3 changed files with 524 additions and 0 deletions
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|
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{ |
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"cells": [ |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "a0adab93-e569-4af0-80f1-ce5b7a116507", |
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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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"%run week2/community-contributions/day1_class_definition.ipynb" |
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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": "4566399a-e16d-41cd-bef4-f34b811e6377", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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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.\"" |
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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": "cf3d34e9-f8a8-4a06-aa3a-8faeb5f81e68", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"gpt_startmessage = \"Hello\"\n", |
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"claude_startmessage = \"Hi\"\n" |
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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": "49335337-d713-4d9e-aba0-41a309c37699", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"print(f\"GPT:\\n{gpt_startmessage}\\n\")\n", |
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"print(f\"Claude:\\n{claude_startmessage}\\n\")\n", |
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"\n", |
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"# startMessage added as user role\n", |
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"gpt=GPT_Wrapper(gpt_system, gpt_startmessage)\n", |
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"claude=Claude_Wrapper(claude_system, claude_startmessage)\n", |
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"\n", |
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"initialMsg = [\n", |
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" {\"role\": \"system\", \"content\": gpt_system},\n", |
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" {\"role\": \"assistant\", \"content\": gpt_startmessage}\n", |
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"]\n", |
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"# Replace user for assistant role\n", |
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"gpt.messageSet(initialMsg)\n", |
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"claude.messageSet([{\"role\": \"assistant\", \"content\": claude_startmessage}])\n", |
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"\n", |
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"claude_next=claude_startmessage\n", |
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"for i in range(5):\n", |
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" gpt.messageAppend(\"user\", claude_next)\n", |
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" gpt_next = gpt.getResult()\n", |
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" print(f\"GPT:\\n{gpt_next}\\n\")\n", |
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" gpt.messageAppend(\"assistant\", gpt_next)\n", |
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"\n", |
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" claude.messageAppend(\"user\", gpt_next)\n", |
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" claude_next = claude.getResult()\n", |
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" print(f\"Claude:\\n{claude_next}\\n\")\n", |
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" claude.messageAppend(\"assistant\", claude_next)" |
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] |
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} |
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], |
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"metadata": { |
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"kernelspec": { |
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"display_name": "Python 3 (ipykernel)", |
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"language": "python", |
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"name": "python3" |
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}, |
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"language_info": { |
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"codemirror_mode": { |
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"name": "ipython", |
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"version": 3 |
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}, |
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"file_extension": ".py", |
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"mimetype": "text/x-python", |
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"name": "python", |
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"nbconvert_exporter": "python", |
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"pygments_lexer": "ipython3", |
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"version": "3.11.11" |
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} |
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}, |
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"nbformat": 4, |
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"nbformat_minor": 5 |
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} |
@ -0,0 +1,116 @@
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{ |
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"cells": [ |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "a0adab93-e569-4af0-80f1-ce5b7a116507", |
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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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"%run week2/community-contributions/day1_class_definition.ipynb" |
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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": "4566399a-e16d-41cd-bef4-f34b811e6377", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"system_msg = \"You are an assistant that is great at telling jokes\"\n", |
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"user_msg = \"Tell a light-hearted joke for an audience of Software Engineers\"" |
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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": "362759bc-ce43-4f54-b8e2-1dab19c66a62", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Easy to instantiate and use, just create an object \n", |
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"# using the right Wrapper" |
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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": "a6e5468e-1f1d-40e4-afae-c292abc26c12", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"gpt=GPT_Wrapper(system_msg, user_msg)\n", |
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"print(\"GPT: \" + gpt.getResult())\n" |
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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": "e650839a-7bc4-4b6c-b6ea-e836644b076f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"claude=Claude_Wrapper(system_msg, user_msg)\n", |
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"print(\"Claude: \" + claude.getResult())\n" |
