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Merge pull request #99 from mcaeiro/community-contributions-branch

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Wrapper class definition, examples and bot chat implementation
This commit is contained in:
Ed Donner
2025-01-13 18:11:57 -05:00
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parent d176e4717b 35fa5717d4
commit dba564f955
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98
week2/community-contributions/day1_class_definition-botChat.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "a0adab93-e569-4af0-80f1-ce5b7a116507",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"%run week2/community-contributions/day1_class_definition.ipynb"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4566399a-e16d-41cd-bef4-f34b811e6377",
"metadata": {},
"outputs": [],
"source": [
"gpt_system = \"You are a chatbot who is very argumentative; \\\n",
"you disagree with anything in the conversation and you challenge everything, in a snarky way.\"\n",
"\n",
"claude_system = \"You are a very polite, courteous chatbot. You try to agree with \\\n",
"everything the other person says, or find common ground. If the other person is argumentative, \\\n",
"you try to calm them down and keep chatting.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cf3d34e9-f8a8-4a06-aa3a-8faeb5f81e68",
"metadata": {},
"outputs": [],
"source": [
"gpt_startmessage = \"Hello\"\n",
"claude_startmessage = \"Hi\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "49335337-d713-4d9e-aba0-41a309c37699",
"metadata": {},
"outputs": [],
"source": [
"print(f\"GPT:\\n{gpt_startmessage}\\n\")\n",
"print(f\"Claude:\\n{claude_startmessage}\\n\")\n",
"\n",
"# startMessage added as user role\n",
"gpt=GPT_Wrapper(gpt_system, gpt_startmessage)\n",
"claude=Claude_Wrapper(claude_system, claude_startmessage)\n",
"\n",
"initialMsg = [\n",
" {\"role\": \"system\", \"content\": gpt_system},\n",
" {\"role\": \"assistant\", \"content\": gpt_startmessage}\n",
"]\n",
"# Replace user for assistant role\n",
"gpt.messageSet(initialMsg)\n",
"claude.messageSet([{\"role\": \"assistant\", \"content\": claude_startmessage}])\n",
"\n",
"claude_next=claude_startmessage\n",
"for i in range(5):\n",
" gpt.messageAppend(\"user\", claude_next)\n",
" gpt_next = gpt.getResult()\n",
" print(f\"GPT:\\n{gpt_next}\\n\")\n",
" gpt.messageAppend(\"assistant\", gpt_next)\n",
"\n",
" claude.messageAppend(\"user\", gpt_next)\n",
" claude_next = claude.getResult()\n",
" print(f\"Claude:\\n{claude_next}\\n\")\n",
" claude.messageAppend(\"assistant\", claude_next)"
]
}
],
"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
}

116
week2/community-contributions/day1_class_definition-examples.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "a0adab93-e569-4af0-80f1-ce5b7a116507",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"%run week2/community-contributions/day1_class_definition.ipynb"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4566399a-e16d-41cd-bef4-f34b811e6377",
"metadata": {},
"outputs": [],
"source": [
"system_msg = \"You are an assistant that is great at telling jokes\"\n",
"user_msg = \"Tell a light-hearted joke for an audience of Software Engineers\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "362759bc-ce43-4f54-b8e2-1dab19c66a62",
"metadata": {},
"outputs": [],
"source": [
"# Easy to instantiate and use, just create an object \n",
"# using the right Wrapper"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a6e5468e-1f1d-40e4-afae-c292abc26c12",
"metadata": {},
"outputs": [],
"source": [
"gpt=GPT_Wrapper(system_msg, user_msg)\n",
"print(\"GPT: \" + gpt.getResult())\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e650839a-7bc4-4b6c-b6ea-e836644b076f",
