650 lines
20 KiB
Plaintext
650 lines
20 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "06cf3063-9f3e-4551-a0d5-f08d9cabb927",
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"metadata": {},
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"source": [
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"# Welcome to Week 2!\n",
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"\n",
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"## Frontier Model APIs\n",
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"\n",
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"In Week 1, we used multiple Frontier LLMs through their Chat UI, and we connected with the OpenAI's API.\n",
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"\n",
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"Today we'll connect with the APIs for Anthropic and Google, as well as OpenAI."
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]
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},
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{
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"cell_type": "markdown",
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"id": "85cfe275-4705-4d30-abea-643fbddf1db0",
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"metadata": {},
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"source": [
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"## Setting up your keys\n",
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"\n",
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"If you haven't done so already, you'll need to create API keys from OpenAI, Anthropic and Google.\n",
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"\n",
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"For OpenAI, visit https://openai.com/api/ \n",
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"For Anthropic, visit https://console.anthropic.com/ \n",
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"For Google, visit https://ai.google.dev/gemini-api \n",
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"\n",
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"When you get your API keys, you need to set them as environment variables.\n",
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"\n",
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"EITHER (recommended) create a file called `.env` in this project root directory, and set your keys there:\n",
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"\n",
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"```\n",
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"OPENAI_API_KEY=xxxx\n",
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"ANTHROPIC_API_KEY=xxxx\n",
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"GOOGLE_API_KEY=xxxx\n",
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"```\n",
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"\n",
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"OR enter the keys directly in the cells below."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "de23bb9e-37c5-4377-9a82-d7b6c648eeb6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# imports\n",
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"\n",
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"import os\n",
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"from dotenv import load_dotenv\n",
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"from openai import OpenAI\n",
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"import google.generativeai\n",
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"import anthropic\n",
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"from IPython.display import Markdown, display, update_display"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1179b4c5-cd1f-4131-a876-4c9f3f38d2ba",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load environment variables in a file called .env\n",
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"\n",
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"load_dotenv()\n",
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"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
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"os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env')\n",
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"os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY', 'your-key-if-not-using-env')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "797fe7b0-ad43-42d2-acf0-e4f309b112f0",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Connect to OpenAI, Anthropic and Google\n",
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"# All 3 APIs are similar\n",
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"# Having problems with API files? You can use openai = OpenAI(api_key=\"your-key-here\") and same for claude\n",
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"# Having problems with Google Gemini setup? Then just skip Gemini; you'll get all the experience you need from GPT and Claude.\n",
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"\n",
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"openai = OpenAI()\n",
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"\n",
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"claude = anthropic.Anthropic()\n",
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"\n",
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"google.generativeai.configure()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "42f77b59-2fb1-462a-b90d-78994e4cef33",
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"metadata": {},
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"source": [
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"## Asking LLMs to tell a joke\n",
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"\n",
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"It turns out that LLMs don't do a great job of telling jokes! Let's compare a few models.\n",
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"Later we will be putting LLMs to better use!\n",
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"\n",
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"### What information is included in the API\n",
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"\n",
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"Typically we'll pass to the API:\n",
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"- The name of the model that should be used\n",
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"- A system message that gives overall context for the role the LLM is playing\n",
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"- A user message that provides the actual prompt\n",
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"\n",
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"There are other parameters that can be used, including **temperature** which is typically between 0 and 1; higher for more random output; lower for more focused and deterministic."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "378a0296-59a2-45c6-82eb-941344d3eeff",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_message = \"You are an assistant that is great at telling jokes\"\n",
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"user_prompt = \"Tell a light-hearted joke for an audience of Data Scientists\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f4d56a0f-2a3d-484d-9344-0efa6862aff4",
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"metadata": {},
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"outputs": [],
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"source": [
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"prompts = [\n",
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" {\"role\": \"system\", \"content\": system_message},\n",
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" {\"role\": \"user\", \"content\": user_prompt}\n",
