS,
         min_similarity: float = SIMILARITY_THRESHOLD,
-    ) -> List[Tuple[str, str, str, float]]:
+    ) -> List[Tuple[str, str, float]]:
         """Search for messages similar to the query using embeddings."""
-        query_embedding = self._generate_embedding(query)
+        query_embedding = llama_wrapper.embedding(
+            text=query,
+            model=EMBEDDING_MODEL,
+            openai_url=EMBEDDING_ENDPOINT,
+            openai_api_key=EMBEDDING_ENDPOINT_KEY,
+        )
         if not query_embedding:
             return []
 
@@ -285,19 +272,28 @@ class ChatDatabase:
             SELECT cm.message_id, cm.content, me.embedding 
             FROM chat_messages cm
             JOIN message_embeddings me ON cm.message_id = me.message_id
+            WHERE cm.username != 'vibe-bot'
         """
         )
         rows = cursor.fetchall()
 
-        results = []
+        results: list[tuple[str, str, float]] = []
         for message_id, content, embedding_blob in rows:
             embedding_vector = self._bytes_to_vector(embedding_blob)
             similarity = self._calculate_similarity(query_vector, embedding_vector)
 
             if similarity >= min_similarity:
-                results.append(
+                cursor.execute(
-                    (message_id, content[:500], similarity)
+                    """
-                )  # Limit content length
+                        SELECT content
+                        FROM chat_messages
+                        WHERE message_id = ?
+                        ORDER BY timestamp DESC
+                    """,
+                    (f"{message_id}_response",),
+                )
+                response: str = cursor.fetchone()[0]
+                results.append((content, response, similarity))
 
         conn.close()
 
@@ -305,28 +301,48 @@ class ChatDatabase:
         results.sort(key=lambda x: x[2], reverse=True)
         return results[:top_k]
 
-    def get_user_history(
+    def get_user_history(self, user_id: str, limit: int = 20) -> list[tuple[str, str]]:
-        self, user_id: str, limit: int = 20
-    ) -> List[Tuple[str, str, datetime]]:
         """Get message history for a specific user."""
         conn = sqlite3.connect(self.db_path)
         cursor = conn.cursor()
 
+        logger.info(f"Fetching last {limit} user messages")
         cursor.execute(
             """
             SELECT message_id, content, timestamp
             FROM chat_messages
-            WHERE user_id = ?
+            WHERE username != 'vibe-bot'
             ORDER BY timestamp DESC
             LIMIT ?
         """,
-            (user_id, limit),
+            (limit,),
         )
 
         messages = cursor.fetchall()
+
+        # Format is [user message, bot response]
+        conversations: list[tuple[str, str]] = []
+        for message in messages:
+            msg_content: str = message[1]
+            logger.info(f"Finding response for {msg_content[:50]}")
+            cursor.execute(
+                """
+                    SELECT content
+                    FROM chat_messages
+                    WHERE message_id = ?
+                    ORDER BY timestamp DESC
+                """,
+                (f"{message[0]}_response",),
+            )
+            response_content: str = cursor.fetchone()
+            if response_content:
+                logger.info(f"Found response: {response_content[0][:50]}")
+                conversations.append((msg_content, response_content[0]))
+            else:
+                logger.info("No response found")
         conn.close()
 
-        return messages
+        return conversations
 
     def get_conversation_context(
         self, user_id: str, current_message: str, max_context: int = 5
@@ -344,15 +360,19 @@ class ChatDatabase:
         context_parts = []
 
         # Add recent messages
-        for message_id, content, timestamp in recent_messages:
+        for user_message, bot_message in recent_messages:
-            context_parts.append(f"[{timestamp}] User: {content}")
+            combined_content = f"[Recent chat]\n{user_message}\n{bot_message}"
+            context_parts.append(combined_content)
 
