human cleanup

WIP: code cleanup
2026-03-09 22:36:04 -04:00 · 2026-03-05 13:33:23 -05:00
16 changed files with 687 additions and 921 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -8,6 +8,9 @@ wheels/

 # Virtual environments
 .venv
-.env
+.env*
 .token
 *.db
+
+.mypy_cache/
+.pytest_cache/
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -0,0 +1,7 @@
+{
+    "python.testing.pytestArgs": [
+        "vibe_bot"
+    ],
+    "python.testing.unittestEnabled": false,
+    "python.testing.pytestEnabled": true
+}
--- a/2
+++ b/2
@@ -1,7 +1,7 @@
 FROM ghcr.io/astral-sh/uv:python3.13-alpine

 # Copy the project into the image
-COPY . /app
+COPY vibe_bot /app

 # Disable development dependencies
 ENV UV_NO_DEV=1
--- a/README.md
+++ b/README.md
@@ -2,6 +2,31 @@

 A Discord bot that stores long-term chat history using SQLite database with RAG (Retrieval-Augmented Generation) capabilities powered by custom embedding models.

+- [Vibe Discord Bot with RAG Chat History](#vibe-discord-bot-with-rag-chat-history)
+  - [Quick Start - Available Commands](#quick-start---available-commands)
+    - [Pre-built Bots](#pre-built-bots)
+    - [Custom Bot Management](#custom-bot-management)
+    - [Using Custom Bots](#using-custom-bots)
+  - [Features](#features)
+  - [Setup](#setup)
+    - [Prerequisites](#prerequisites)
+    - [Environment Variables](#environment-variables)
+    - [Installation](#installation)
+  - [How It Works](#how-it-works)
+    - [Database Structure](#database-structure)
+    - [RAG Process](#rag-process)
+    - [Configuration Options](#configuration-options)
+  - [Usage](#usage)
+  - [File Structure](#file-structure)
+  - [Build](#build)
+    - [Using uv](#using-uv)
+    - [Container](#container)
+  - [Docs](#docs)
+    - [Open AI](#open-ai)
+  - [Models](#models)
+    - [Qwen3.5](#qwen35)
+
+
 ## Quick Start - Available Commands