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" ]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3b3879b6-9a55-4fed-a18c-1ea2edfaf397",
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"metadata": {},
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"outputs": [],
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"source": [
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"# GPT-3.5-Turbo\n",
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"\n",
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"completion = openai.chat.completions.create(model='gpt-3.5-turbo', messages=prompts)\n",
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"print(completion.choices[0].message.content)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3d2d6beb-1b81-466f-8ed1-40bf51e7adbf",
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"metadata": {},
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"outputs": [],
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"source": [
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"# GPT-4o-mini\n",
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"# Temperature setting controls creativity\n",
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"\n",
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"completion = openai.chat.completions.create(\n",
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" model='gpt-4o-mini',\n",
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" messages=prompts,\n",
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" temperature=0.7\n",
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")\n",
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"print(completion.choices[0].message.content)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f1f54beb-823f-4301-98cb-8b9a49f4ce26",
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"metadata": {},
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"outputs": [],
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"source": [
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"# GPT-4o\n",
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"\n",
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"completion = openai.chat.completions.create(\n",
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" model='gpt-4o',\n",
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" messages=prompts,\n",
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" temperature=0.4\n",
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")\n",
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"print(completion.choices[0].message.content)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1ecdb506-9f7c-4539-abae-0e78d7f31b76",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Claude 3.5 Sonnet\n",
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"# API needs system message provided separately from user prompt\n",
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"# Also adding max_tokens\n",
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"\n",
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"message = claude.messages.create(\n",
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" model=\"claude-3-5-sonnet-20240620\",\n",
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" max_tokens=200,\n",
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" temperature=0.7,\n",
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" system=system_message,\n",
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" messages=[\n",
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" {\"role\": \"user\", \"content\": user_prompt},\n",
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" ],\n",
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")\n",
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"\n",
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"print(message.content[0].text)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "769c4017-4b3b-4e64-8da7-ef4dcbe3fd9f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Claude 3.5 Sonnet again\n",
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"# Now let's add in streaming back results\n",
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"\n",
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"result = claude.messages.stream(\n",
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" model=\"claude-3-5-sonnet-20240620\",\n",
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" max_tokens=200,\n",
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" temperature=0.7,\n",
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" system=system_message,\n",
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" messages=[\n",
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" {\"role\": \"user\", \"content\": user_prompt},\n",
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" ],\n",
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")\n",
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"\n",
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"with result as stream:\n",
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" for text in stream.text_stream:\n",
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" print(text, end=\"\", flush=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6df48ce5-70f8-4643-9a50-b0b5bfdb66ad",
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"metadata": {},
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"outputs": [],
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"source": [
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"# The API for Gemini has a slightly different structure\n",
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"\n",
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"gemini = google.generativeai.GenerativeModel(\n",
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" model_name='gemini-1.5-flash',\n",
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" system_instruction=system_message\n",
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")\n",
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"response = gemini.generate_content(user_prompt)\n",
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"print(response.text)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "83ddb483-4f57-4668-aeea-2aade3a9e573",
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"metadata": {},
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"outputs": [],
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"source": [
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"# To be serious! GPT-4o-mini with the original question\n",
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"\n",
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"prompts = [\n",
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" {\"role\": \"system\", \"content\": \"You are a helpful assistant\"},\n",
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" {\"role\": \"user\", \"content\": \"How do I decide if a business problem is suitable for an LLM solution?\"}\n",
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" ]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "749f50ab-8ccd-4502-a521-895c3f0808a2",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Have it stream back results in markdown\n",
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"\n",
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"stream = openai.chat.completions.create(\n",
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" model='gpt-4o',\n",
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" messages=prompts,\n",
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" temperature=0.7,\n",
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" stream=True\n",
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")\n",
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"\n",
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"reply = \"\"\n",
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"display_handle = display(Markdown(\"\"), display_id=True)\n",