         # Add similar messages
-        for message_id, content, similarity in similar_messages:
+        for user_message, bot_message, similarity in similar_messages:
-            if f"[{content}" not in "\n".join(context_parts):  # Avoid duplicates
+            combined_content = f"{user_message}\n{bot_message}"
-                context_parts.append(f"[Similar] {content}")
+            if combined_content not in "\n".join(context_parts):
+                context_parts.append(f"[You remember]\n{combined_content}")
 
-        return "\n".join(context_parts[-max_context * 2 :])  # Limit total context
+        # Conversation history needs to be delivered in "newest context last" order
+        context_parts.reverse()
+        return "\n".join(context_parts[-max_context * 4 :])  # Limit total context
 
     def clear_all_messages(self):
         """Clear all messages and embeddings from the database."""
@@ -390,6 +410,7 @@ class CustomBotManager:
         conn = sqlite3.connect(self.db_path)
         cursor = conn.cursor()
 
+        # Create table to hold custom bots
         cursor.execute(
             """
             CREATE TABLE IF NOT EXISTS custom_bots (
@@ -461,8 +482,9 @@ class CustomBotManager:
         if user_id:
             cursor.execute(
                 """
-                SELECT bot_name, system_prompt, created_by
+                SELECT bot_name, system_prompt, name
-                FROM custom_bots
+                FROM custom_bots cb, username_map um
+                JOIN username_map ON custom_bots.created_by = username_map.id
                 WHERE is_active = 1
                 ORDER BY created_at DESC
             """

vibe_bot/llama_wrapper.py

@@ -5,9 +5,12 @@
 import openai
 from typing import Iterable
 from openai.types.chat import ChatCompletionMessageParam
+from openai._types import FileTypes, SequenceNotStr
+from typing import Union
+from io import BufferedReader, BytesIO
 
 
-def chat_completion_think(
+def chat_completion(
     system_prompt: str,
     user_prompt: str,
     openai_url: str,
@@ -80,35 +83,56 @@ def chat_completion_instruct(
         return ""
 
 
-def image_generation(prompt: str, n=1) -> str:
+def image_generation(prompt: str, openai_url: str, openai_api_key: str, n=1) -> str:
-    client = openai.OpenAI(base_url=OPENAI_API_IMAGE_ENDPOINT, api_key="placeholder")
+    """Generates an image using the given prompt and returns the base64 encoded image data
+
+    Returns:
+        str: 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",
     )
     if response.data:
-        return response.data[0].url
+        return response.data[0].b64_json or ""
     else:
         return ""
 
 
-def image_edit(image, mask, prompt, n=1, size="1024x1024"):
+def image_edit(
-    client = openai.OpenAI(base_url=OPENAI_API_EDIT_ENDPOINT, api_key="placeholder")
+    image: BufferedReader | BytesIO,
+    prompt: str,
+    openai_url: str,
+    openai_api_key: str,
+    n=1,
+) -> str:
+    client = openai.OpenAI(base_url=openai_url, api_key=openai_api_key)
     response = client.images.edit(
         image=image,
-        mask=mask,
         prompt=prompt,
         n=n,
-        size=size,
+        size="1024x1024",
     )
-    return response.data[0].url
+    if response.data:
+        return response.data[0].b64_json or ""
+    else:
+        return ""
 
 
-def embeddings(text, model="text-embedding-3-small"):
+def embedding(
-    client = openai.OpenAI(base_url=OPENAI_API_EMBED_ENDPOINT, api_key="placeholder")
+    text: str, openai_url: str, openai_api_key: str, model: str
+) -> list[float]:
+    client = openai.OpenAI(base_url=openai_url, api_key=openai_api_key)
     response = client.embeddings.create(
-        input=text,
+        input=[text], model=model, encoding_format="float"
-        model=model,
     )
-    return response.data[0].embedding
+    if response:
+        raw_data = response[0].embedding  # type: ignore
+        # The result could be an array of floats or an array of an array of floats.
+        try:
+            return raw_data[0]
+        except Exception:
+            return raw_data
+    return []