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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": "49335337-d713-4d9e-aba0-41a309c37699", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"gemini=Gemini_Wrapper(system_msg, user_msg)\n", |
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"print(\"Gemini: \" + gemini.getResult())\n" |
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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": "31d11b7b-5d14-4e3d-88e1-29239b667f3f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"ollama=Ollama_Wrapper(system_msg, user_msg)\n", |
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"print(\"Ollama: \" + ollama.getResult())\n" |
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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": "282efb89-23b0-436e-8458-d6aef7d23117", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"#Easy to change the prompt and reuse\n", |
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"\n", |
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"ollama.setUserPrompt(\"Tell a light-hearted joke for an audience of Managers\")\n", |
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"print(\"Ollama: \" + ollama.getResult())" |
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] |
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} |
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], |
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"metadata": { |
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"kernelspec": { |
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"display_name": "Python 3 (ipykernel)", |
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"language": "python", |
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"name": "python3" |
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}, |
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"language_info": { |
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"codemirror_mode": { |
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"name": "ipython", |
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"version": 3 |
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}, |
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"file_extension": ".py", |
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"mimetype": "text/x-python", |
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"name": "python", |
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"nbconvert_exporter": "python", |
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"pygments_lexer": "ipython3", |
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"version": "3.11.11" |
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} |
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}, |
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"nbformat": 4, |
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"nbformat_minor": 5 |
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} |
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{ |
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"cells": [ |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "a0adab93-e569-4af0-80f1-ce5b7a116507", |
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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" |
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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": "9f583520-3c49-4e79-84ae-02bfc57f1e49", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Creating a set of classes to simplify LLM use\n", |
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"\n", |
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"from abc import ABC, abstractmethod\n", |
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"from dotenv import load_dotenv\n", |
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"# Imports for type definition\n", |
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"from collections.abc import MutableSequence\n", |
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"from typing import TypedDict\n", |
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"\n", |
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"class LLM_Wrapper(ABC):\n", |
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" \"\"\"\n", |
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" The parent (abstract) class to specific LLM classes, normalising and providing common \n", |
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" and simplified ways to call LLMs while adding some level of abstraction on\n", |
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" specifics\n", |
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" \"\"\"\n", |
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"\n", |
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" MessageEntry = TypedDict('MessageEntry', {'role': str, 'content': str})\n", |
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" \n", |
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" system_prompt: str # The system prompt used for the LLM\n", |
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" user_prompt: str # The user prompt\n", |
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" __api_key: str # The (private) api key\n", |
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" temperature: float = 0.5 # Default temperature\n", |
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" __msg: MutableSequence[MessageEntry] # Message builder\n", |
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"\n", |
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" def __init__(self, system_prompt:str, user_prompt:str, env_apikey_var:str=None):\n", |
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" \"\"\"\n", |
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" env_apikey_var: str # The name of the env variable where to find the api_key\n", |
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" # We store the retrieved api_key for future calls\n", |
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" \"\"\"\n", |
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" self.system_prompt = system_prompt\n", |
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" self.user_prompt = user_prompt\n", |
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" if env_apikey_var:\n", |
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" load_dotenv(override=True)\n", |
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" self.__api_key = os.getenv(env_apikey_var)\n", |
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"\n", |
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" # # API Key format check\n", |
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" # if env_apikey_var and self.__api_key:\n", |
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" # print(f\"API Key exists and begins {self.__api_key[:8]}\")\n", |
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" # else:\n", |
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" # print(\"API Key not set\")\n", |
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" \n", |
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" def setSystemPrompt(self, prompt:str):\n", |
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" self.system_prompt = prompt\n", |
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"\n", |
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" def setUserPrompt(self, prompt:str):\n", |
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" self.user_prompt = prompt\n", |