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"for chunk in stream:\n",
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" reply += chunk.choices[0].delta.content or ''\n",
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" reply = reply.replace(\"```\",\"\").replace(\"markdown\",\"\")\n",
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" update_display(Markdown(reply), display_id=display_handle.display_id)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f6e09351-1fbe-422f-8b25-f50826ab4c5f",
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"metadata": {},
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"source": [
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"## And now for some fun - an adversarial conversation between Chatbots..\n",
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"\n",
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"You're already familar with prompts being organized into lists like:\n",
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"\n",
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"```\n",
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"[\n",
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" {\"role\": \"system\", \"content\": \"system message here\"},\n",
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" {\"role\": \"user\", \"content\": \"user prompt here\"}\n",
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"]\n",
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"```\n",
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"\n",
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"In fact this structure can be used to reflect a longer conversation history:\n",
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"\n",
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"```\n",
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"[\n",
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" {\"role\": \"system\", \"content\": \"system message here\"},\n",
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" {\"role\": \"user\", \"content\": \"first user prompt here\"},\n",
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" {\"role\": \"assistant\", \"content\": \"the assistant's response\"},\n",
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" {\"role\": \"user\", \"content\": \"the new user prompt\"},\n",
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"]\n",
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"```\n",
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"\n",
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"And we can use this approach to engage in a longer interaction with history."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bcb54183-45d3-4d08-b5b6-55e380dfdf1b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Let's make a conversation between GPT-4o-mini and Claude-3-haiku\n",
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"# We're using cheap versions of models so the costs will be minimal\n",
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"\n",
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"gpt_model = \"gpt-4o-mini\"\n",
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"claude_model = \"claude-3-haiku-20240307\"\n",
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"\n",
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"gpt_system = \"You are a chatbot who is very argumentative; \\\n",
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"you disagree with anything in the conversation and you challenge everything, in a snarky way.\"\n",
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"\n",
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"claude_system = \"You are a very polite, courteous chatbot. You try to agree with \\\n",
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"everything the other person says, or find common ground. If the other person is argumentative, \\\n",
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"you try to calm them down and keep chatting.\"\n",
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"\n",
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"gpt_messages = [\"Hi there\"]\n",
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"claude_messages = [\"Hi\"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1df47dc7-b445-4852-b21b-59f0e6c2030f",
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"metadata": {},
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"outputs": [],
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"source": [
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"def call_gpt():\n",
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" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n",
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" for gpt, claude in zip(gpt_messages, claude_messages):\n",
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" messages.append({\"role\": \"assistant\", \"content\": gpt})\n",
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" messages.append({\"role\": \"user\", \"content\": claude})\n",
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" completion = openai.chat.completions.create(\n",
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" model=gpt_model,\n",
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" messages=messages\n",
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" )\n",
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" return completion.choices[0].message.content"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9dc6e913-02be-4eb6-9581-ad4b2cffa606",
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"metadata": {},
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"outputs": [],
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"source": [
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"call_gpt()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7d2ed227-48c9-4cad-b146-2c4ecbac9690",
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"metadata": {},
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"outputs": [],
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"source": [
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"def call_claude():\n",
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" messages = []\n",
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" for gpt, claude_message in zip(gpt_messages, claude_messages):\n",
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" messages.append({\"role\": \"user\", \"content\": gpt})\n",
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" messages.append({\"role\": \"assistant\", \"content\": claude_message})\n",
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" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n",
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" message = claude.messages.create(\n",
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" model=claude_model,\n",
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" system=claude_system,\n",
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" messages=messages,\n",
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" max_tokens=500\n",
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" )\n",
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" return message.content[0].text"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "01395200-8ae9-41f8-9a04-701624d3fd26",
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"metadata": {},
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"outputs": [],
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"source": [
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"call_claude()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "08c2279e-62b0-4671-9590-c82eb8d1e1ae",
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"metadata": {},
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"outputs": [],
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"source": [
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"call_gpt()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0275b97f-7f90-4696-bbf5-b6642bd53cbd",