vibe_bot/main.py

@@ -5,7 +5,19 @@ import base64
 from io import BytesIO
 from openai import OpenAI
 import logging
-from database import get_database, CustomBotManager
+from database import get_database, CustomBotManager  # type: ignore
+from config import (  # type: ignore
+    CHAT_ENDPOINT_KEY,
+    DISCORD_TOKEN,
+    CHAT_ENDPOINT,
+    CHAT_MODEL,
+    IMAGE_EDIT_ENDPOINT_KEY,
+    IMAGE_GEN_ENDPOINT,
+    IMAGE_EDIT_ENDPOINT,
+    MAX_COMPLETION_TOKENS,
+)
+import llama_wrapper  # type: ignore
+import requests
 
 # Configure logging
 logging.basicConfig(
@@ -13,31 +25,11 @@ logging.basicConfig(
 )
 logger = logging.getLogger(__name__)
 
-DISCORD_TOKEN = os.getenv("DISCORD_TOKEN", "placeholder")
-
-OPENAI_API_ENDPOINT = os.getenv("OPENAI_API_ENDPOINT")
-IMAGE_GEN_ENDPOINT = os.getenv("IMAGE_GEN_ENDPOINT")
-IMAGE_EDIT_ENDPOINT = os.getenv("IMAGE_EDIT_ENDPOINT")
-MAX_COMPLETION_TOKENS = int(os.getenv("MAX_COMPLETION_TOKENS", "1000"))
-
-if not OPENAI_API_ENDPOINT:
-    raise Exception("OPENAI_API_ENDPOINT required.")
-
-if not IMAGE_GEN_ENDPOINT:
-    raise Exception("IMAGE_GEN_ENDPOINT required.")
-
-# Set your OpenAI API key as an environment variable
-# You can also pass it directly but environment variables are safer
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "placeholder")
-
 # Initialize the bot
 intents = discord.Intents.default()
 intents.message_content = True
 bot = commands.Bot(command_prefix="!", intents=intents)
 
-# OpenAI Completions API endpoint
-OPENAI_COMPLETIONS_URL = f"{OPENAI_API_ENDPOINT}/chat/completions"
-
 
 @bot.event
 async def on_ready():
@@ -46,7 +38,7 @@ async def on_ready():
     logger.info(f"Bot logged in as {bot.user}")
 
 
-@bot.command(name="custom-bot")
+@bot.command(name="custom-bot")  # type: ignore
 async def custom_bot(ctx, bot_name: str, *, personality: str):
     """Create a custom bot with a name and personality
 
@@ -129,14 +121,14 @@ async def list_custom_bots(ctx):
         f"Found {len(bots)} custom bots, displaying top 10 for {ctx.author.name}"
     )
     bot_list = "🤖 **Available Custom Bots**:\n\n"
-    for name, prompt, creator in bots[:10]:  # Limit to 10 bots
+    for name, prompt, creator in bots:
-        bot_list += f"• **{name}** (created by {creator})\n"
+        bot_list += f"• **{name}**\n"
 
     logger.info(f"Sending bot list response to {ctx.author.name}")
     await ctx.send(bot_list)
 
 
-@bot.command(name="delete-custom-bot")
+@bot.command(name="delete-custom-bot")  # type: ignore
 async def delete_custom_bot(ctx, bot_name: str):
     """Delete a custom bot (only the creator can delete)
 
@@ -194,16 +186,16 @@ async def on_message(message):
     if message.author == bot.user:
         return
 
+    message_author = message.author.name
+    message_content = message.content.lower()
+
     logger.debug(
-        f"Processing message from {message.author.name}: '{message.content[:50]}...'"
+        f"Processing message from {message_author}: '{message_content[:50]}...'"
     )
 
     ctx = await bot.get_context(message)
 
