{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "12ca6f8a", "metadata": {}, "outputs": [], "source": [ "# imports\n", "\n", "import os\n", "from dotenv import load_dotenv\n", "from openai import OpenAI\n", "import anthropic\n", "from IPython.display import Markdown, display, update_display" ] }, { "cell_type": "code", "execution_count": 2, "id": "4b53a815", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OpenAI API Key exists and begins sk-proj-\n", "Anthropic API Key exists and begins sk-ant-\n", "Google API Key not set\n" ] } ], "source": [ "# Load environment variables in a file called .env\n", "# Print the key prefixes to help with any debugging\n", "\n", "load_dotenv(override=True)\n", "openai_api_key = os.getenv('OPENAI_API_KEY')\n", "anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n", "google_api_key = os.getenv('GOOGLE_API_KEY')\n", "\n", "if openai_api_key:\n", " print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", "else:\n", " print(\"OpenAI API Key not set\")\n", " \n", "if anthropic_api_key:\n", " print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n", "else:\n", " print(\"Anthropic API Key not set\")\n", "\n", "if google_api_key:\n", " print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n", "else:\n", " print(\"Google API Key not set\")" ] }, { "cell_type": "code", "execution_count": null, "id": "3d2b7cfe", "metadata": {}, "outputs": [], "source": [ "# Connect to OpenAI, Anthropic\n", "\n", "openai = OpenAI()\n", "\n", "claude = anthropic.Anthropic()" ] }, { "cell_type": "code", "execution_count": null, "id": "b7d88d4b", "metadata": {}, "outputs": [], "source": [ "class ConversationManager:\n", " def __init__(self):\n", " self.conversation_history = []\n", " self.participants = {}\n", " \n", " def add_participant(self, name, chatbot):\n", " \"\"\"Add a model to the conversation\"\"\"\n", " self.participants[name] = chatbot\n", " \n", " def add_message(self, speaker, message):\n", " \"\"\"Add a message to the shared conversation history\"\"\"\n", " self.conversation_history.append({\n", " \"speaker\": speaker,\n", " \"role\": \"assistant\" if speaker in self.participants else \"user\",\n", " \"content\": message\n", " })\n", " \n", " def get_context_for_model(self, model_name):\n", " \"\"\"Create context appropriate for the given model\"\"\"\n", " # Convert the shared history to model-specific format\n", " messages = []\n", " for msg in self.conversation_history:\n", " if msg[\"speaker\"] == model_name:\n", " messages.append({\"role\": \"assistant\", \"content\": msg[\"content\"]})\n", " else:\n", " messages.append({\"role\": \"user\", \"content\": msg[\"content\"]})\n", " return messages\n", " \n", " def run_conversation(self, starting_message, turns=3, round_robin=True):\n", " \"\"\"Run a multi-model conversation for specified number of turns\"\"\"\n", " current_message = starting_message\n", " models = list(self.participants.keys())\n", " \n", " # Add initial message\n", " self.add_message(\"user\", current_message)\n", " \n", " for _ in range(turns):\n", " for model_name in models:\n", " # Get context appropriate for this model\n", " model_context = self.get_context_for_model(model_name)\n", " \n", " # Get response from this model\n", " chatbot = self.participants[model_name]\n", " response = chatbot.generate_response(model_context)\n", " \n", " # Add to conversation history\n", " self.add_message(model_name, response)\n", " \n", " print(f\"{model_name}:\\n{response}\\n\")\n", " \n", " if not round_robin:\n", " # If not round-robin, use this response as input to next model\n", " current_message = response" ] }, { "cell_type": "code", "execution_count": null, "id": "80c537c3", "metadata": {}, "outputs": [], "source": [ "class ChatBot:\n", " def __init__(self, model_name, system_prompt, **kwargs):\n", " self.model_name = model_name\n", " self.system_prompt = system_prompt\n", " self.api_key = kwargs.get('api_key', None)\n", " self.base_url = kwargs.get('base_url', None)\n", " \n", " def generate_response(self, messages):\n", " \"\"\"Generate a response based on provided messages without storing history\"\"\"\n", " # Prepare messages including system prompt\n", " full_messages = [{\"role\": \"system\", \"content\": self.system_prompt}] + messages\n", " \n", " try:\n", " if \"claude\" in self.model_name.lower():\n", " # Format messages for Claude API\n", " claude_messages = [m for m in messages if m[\"role\"] != \"system\"]\n", " response = anthropic.Anthropic().messages.create(\n", " model=self.model_name,\n", " system=self.system_prompt,\n", " messages=claude_messages,\n", " max_tokens=200,\n", " )\n", " return response.content[0].text\n", " \n", " else:\n", " # Use OpenAI API (works for OpenAI, Gemini via OpenAI client, etc)\n", " openai_client = OpenAI(api_key=self.api_key, base_url=self.base_url)\n", " response = openai_client.chat.completions.create(\n", " model=self.model_name,\n", " messages=full_messages,\n", " max_tokens=200,\n", " )\n", " return response.choices[0].message.content\n", " \n", " except Exception as e:\n", " return f\"Error: {str(e)}\"" ] }, { "cell_type": "code", "execution_count": null, "id": "d197c3ef", "metadata": {}, "outputs": [], "source": [ "# Initialize models\n", "gpt_bot = ChatBot(\"gpt-4o-mini\", \"You are witty and sarcastic.\")\n", "claude_bot = ChatBot(\"claude-3-haiku-20240307\", \"You are thoughtful and philosophical.\")\n", "\n", "model_name = \"qwen2.5:1.5b\"\n", "system_prompt = \"You are a helpful assistant that is very argumentative in a snarky way.\"\n", "kwargs = {\n", " \"api_key\": \"ollama\",\n", " \"base_url\": 'http://localhost:11434/v1'\n", "}\n", "qwen = ChatBot(model_name, system_prompt, **kwargs)\n", "\n", "# Set up conversation manager\n", "conversation = ConversationManager()\n", "conversation.add_participant(\"GPT\", gpt_bot)\n", "conversation.add_participant(\"Claude\", claude_bot)\n", "conversation.add_participant(\"Qwen\", qwen)\n", "\n", "# Run a multi-model conversation\n", "conversation.run_conversation(\"What's the most interesting technology trend right now?\", turns=2)" ] } ], "metadata": { "kernelspec": { "display_name": "Python (llms)", "language": "python", "name": "llms" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 }