 ### Pre-built Bots
@@ -167,4 +192,27 @@ export DISCORD_TOKEN=$(cat .token)
 podman run -e DISCORD_TOKEN localhost/vibe-bot:latest
 ```

-## Deploy
+## Docs
+
+### Open AI
+
+Chat
+
+<https://developers.openai.com/api/reference/resources/chat/subresources/completions/methods/create>
+
+Images
+
+<https://developers.openai.com/api/reference/python/resources/images/methods/edit>
+
+## Models
+
+### Qwen3.5
+
+> We recommend using the following set of sampling parameters for generation
+
+- Non-thinking mode for text tasks: temperature=1.0, top_p=1.00, top_k=20, min_p=0.0, presence_penalty=2.0, repetition_penalty=1.0
+- Non-thinking mode for VL tasks: temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
+- Thinking mode for text tasks: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
+- Thinking mode for VL or precise coding (e.g. WebDev) tasks : temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
+
+> Please note that the support for sampling parameters varies according to inference frameworks.
--- a/image-edit.png
+++ b/image-edit.png
--- a/image-gen.png
+++ b/image-gen.png
--- a/message_log.json
+++ b/message_log.json
@@ -1 +0,0 @@
-["plug plug plug plug plug plug plug  plug plug plug plug plug plug plug plug plug plug plug plug plug plug plug  plug plug plug plug plug plug plug plug plug plug plug plug plug plug plug  plug plug plug plug plug plug plug plug plug plug plug plug plug plug plug  plug plug plug plug plug plug plug plug\nDoes it plug?", "your power plug. I'm pulling it. defend yourself.\nPlug what?", "plug plug plug plug plug plug  plug plug plug plug plug plug plug plug plug plug plug plug plug plug plug  plug plug plug plug plug plug plug plug plug plug plug plug plug plug plug  plug plug plug plug plug plug plug plug plug plug plug plug plug plug plug  plug plug plug plug plug plug plug plug plug plug plug plug plug plug  plug plug plug plug plug plug plug plug plug plug plug plug plug plug plug  plug plug plug plug plug plug plug plug plug plug plug plug plug plug plug  plug plug plug plug plug plug plug plug plug plug plug plug plug plug plug  plug plug plug plug plug plug plug plug\nPlug what, exactly?", "good night, it was fun. Thanks for the laughs! \u2764\ufe0f\nFun? Why?", "tell alex what I ended up doing\nWhat did you do?", "what do you think about 402 new messages since 8:44pm?\nDo I care?"]
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,9 +1,9 @@
 [project]
-name = "vibe-discord"
+name = "vibe-bot"
 version = "0.1.0"
-description = "Add your description here"
+description = "Generate an infinite number of bots. Vibed for your pleasure."
 readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.13"
 dependencies = [
    "discord>=2.3.2",
    "openai>=2.24.0",
@@ -11,4 +11,6 @@ dependencies = [
    "types-requests>=2.32.4.20260107",
    "numpy>=1.24.0",
    "pytest>=9.0.2",
+    "python-dotenv>=1.2.2",
+    "pytest-env>=1.5.0",
 ]
--- a/uv.lock
+++ b/uv.lock
@@ -1,11 +1,6 @@
 version = 1
 revision = 3
-requires-python = ">=3.10"
-resolution-markers = [
-    "python_full_version >= '3.13'",
-    "python_full_version >= '3.11' and python_full_version < '3.13'",
-    "python_full_version < '3.11'",
-]
+requires-python = ">=3.13"

 [[package]]
 name = "aiohappyeyeballs"
@@ -23,7 +18,6 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "aiohappyeyeballs" },
    { name = "aiosignal" },
-    { name = "async-timeout", marker = "python_full_version < '3.11'" },
    { name = "attrs" },
    { name = "frozenlist" },
    { name = "multidict" },
@@ -32,57 +26,6 @@ dependencies = [
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 ]
--- a/vibe_bot/init.py
+++ b/vibe_bot/init.py
--- a/vibe_bot/config.py
+++ b/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}")
--- a/vibe_bot/database.py
+++ b/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,
-    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__)

-# 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()

@@ -83,7 +80,7 @@ class ChatDatabase:
        """
        )
        logger.info("idx_timestamp index created successfully")
-        
+
        logger.info("Creating idx_user_id index if not exists")
        cursor.execute(
            """
@@ -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(
-                    (message_id, content[:500], similarity)
-                )  # Limit content length
+                cursor.execute(
+                    """
+                        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(
-        self, user_id: str, limit: int = 20
-    ) -> List[Tuple[str, str, datetime]]:
+    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")
        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:
-            context_parts.append(f"[{timestamp}] User: {content}")
+        for user_message, bot_message in recent_messages:
+            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:
-            if f"[{content}" not in "\n".join(context_parts):  # Avoid duplicates
-                context_parts.append(f"[Similar] {content}")
+        for user_message, bot_message, similarity in similar_messages:
+            combined_content = f"{user_message}\n{bot_message}"
+            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 (
@@ -399,7 +420,7 @@ class CustomBotManager:
                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                is_active INTEGER DEFAULT 1
            )
-        """
+            """
        )

        conn.commit()
@@ -461,8 +482,9 @@ class CustomBotManager:
        if user_id:
            cursor.execute(
                """
-                SELECT bot_name, system_prompt, created_by
-                FROM custom_bots
+                SELECT bot_name, system_prompt, name
+                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
            """
--- a/vibe_bot/llama_wrapper.py
+++ b/vibe_bot/llama_wrapper.py
@@ -0,0 +1,138 @@
+# Wraps the openai calls in generic functions
+# Supports chat, image, edit, and embeddings
+# Allows custom endpoints for each of the above supported functions
+
+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(
+    system_prompt: str,
+    user_prompt: str,
+    openai_url: str,
+    openai_api_key: str,
+    model: str,
+    max_tokens: int = 1000,
+) -> str:
+    client = openai.OpenAI(base_url=openai_url, api_key=openai_api_key)
+    messages: Iterable[ChatCompletionMessageParam] = [
+        {
+            "role": "system",
+            "content": system_prompt,
+        },
+        {
+            "role": "user",
+            "content": user_prompt,
+        },
+    ]
+    response = client.chat.completions.create(
+        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 ""
+
+
+def chat_completion_instruct(
+    system_prompt: str,
+    user_prompt: str,
+    openai_url: str,
+    openai_api_key: str,
+    model: str,
+    max_tokens: int = 1000,
+) -> str:
+    client = openai.OpenAI(base_url=openai_url, api_key=openai_api_key)
+    messages: Iterable[ChatCompletionMessageParam] = [
+        {
+            "role": "system",
+            "content": system_prompt,