-    # Check if the message starts with a custom bot command
+    logger.info("Initializing CustomBotManager to check for custom bot commands")
-    content = message.content.lower()
-
-    logger.info(f"Initializing CustomBotManager to check for custom bot commands")
     custom_bot_manager = CustomBotManager()
 
     logger.info("Fetching list of custom bots to check for matching commands")
@@ -212,7 +204,7 @@ async def on_message(message):
     logger.info(f"Checking {len(custom_bots)} custom bots for command match")
     for bot_name, system_prompt, _ in custom_bots:
         # Check if message starts with the custom bot name followed by a space
-        if content.startswith(f"!{bot_name} "):
+        if message_content.startswith(f"!{bot_name} "):
             logger.info(
                 f"Custom bot command detected: '{bot_name}' triggered by {message.author.name}"
             )
@@ -224,25 +216,14 @@ async def on_message(message):
             )
 
             # Prepare the payload with custom personality
-            payload = {
-                "model": "qwen3-vl-30b-a3b-instruct",
-                "messages": [
-                    {
-                        "role": "system",
-                        "content": system_prompt,
-                    },
-                    {"role": "user", "content": user_message},
-                ],
-                "max_completion_tokens": MAX_COMPLETION_TOKENS,
-            }
-
             response_prefix = f"**{bot_name} response**"
 
             logger.info(f"Sending request to OpenAI API for bot '{bot_name}'")
             await handle_chat(
                 ctx=ctx,
+                bot_name=bot_name,
                 message=user_message,
-                payload=payload,
+                system_prompt=system_prompt,
                 response_prefix=response_prefix,
             )
             return
@@ -258,24 +239,22 @@ async def doodlebob(ctx, *, message: str):
     logger.info(f"Doodlebob command triggered by {ctx.author.name}: {message[:100]}")
     await ctx.send(f"**Doodlebob erasing {message[:100]}...**")
 
-    image_prompt_payload = {
+    system_prompt = (
-        "model": "qwen3-vl-30b-a3b-instruct",
-        "messages": [
-            {
-                "role": "system",
-                "content": (
         "Given the following message, convert it to a detailed image generation prompt that will be passed directly into an image generation model."
         "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"
         " 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."
-                ),
+    )
-            },
-            {"role": "user", "content": message},
-        ],
-    }
 
     # Wait for the generated image prompt
-    image_prompt = await call_llm(ctx, image_prompt_payload)
+    image_prompt = llama_wrapper.chat_completion_instruct(
+        system_prompt=system_prompt,
+        user_prompt=message,
+        openai_url=CHAT_ENDPOINT,
+        openai_api_key=CHAT_ENDPOINT_KEY,
+        model=CHAT_MODEL,
+        max_tokens=MAX_COMPLETION_TOKENS,
+    )
 
     # If the string is empty we had an error
     if image_prompt == "":
@@ -285,33 +264,17 @@ async def doodlebob(ctx, *, message: str):
     # Alert the user we're generating the image
     await ctx.send(f"**Doodlebob calling drone strike on {image_prompt[:100]}...**")
 
-    # Create the image prompt payload
+    image_b64 = llama_wrapper.image_generation(
-    image_payload = {
+        prompt=message,
-        "model": "default",
+        openai_url=IMAGE_EDIT_ENDPOINT,
-        "prompt": image_prompt,
+        openai_api_key=IMAGE_EDIT_ENDPOINT_KEY,
-        "n": 1,
-        "size": "1024x1024",
-    }
-
-    # Call the image generation endpoint
-    response = requests.post(
-        f"{IMAGE_GEN_ENDPOINT}/images/generations",
-        json=image_payload,
-        timeout=120,
     )
 
-    if response.status_code == 200:
+    # Save the image to a file
-        result = response.json()
+    edited_image_data = BytesIO(base64.b64decode(image_b64))
-        # Send image
+    send_img = discord.File(edited_image_data, filename="image.png")
-        image_data = BytesIO(base64.b64decode(result["data"][0]["b64_json"]))
-        send_img = discord.File(image_data, filename="image.png")
     await ctx.send(file=send_img)
 
-    else:
-        print(f"❌ Error: {response.status_code}")
-        print(response.text)
-        return None
-
 