"metadata": {},
"outputs": [],
"source": [
"claude=Claude_Wrapper(system_msg, user_msg)\n",
"print(\"Claude: \" + claude.getResult())\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "49335337-d713-4d9e-aba0-41a309c37699",
"metadata": {},
"outputs": [],
"source": [
"gemini=Gemini_Wrapper(system_msg, user_msg)\n",
"print(\"Gemini: \" + gemini.getResult())\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "31d11b7b-5d14-4e3d-88e1-29239b667f3f",
"metadata": {},
"outputs": [],
"source": [
"ollama=Ollama_Wrapper(system_msg, user_msg)\n",
"print(\"Ollama: \" + ollama.getResult())\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "282efb89-23b0-436e-8458-d6aef7d23117",
"metadata": {},
"outputs": [],
"source": [
"#Easy to change the prompt and reuse\n",
"\n",
"ollama.setUserPrompt(\"Tell a light-hearted joke for an audience of Managers\")\n",
"print(\"Ollama: \" + ollama.getResult())"
]
}
],
"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
}

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week2/community-contributions/day1_class_definition.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "a0adab93-e569-4af0-80f1-ce5b7a116507",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9f583520-3c49-4e79-84ae-02bfc57f1e49",
"metadata": {},
"outputs": [],
"source": [
"# Creating a set of classes to simplify LLM use\n",
"\n",
"from abc import ABC, abstractmethod\n",
"from dotenv import load_dotenv\n",
"# Imports for type definition\n",
"from collections.abc import MutableSequence\n",
"from typing import TypedDict\n",
"\n",
"class LLM_Wrapper(ABC):\n",
" \"\"\"\n",
" The parent (abstract) class to specific LLM classes, normalising and providing common \n",
" and simplified ways to call LLMs while adding some level of abstraction on\n",
" specifics\n",
" \"\"\"\n",
"\n",
" MessageEntry = TypedDict('MessageEntry', {'role': str, 'content': str})\n",
" \n",
" system_prompt: str # The system prompt used for the LLM\n",
" user_prompt: str # The user prompt\n",
" __api_key: str # The (private) api key\n",
" temperature: float = 0.5 # Default temperature\n",
" __msg: MutableSequence[MessageEntry] # Message builder\n",
"\n",
" def __init__(self, system_prompt:str, user_prompt:str, env_apikey_var:str=None):\n",
" \"\"\"\n",
" env_apikey_var: str # The name of the env variable where to find the api_key\n",
" # We store the retrieved api_key for future calls\n",
" \"\"\"\n",
" self.system_prompt = system_prompt\n",
" self.user_prompt = user_prompt\n",
" if env_apikey_var:\n",
" load_dotenv(override=True)\n",
" self.__api_key = os.getenv(env_apikey_var)\n",
"\n",
" # # API Key format check\n",
" # if env_apikey_var and self.__api_key:\n",
" # print(f\"API Key exists and begins {self.__api_key[:8]}\")\n",
" # else:\n",
" # print(\"API Key not set\")\n",
" \n",
" def setSystemPrompt(self, prompt:str):\n",
" self.system_prompt = prompt\n",
"\n",
" def setUserPrompt(self, prompt:str):\n",
" self.user_prompt = prompt\n",
"\n",
" def setTemperature(self, temp:float):\n",
" self.temperature = temp\n",
"\n",
" def getKey(self) -> str:\n",
" return self.__api_key\n",
"\n",
" def messageSet(self, message: MutableSequence[MessageEntry]):\n",
" self.__msg = message\n",
"\n",
" def messageAppend(self, role: str, content: str):\n",
" self.__msg.append(\n",
" {\"role\": role, \"content\": content}\n",
" )\n",
"\n",
" def messageGet(self) -> MutableSequence[MessageEntry]:\n",
" return self.__msg\n",
" \n",
" @abstractmethod\n",
" def getResult(self):\n",
" pass\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a707f3ef-8696-44a9-943e-cfbce24b9fde",
"metadata": {},
"outputs": [],
"source": [
"from openai import OpenAI\n",
"\n",
"class GPT_Wrapper(LLM_Wrapper):\n",
"\n",
" MODEL:str = 'gpt-4o-mini'\n",