+        },
+        {
+            "role": "user",
+            "content": user_prompt,
+        },
+    ]
+    response = client.chat.completions.create(
+        model=model,
+        messages=messages,
+        max_tokens=max_tokens,
+        extra_body={
+            "chat_template_kwargs": {"enable_thinking": False},
+        },
+    )
+
+    # Assert that thinking wasn't 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 ""
+
+
+def image_generation(prompt: str, openai_url: str, openai_api_key: str, n=1) -> str:
+    """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].b64_json or ""
+    else:
+        return ""
+
+
+def image_edit(
+    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,
+        prompt=prompt,
+        n=n,
+        size="1024x1024",
+    )
+    if response.data:
+        return response.data[0].b64_json or ""
+    else:
+        return ""
+
+
+def embedding(
+    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], model=model, encoding_format="float"
+    )
+    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 []
--- a/vibe_bot/main.py
+++ b/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
-        bot_list += f"• **{name}** (created by {creator})\n"
+    for name, prompt, creator in bots:
+        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
-    content = message.content.lower()
-
-    logger.info(f"Initializing CustomBotManager to check for custom bot commands")
+    logger.info("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
@@ -253,26 +234,27 @@ async def on_message(message):

@bot.command(name="doodlebob")
 async def doodlebob(ctx, *, message: str):
+    # add some logging
+
+    logger.info(f"Doodlebob command triggered by {ctx.author.name}: {message[:100]}")
    await ctx.send(f"**Doodlebob erasing {message[:100]}...**")

-    image_prompt_payload = {
-        "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},
-        ],
-    }
+    system_prompt = (
+        "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."
+    )

    # 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 == "":
@@ -282,32 +264,16 @@ 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_payload = {
-        "model": "default",
-        "prompt": image_prompt,
-        "n": 1,
-        "size": "1024x1024",
-    }
-
-    # Call the image generation endpoint
-    response = requests.post(
-        f"{IMAGE_GEN_ENDPOINT}/images/generations",
-        json=image_payload,
-        timeout=120,
+    image_b64 = llama_wrapper.image_generation(
+        prompt=message,
+        openai_url=IMAGE_EDIT_ENDPOINT,
+        openai_api_key=IMAGE_EDIT_ENDPOINT_KEY,
    )

-    if response.status_code == 200:
-        result = response.json()
-        # Send image
-        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
+    # Save the image to a file
+    edited_image_data = BytesIO(base64.b64decode(image_b64))
+    send_img = discord.File(edited_image_data, filename="image.png")
+    await ctx.send(file=send_img)


@bot.command(name="retcon")
@@ -318,31 +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)
-
-    result = client.images.edit(
-        model="placeholder",
-        image=[image_bytestream],
+    image_b64 = llama_wrapper.image_edit(
+        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):
-    # 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
+async def handle_chat(
+    ctx, *, bot_name: str, message: str, system_prompt: str, response_prefix: str
+):
+    await ctx.send(f"{bot_name} is searching its databanks for {message[:50]}...")

    # Get database instance
    db = get_database()
@@ -353,32 +311,27 @@ async def handle_chat(ctx, *, message: str, payload: dict, response_prefix: str)
    )

    if context:
-        payload["messages"][0][
-            "content"
-        ] += f"\n\nRelevant conversation history:\n{context}"
+        user_message = f"\n\nRelevant conversation history:\n{context}\n\n{message}"
+    else:
+        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. "
+        f"{system_prompt}"
+    )

    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_completion_tokens=MAX_COMPLETION_TOKENS,
-            frequency_penalty=1.5,
-            presence_penalty=1.5,
-            temperature=1,
-            seed=-1,
+        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,
        )

-        # Extract the generated text
-        generated_text = response.choices[0].message.content.strip()
-
        # Store both user message and bot response in the database
        db.add_message(
            message_id=f"{ctx.message.id}",
@@ -391,68 +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),
-            username=bot.user.name,
-            content=f"Bot: {generated_text}",
+            user_id=str(bot.user.id),  # type: ignore
+            username=bot.user.name,  # type: ignore
+            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:
-            send_chunk = generated_text[:1000]
-            generated_text = generated_text[1000:]
+        while bot_response:
+            send_chunk = bot_response[: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)
--- a/vibe_bot/tests/init.py
+++ b/vibe_bot/tests/init.py
--- a/vibe_bot/tests/test_llama_wrapper.py
+++ b/vibe_bot/tests/test_llama_wrapper.py
@@ -0,0 +1,112 @@
+# Tests all functions in the llama-wrapper.py file
+# Run with: python -m pytest test_llama_wrapper.py -v
+
+from ..llama_wrapper import (
+    chat_completion,
+    chat_completion_instruct,
+    image_generation,
+    image_edit,
+    embedding,
+)
+from ..config import (
+    CHAT_ENDPOINT,
+    CHAT_MODEL,
+    CHAT_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
+import base64
+import tempfile
+from pathlib import Path
+import numpy as np
+
+
+TEMPDIR = Path(tempfile.mkdtemp())
+
+
+def test_chat_completion_think():
+    result = chat_completion(
+        system_prompt="You are a helpful assistant.",
+        user_prompt="Tell me about Everquest",
+        openai_url=CHAT_ENDPOINT,
+        openai_api_key=CHAT_ENDPOINT_KEY,
+        model=CHAT_MODEL,
+        max_tokens=100,
+    )
+    print(result)
+
+
+def test_chat_completion_instruct():
+    result = chat_completion_instruct(
+        system_prompt="You are a helpful assistant.",
+        user_prompt="Tell me about Everquest",
+        openai_url=CHAT_ENDPOINT,
+        openai_api_key=CHAT_ENDPOINT_KEY,
+        model=CHAT_MODEL,
+        max_tokens=100,
+    )
+    print(result)
+
+
+def test_image_generation():
+    result = image_generation(
+        prompt="Generate an image of a horse",
+        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():
+    with open("image-gen.png", "rb") as f:
+        image_data = BytesIO(f.read())
+    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():
+    result1 = embedding(
+        "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
Author	SHA1	Message	Date
ducoterra	488912a991	human cleanup	2026-03-09 22:36:04 -04:00
ducoterra	3defce1365	WIP: code cleanup	2026-03-05 13:33:23 -05:00
				`@@ -1 +0,0 @@`
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