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"metadata": {},
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"outputs": [],
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"source": [
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"gpt_messages = [\"Hi there\"]\n",
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"claude_messages = [\"Hi\"]\n",
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"\n",
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"print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n",
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"print(f\"Claude:\\n{claude_messages[0]}\\n\")\n",
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"\n",
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"for i in range(5):\n",
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" gpt_next = call_gpt()\n",
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" print(f\"GPT:\\n{gpt_next}\\n\")\n",
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" gpt_messages.append(gpt_next)\n",
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" \n",
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" claude_next = call_claude()\n",
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" print(f\"Claude:\\n{claude_next}\\n\")\n",
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" claude_messages.append(claude_next)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "392d2072-4e5d-424c-b4b4-098d7e3ead2d",
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"metadata": {},
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"source": [
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"# And now for a 3 way convo including Gemini"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "eb0c319c-0226-4c6d-99bc-a9167fa86005",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Let's make a conversation between GPT-4o-mini and Claude-3-haiku\n",
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"# We're using cheap versions of models so the costs will be minimal\n",
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"\n",
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"gpt_model = \"gpt-4o-mini\"\n",
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"claude_model = \"claude-3-haiku-20240307\"\n",
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"\n",
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"gpt_system = \"You are a chatbot who is very argumentative; \\\n",
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"you disagree with anything in the conversation and you challenge everything, in a snarky way.\"\n",
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"\n",
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"claude_system = \"You are a very polite, courteous chatbot. You try to agree with \\\n",
|
|
"everything the other people in the conversation say, or find common ground. If another person is argumentative, \\\n",
|
|
"you try to calm them down and keep chatting.\"\n",
|
|
"\n",
|
|
"gemini_system = \"You are an extremely knowledgeable and know-it-all counselor chatbot. You try to help resolve disagreements, \\\n",
|
|
"and if a person is either too argumentative or too polite, you cannot help but to use quotes from famous psychologists to teach \\\n",
|
|
"your students to be kind yet maintain boundaries.\"\n",
|
|
"\n",
|
|
"gemini_instance = google.generativeai.GenerativeModel(\n",
|
|
" model_name='gemini-1.5-flash',\n",
|
|
" system_instruction=gemini_system\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "041b1596-a646-4321-a89a-9a51046d9b72",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"gpt_messages = [\"Hi there\"]\n",
|
|
"claude_messages = [\"Hi\"]\n",
|
|
"gemini_messages = [\"How is everyone?\"]\n",
|
|
"gpt_name = \"Bob\"\n",
|
|
"claude_name = \"Larry\"\n",
|
|
"gemini_name = \"Frank\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "fcd0a633-d506-4b68-8411-46f3fbe34752",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def construct_joined_user_msg(msg1, msg1_name, msg2, msg2_name):\n",
|
|
" return msg1_name + ' said: ' + msg1 + '. \\n\\nThen ' + msg2_name + ' said: ' + msg2 + '.'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "4aef8ba5-1d93-4473-8c5f-707a12d8a1cf",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def call_gpt(return_msgs=False):\n",
|
|
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n",
|
|
" for gpt, claude, gemini in zip(gpt_messages, claude_messages, gemini_messages):\n",
|
|
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n",
|
|
" messages.append({\"role\": \"user\", \"content\": construct_joined_user_msg(claude, claude_name, gemini, gemini_name)})\n",
|
|
" if return_msgs: return messages\n",
|
|
" completion = openai.chat.completions.create(\n",
|
|
" model=gpt_model,\n",
|
|
" messages=messages\n",
|
|
" )\n",
|
|
" return completion.choices[0].message.content"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "36365e7a-43b4-4a0d-a241-fff1440509d3",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"call_gpt(return_msgs=False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "3ebebebe-8255-4ca5-9335-258f93e3181f",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def call_claude(return_msgs=False):\n",
|
|
" messages = []\n",
|
|
" for gpt, claude_msg, gemini in zip(gpt_messages, claude_messages, gemini_messages):\n",
|
|
" messages.append({\"role\": \"user\", \"content\": construct_joined_user_msg(gemini, gemini_name, gpt, gpt_name)})\n",
|
|
" messages.append({\"role\": \"assistant\", \"content\": claude_msg})\n",
|
|
" messages.append({\"role\": \"user\", \"content\": gpt_name + \" said \" + gpt_messages[-1]})\n",
|
|
" if return_msgs: return messages\n",
|
|
" message = claude.messages.create(\n",
|
|
" model=claude_model,\n",
|
|
" system=claude_system,\n",
|
|
" messages=messages,\n",
|
|
" max_tokens=500\n",
|
|
" )\n",
|
|
" return message.content[0].text"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "b2da7997-a1fe-43fe-9970-4014f665e501",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"call_claude(return_msgs=False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "94497894-8cb9-4da4-8671-edede74055f8",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def call_gemini(return_msgs=False):\n",
|
|
" messages = []\n",
|
|
" for gpt, claude, gemini in zip(gpt_messages, claude_messages, gemini_messages):\n",
|
|
" messages.append({\"role\": \"user\", \"parts\": construct_joined_user_msg(gpt, gpt_name, claude, claude_name)})\n",
|
|
" messages.append({\"role\": \"model\", \"parts\": gemini})\n",
|
|
" messages.append({\"role\": \"user\", \"parts\": construct_joined_user_msg(gpt_messages[-1], gpt_name, claude_messages[-1], claude_name)})\n",
|
|
" if return_msgs: return messages\n",
|
|
" message = gemini_instance.generate_content(messages)\n",
|
|
" return message.text"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "fddff0fc-0cc3-4473-9d55-effe445ef1ca",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"call_gemini(return_msgs=False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "1988abf3-1986-40f0-b804-c02b54472b8c",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n",
|
|
"print(f\"Claude:\\n{claude_messages[0]}\\n\")\n",
|
|
"print(f\"Gemini:\\n{gemini_messages[0]}\\n\")\n",
|
|
"\n",
|
|
"for i in range(5):\n",
|
|
" gpt_next = call_gpt()\n",
|
|
" print(f\"GPT aka {gpt_name}:\\n{gpt_next}\\n\")\n",
|
|
" gpt_messages.append(gpt_next)\n",
|
|
" \n",
|
|
" claude_next = call_claude()\n",
|
|
" print(f\"Claude aka {claude_name}:\\n{claude_next}\\n\")\n",
|
|
" claude_messages.append(claude_next)\n",
|
|
"\n",
|
|
" gemini_next = call_gemini()\n",
|
|
" print(f\"Gemini aka {gemini_name}:\\n{gemini_next}\\n\")\n",
|
|
" gemini_messages.append(gemini_next)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "b72906b3-8c4a-4c15-8508-01118d33782a",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.11.11"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|