 @bot.command(name="retcon")
 async def retcon(ctx, *, message: str):
@@ -321,31 +284,23 @@ async def retcon(ctx, *, message: str):
 
     await ctx.send(f"**Rewriting history to match {message[:100]}...**")
 
-    client = OpenAI(base_url=IMAGE_EDIT_ENDPOINT, api_key=OPENAI_API_KEY)
+    image_b64 = llama_wrapper.image_edit(
-
+        image=image_bytestream,
-    result = client.images.edit(
-        model="placeholder",
-        image=[image_bytestream],
         prompt=message,
-        size="1024x1024",
+        openai_url=IMAGE_EDIT_ENDPOINT,
+        openai_api_key=IMAGE_EDIT_ENDPOINT_KEY,
     )
 
-    image_base64 = result.data[0].b64_json
-    image_bytes = base64.b64decode(image_base64)
-
     # Save the image to a file
-    edited_image_data = BytesIO(image_bytes)
+    edited_image_data = BytesIO(base64.b64decode(image_b64))
     send_img = discord.File(edited_image_data, filename="image.png")
     await ctx.send(file=send_img)
 
 
-async def handle_chat(ctx, *, message: str, payload: dict, response_prefix: str):
+async def handle_chat(
-    # Check if API key is set
+    ctx, *, bot_name: str, message: str, system_prompt: str, response_prefix: str
-    if not OPENAI_API_KEY:
+):
-        await ctx.send(
+    await ctx.send(f"{bot_name} is searching its databanks for {message[:50]}...")
-            "Error: OpenAI API key is not configured. Please set the OPENAI_API_KEY environment variable."
-        )
-        return
 
     # Get database instance
     db = get_database()
@@ -356,31 +311,26 @@ async def handle_chat(ctx, *, message: str, payload: dict, response_prefix: str)
     )
 
     if context:
-        payload["messages"][0][
+        user_message = f"\n\nRelevant conversation history:\n{context}\n\n{message}"
-            "content"
+    else:
-        ] += f"\n\nRelevant conversation history:\n{context}"
+        user_message = message
 
-    payload["messages"][1]["content"] = message
+    logger.info(user_message)
 
-    print(payload)
+    system_prompt_edit = (
-
+        "Keep your responses somewhat short, limited to 500 words or less. "
-    try:
+        f"{system_prompt}"
-        # Initialize OpenAI client
-        client = OpenAI(api_key=OPENAI_API_KEY, base_url=OPENAI_API_ENDPOINT)
-
-        # Call OpenAI API
-        response = client.chat.completions.create(
-            model=payload["model"],
-            messages=payload["messages"],
-            max_completion_tokens=MAX_COMPLETION_TOKENS,
-            frequency_penalty=1.5,
-            presence_penalty=1.5,
-            temperature=1,
-            seed=-1,
     )
 
-        # Extract the generated text
+    try:
-        generated_text = response.choices[0].message.content.strip()
+        bot_response = llama_wrapper.chat_completion_instruct(
+            system_prompt=system_prompt_edit,
+            user_prompt=user_message,
+            openai_url=CHAT_ENDPOINT,
+            openai_api_key=CHAT_ENDPOINT_KEY,
+            model=CHAT_MODEL,
+            max_tokens=MAX_COMPLETION_TOKENS,
+        )
 
         # Store both user message and bot response in the database
         db.add_message(
@@ -394,68 +344,24 @@ async def handle_chat(ctx, *, message: str, payload: dict, response_prefix: str)
 
         db.add_message(
             message_id=f"{ctx.message.id}_response",
-            user_id=str(bot.user.id),
+            user_id=str(bot.user.id),  # type: ignore
-            username=bot.user.name,
+            username=bot.user.name,  # type: ignore
-            content=f"Bot: {generated_text}",
+            content=f"Bot: {bot_response}",
             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)
-        while generated_text:
+        while bot_response:
-            send_chunk = generated_text[:1000]
+            send_chunk = bot_response[:1000]
-            generated_text = generated_text[1000:]
+            bot_response = bot_response[1000:]
             await ctx.send(send_chunk)
 