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"\n", |
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" def setTemperature(self, temp:float):\n", |
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" self.temperature = temp\n", |
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"\n", |
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" def getKey(self) -> str:\n", |
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" return self.__api_key\n", |
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"\n", |
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" def messageSet(self, message: MutableSequence[MessageEntry]):\n", |
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" self.__msg = message\n", |
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"\n", |
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" def messageAppend(self, role: str, content: str):\n", |
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" self.__msg.append(\n", |
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" {\"role\": role, \"content\": content}\n", |
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" )\n", |
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"\n", |
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" def messageGet(self) -> MutableSequence[MessageEntry]:\n", |
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" return self.__msg\n", |
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" \n", |
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" @abstractmethod\n", |
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" def getResult(self):\n", |
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" pass\n", |
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"\n" |
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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": "a707f3ef-8696-44a9-943e-cfbce24b9fde", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"from openai import OpenAI\n", |
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"\n", |
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"class GPT_Wrapper(LLM_Wrapper):\n", |
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"\n", |
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" MODEL:str = 'gpt-4o-mini'\n", |
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" llm:OpenAI\n", |
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"\n", |
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" def __init__(self, system_prompt:str, user_prompt:str):\n", |
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" super().__init__(system_prompt, user_prompt, \"OPENAI_API_KEY\")\n", |
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" self.llm = OpenAI()\n", |
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" super().messageSet([\n", |
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" {\"role\": \"system\", \"content\": self.system_prompt},\n", |
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" {\"role\": \"user\", \"content\": self.user_prompt}\n", |
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" ])\n", |
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"\n", |
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"\n", |
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" def setSystemPrompt(self, prompt:str):\n", |
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" super().setSystemPrompt(prompt)\n", |
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" super().messageSet([\n", |
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" {\"role\": \"system\", \"content\": self.system_prompt},\n", |
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" {\"role\": \"user\", \"content\": self.user_prompt}\n", |
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" ])\n", |
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"\n", |
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" def setUserPrompt(self, prompt:str):\n", |
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" super().setUserPrompt(prompt)\n", |
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" super().messageSet([\n", |
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" {\"role\": \"system\", \"content\": self.system_prompt},\n", |
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" {\"role\": \"user\", \"content\": self.user_prompt}\n", |
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" ])\n", |
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"\n", |
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" def getResult(self, format=None):\n", |
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" \"\"\"\n", |
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" format is sent as an adittional parameter {\"type\", format}\n", |
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" e.g. json_object\n", |
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" \"\"\"\n", |
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" if format:\n", |
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" response = self.llm.chat.completions.create(\n", |
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" model=self.MODEL,\n", |
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" messages=super().messageGet(),\n", |
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" temperature=self.temperature,\n", |
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" response_format={\"type\": \"json_object\"}\n", |
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" )\n", |
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" if format == \"json_object\":\n", |
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" result = json.loads(response.choices[0].message.content)\n", |
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" else:\n", |
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" result = response.choices[0].message.content\n", |
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" else:\n", |
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" response = self.llm.chat.completions.create(\n", |
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" model=self.MODEL,\n", |
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" messages=super().messageGet(),\n", |
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" temperature=self.temperature\n", |
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" )\n", |
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" result = response.choices[0].message.content\n", |
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" return result" |
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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": "a8529004-0d6a-480c-9634-7d51498255fe", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import ollama\n", |
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"\n", |
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"class Ollama_Wrapper(LLM_Wrapper):\n", |
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"\n", |
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" MODEL:str = 'llama3.2'\n", |
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"\n", |
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" def __init__(self, system_prompt:str, user_prompt:str):\n", |
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" super().__init__(system_prompt, user_prompt, None)\n", |
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" self.llm=ollama\n", |