-    except requests.exceptions.HTTPError as e:
-        await ctx.send(f"Error: OpenAI API error - {e}")
-    except requests.exceptions.Timeout:
-        await ctx.send("Error: Request timed out. Please try again.")
     except Exception as e:
         await ctx.send(f"Error: {str(e)}")
 
 
-async def call_llm(ctx, payload: dict) -> str:
-    # Check if API key is set
-    if not OPENAI_API_KEY:
-        await ctx.send(
-            "Error: OpenAI API key is not configured. Please set the OPENAI_API_KEY environment variable."
-        )
-        return ""
-
-    # Set headers
-    headers = {
-        "Authorization": f"Bearer {OPENAI_API_KEY}",
-        "Content-Type": "application/json",
-    }
-
-    try:
-        # Initialize OpenAI client
-        client = OpenAI(api_key=OPENAI_API_KEY, base_url=OPENAI_API_ENDPOINT)
-
-        # Call OpenAI API
-        response = client.chat.completions.create(
-            model=payload["model"],
-            messages=payload["messages"],
-            max_tokens=MAX_COMPLETION_TOKENS,
-        )
-
-        # Extract the generated text
-        generated_text = response.choices[0].message.content.strip()
-        print(generated_text)
-
-        return generated_text
-
-    except requests.exceptions.HTTPError as e:
-        await ctx.send(f"Error: OpenAI API error - {e}")
-    except requests.exceptions.Timeout:
-        await ctx.send("Error: Request timed out. Please try again.")
-    except Exception as e:
-        await ctx.send(f"Error: {str(e)}")
-    return ""
-
-
 # Run the bot
 if __name__ == "__main__":
     bot.run(DISCORD_TOKEN)

vibe_bot/tests/conftest.py

@@ -1,30 +0,0 @@
-import os
-import pytest
-from dotenv import load_dotenv
-
-# Try to load .env.test first, fallback to .env
-env_test_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), '.env.test')
-env_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), '.env')
-
-if os.path.exists(env_test_path):
-    load_dotenv(env_test_path)
-    print("✓ Loaded environment variables from .env.test")
-elif os.path.exists(env_path):
-    load_dotenv(env_path)
-    print("✓ Loaded environment variables from .env")
-
-@pytest.fixture(autouse=True, scope="session")
-def verify_env_loaded():
-    """Verify critical environment variables are loaded before tests run"""
-    required_vars = [
-        "DISCORD_TOKEN",
-        "OPENAI_API_ENDPOINT",
-        "IMAGE_GEN_ENDPOINT",
-        "IMAGE_EDIT_ENDPOINT"
-    ]
-    
-    missing_vars = [var for var in required_vars if var not in os.environ]
-    if missing_vars:
-        pytest.fail(f"Missing required environment variables: {', '.join(missing_vars)}")
-    
-    yield

vibe_bot/tests/test_llama_wrapper.py

@@ -1,71 +1,112 @@
 # Tests all functions in the llama-wrapper.py file
 # Run with: python -m pytest test_llama_wrapper.py -v
 
-from discord import message
-import pytest
 from ..llama_wrapper import (
-    chat_completion_think,
+    chat_completion,
     chat_completion_instruct,
     image_generation,
     image_edit,
-    embeddings,
+    embedding,
 )
-from dotenv import load_dotenv
+from ..config import (
-import os
+    CHAT_ENDPOINT,
-
+    CHAT_MODEL,
-OPENAI_API_CHAT_ENDPOINT = os.getenv(
+    CHAT_ENDPOINT_KEY,
-    "OPENAI_API_CHAT_ENDPOINT", "https://llama-cpp.reeselink.com"
+    IMAGE_EDIT_ENDPOINT,
+    IMAGE_EDIT_ENDPOINT_KEY,
+    IMAGE_GEN_ENDPOINT,
+    IMAGE_GEN_ENDPOINT_KEY,
+    EMBEDDING_ENDPOINT,
+    EMBEDDING_ENDPOINT_KEY,
 )
-OPENAI_API_IMAGE_ENDPOINT = os.getenv("OPENAI_API_IMAGE_ENDPOINT")
+from io import BytesIO
-OPENAI_API_EDIT_ENDPOINT = os.getenv("OPENAI_API_EDIT_ENDPOINT")
+import base64
-OPENAI_API_EMBED_ENDPOINT = os.getenv("OPENAI_API_EMBED_ENDPOINT")
+import tempfile
+from pathlib import Path
+import numpy as np
 