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" super().messageSet([\n", |
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" {\"role\": \"system\", \"content\": self.system_prompt},\n", |
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" {\"role\": \"user\", \"content\": self.user_prompt}\n", |
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" ])\n", |
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"\n", |
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"\n", |
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" def setSystemPrompt(self, prompt:str):\n", |
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" super().setSystemPrompt(prompt)\n", |
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" super().messageSet([\n", |
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" {\"role\": \"system\", \"content\": self.system_prompt},\n", |
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" {\"role\": \"user\", \"content\": self.user_prompt}\n", |
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" ])\n", |
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"\n", |
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" def setUserPrompt(self, prompt:str):\n", |
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" super().setUserPrompt(prompt)\n", |
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" super().messageSet([\n", |
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" {\"role\": \"system\", \"content\": self.system_prompt},\n", |
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" {\"role\": \"user\", \"content\": self.user_prompt}\n", |
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" ])\n", |
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"\n", |
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" def getResult(self, format=None):\n", |
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" \"\"\"\n", |
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" format is sent as an adittional parameter {\"type\", format}\n", |
||||
" e.g. json_object\n", |
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" \"\"\"\n", |
||||
" response = self.llm.chat(\n", |
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" model=self.MODEL, \n", |
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" messages=super().messageGet()\n", |
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" )\n", |
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" result = response['message']['content']\n", |
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" return result" |
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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": "f25ffb7e-0132-46cb-ad5b-18a300a7eb51", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import anthropic\n", |
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"\n", |
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"class Claude_Wrapper(LLM_Wrapper):\n", |
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"\n", |
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" MODEL:str = 'claude-3-5-haiku-20241022'\n", |
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" MAX_TOKENS:int = 200\n", |
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" llm:anthropic.Anthropic\n", |
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"\n", |
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" def __init__(self, system_prompt:str, user_prompt:str):\n", |
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" super().__init__(system_prompt, user_prompt, \"ANTHROPIC_API_KEY\")\n", |
||||
" self.llm = anthropic.Anthropic()\n", |
||||
" super().messageSet([\n", |
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" {\"role\": \"user\", \"content\": self.user_prompt}\n", |
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" ])\n", |
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"\n", |
||||
" def setSystemPrompt(self, prompt:str):\n", |
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" super().setSystemPrompt(prompt)\n", |
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"\n", |
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" def setUserPrompt(self, prompt:str):\n", |
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" super().setUserPrompt(prompt)\n", |
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" super().messageSet([\n", |
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" {\"role\": \"user\", \"content\": self.user_prompt}\n", |
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" ])\n", |
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"\n", |
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" def getResult(self, format=None):\n", |
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" \"\"\"\n", |
||||
" format is sent as an adittional parameter {\"type\", format}\n", |
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" e.g. json_object\n", |
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" \"\"\"\n", |
||||
" response = self.llm.messages.create(\n", |
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" model=self.MODEL,\n", |
||||
" max_tokens=self.MAX_TOKENS,\n", |
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" temperature=self.temperature,\n", |
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" system=self.system_prompt,\n", |
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" messages=super().messageGet()\n", |
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" )\n", |
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" result = response.content[0].text\n", |
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" return result" |
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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": "4379f1c0-6eeb-4611-8f34-a7303546ab71", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
||||
"import google.generativeai\n", |
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"\n", |
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"class Gemini_Wrapper(LLM_Wrapper):\n", |
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"\n", |
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" MODEL:str = 'gemini-1.5-flash'\n", |
||||
" llm:google.generativeai.GenerativeModel\n", |
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"\n", |
||||
" def __init__(self, system_prompt:str, user_prompt:str):\n", |
||||
" super().__init__(system_prompt, user_prompt, \"GOOGLE_API_KEY\")\n", |
||||
" self.llm = google.generativeai.GenerativeModel(\n", |
||||
" model_name=self.MODEL,\n", |
||||
" system_instruction=self.system_prompt\n", |
||||
" )\n", |
||||
" google.generativeai.configure(api_key=super().getKey())\n", |
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"\n", |
||||
" def setSystemPrompt(self, prompt:str):\n", |
||||
" super().setSystemPrompt(prompt)\n", |
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"\n", |
||||
" def setUserPrompt(self, prompt:str):\n", |
||||
" super().setUserPrompt(prompt)\n", |
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"\n", |
||||
" def getResult(self, format=None):\n", |
||||
" \"\"\"\n", |
||||
" format is sent as an adittional parameter {\"type\", format}\n", |
||||
" e.g. json_object\n", |
||||
" \"\"\"\n", |
||||
" response = self.llm.generate_content(self.user_prompt)\n", |
||||
" result = response.text\n", |
||||
" return result" |
||||
] |
||||
} |
||||
], |
||||
"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 |
||||
} |
Loading…
Reference in new issue