-# Default models
+
-DEFAULT_CHAT_MODEL = os.getenv("DEFAULT_CHAT_MODEL", "qwen3.5-35b-a3b")
+TEMPDIR = Path(tempfile.mkdtemp())
-DEFAULT_EMBED_MODEL = os.getenv("DEFAULT_EMBED_MODEL", "text-embedding-3-small")
-DEFAULT_IMAGE_MODEL = os.getenv("DEFAULT_IMAGE_MODEL", "dall-e-3")
-DEFAULT_EDIT_MODEL = os.getenv("DEFAULT_EDIT_MODEL", "dall-e-2")
 
 
 def test_chat_completion_think():
-    # This test will fail without an actual API endpoint
+    result = chat_completion(
-    # But it's here to show the structure
-    chat_completion_think(
         system_prompt="You are a helpful assistant.",
         user_prompt="Tell me about Everquest",
-        openai_url=OPENAI_API_CHAT_ENDPOINT,
+        openai_url=CHAT_ENDPOINT,
-        openai_api_key="placeholder",
+        openai_api_key=CHAT_ENDPOINT_KEY,
-        model=DEFAULT_CHAT_MODEL,
+        model=CHAT_MODEL,
         max_tokens=100,
     )
+    print(result)
 
 
 def test_chat_completion_instruct():
-    # This test will fail without an actual API endpoint
+    result = chat_completion_instruct(
-    # But it's here to show the structure
-    chat_completion_instruct(
         system_prompt="You are a helpful assistant.",
         user_prompt="Tell me about Everquest",
-        openai_url=OPENAI_API_CHAT_ENDPOINT,
+        openai_url=CHAT_ENDPOINT,
-        openai_api_key="placeholder",
+        openai_api_key=CHAT_ENDPOINT_KEY,
-        model=DEFAULT_CHAT_MODEL,
+        model=CHAT_MODEL,
         max_tokens=100,
     )
+    print(result)
 
 
 def test_image_generation():
-    # This test will fail without an actual API endpoint
+    result = image_generation(
-    # But it's here to show the structure
+        prompt="Generate an image of a horse",
-    pass
+        openai_url=IMAGE_GEN_ENDPOINT,
+        openai_api_key=IMAGE_GEN_ENDPOINT_KEY,
+    )
+    with open("image-gen.png", "wb") as f:
+        f.write(base64.b64decode(result))
 
 
 def test_image_edit():
-    # This test will fail without an actual API endpoint
+    with open("image-gen.png", "rb") as f:
-    # But it's here to show the structure
+        image_data = BytesIO(f.read())
-    pass
+    result = image_edit(
+        image=image_data,
+        prompt="Paint the words 'horse' on the horse.",
+        openai_url=IMAGE_EDIT_ENDPOINT,
+        openai_api_key=IMAGE_EDIT_ENDPOINT_KEY,
+    )
+    with open("image-edit.png", "wb") as f:
+        f.write(base64.b64decode(result))
+
+
+def _cosine_similarity(a, b):
+    """
+    Close to 1: very similar
+    Close to 0: orthogonal
+    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():
-    # This test will fail without an actual API endpoint
+    result1 = embedding(
-    # But it's here to show the structure
+        "this is a horse",
-    pass
+        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