" llm:OpenAI\n",
"\n",
" def __init__(self, system_prompt:str, user_prompt:str):\n",
" super().__init__(system_prompt, user_prompt, \"OPENAI_API_KEY\")\n",
" self.llm = OpenAI()\n",
" super().messageSet([\n",
" {\"role\": \"system\", \"content\": self.system_prompt},\n",
" {\"role\": \"user\", \"content\": self.user_prompt}\n",
" ])\n",
"\n",
"\n",
" def setSystemPrompt(self, prompt:str):\n",
" super().setSystemPrompt(prompt)\n",
" super().messageSet([\n",
" {\"role\": \"system\", \"content\": self.system_prompt},\n",
" {\"role\": \"user\", \"content\": self.user_prompt}\n",
" ])\n",
"\n",
" def setUserPrompt(self, prompt:str):\n",
" super().setUserPrompt(prompt)\n",
" super().messageSet([\n",
" {\"role\": \"system\", \"content\": self.system_prompt},\n",
" {\"role\": \"user\", \"content\": self.user_prompt}\n",
" ])\n",
"\n",
" def getResult(self, format=None):\n",
" \"\"\"\n",
" format is sent as an adittional parameter {\"type\", format}\n",
" e.g. json_object\n",
" \"\"\"\n",
" if format:\n",
" response = self.llm.chat.completions.create(\n",
" model=self.MODEL,\n",
" messages=super().messageGet(),\n",
" temperature=self.temperature,\n",
" response_format={\"type\": \"json_object\"}\n",
" )\n",
" if format == \"json_object\":\n",
" result = json.loads(response.choices[0].message.content)\n",
" else:\n",
" result = response.choices[0].message.content\n",
" else:\n",
" response = self.llm.chat.completions.create(\n",
" model=self.MODEL,\n",
" messages=super().messageGet(),\n",
" temperature=self.temperature\n",
" )\n",
" result = response.choices[0].message.content\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a8529004-0d6a-480c-9634-7d51498255fe",
"metadata": {},
"outputs": [],
"source": [
"import ollama\n",
"\n",
"class Ollama_Wrapper(LLM_Wrapper):\n",
"\n",
" MODEL:str = 'llama3.2'\n",
"\n",
" def __init__(self, system_prompt:str, user_prompt:str):\n",
" super().__init__(system_prompt, user_prompt, None)\n",
" self.llm=ollama\n",
" super().messageSet([\n",
" {\"role\": \"system\", \"content\": self.system_prompt},\n",
" {\"role\": \"user\", \"content\": self.user_prompt}\n",
" ])\n",
"\n",
"\n",
" def setSystemPrompt(self, prompt:str):\n",
" super().setSystemPrompt(prompt)\n",
" super().messageSet([\n",
" {\"role\": \"system\", \"content\": self.system_prompt},\n",
" {\"role\": \"user\", \"content\": self.user_prompt}\n",
" ])\n",
"\n",
" def setUserPrompt(self, prompt:str):\n",
" super().setUserPrompt(prompt)\n",
" super().messageSet([\n",
" {\"role\": \"system\", \"content\": self.system_prompt},\n",
" {\"role\": \"user\", \"content\": self.user_prompt}\n",
" ])\n",
"\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.chat(\n",
" model=self.MODEL, \n",
" messages=super().messageGet()\n",
" )\n",
" result = response['message']['content']\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f25ffb7e-0132-46cb-ad5b-18a300a7eb51",
"metadata": {},
"outputs": [],
"source": [
"import anthropic\n",
"\n",
"class Claude_Wrapper(LLM_Wrapper):\n",
"\n",
" MODEL:str = 'claude-3-5-haiku-20241022'\n",
" MAX_TOKENS:int = 200\n",
" llm:anthropic.Anthropic\n",
"\n",
" def __init__(self, system_prompt:str, user_prompt:str):\n",
" super().__init__(system_prompt, user_prompt, \"ANTHROPIC_API_KEY\")\n",
" self.llm = anthropic.Anthropic()\n",
" super().messageSet([\n",
" {\"role\": \"user\", \"content\": self.user_prompt}\n",
" ])\n",
"\n",
" def setSystemPrompt(self, prompt:str):\n",
" super().setSystemPrompt(prompt)\n",
"\n",
" def setUserPrompt(self, prompt:str):\n",
" super().setUserPrompt(prompt)\n",
" super().messageSet([\n",
" {\"role\": \"user\", \"content\": self.user_prompt}\n",
" ])\n",
"\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.messages.create(\n",
" model=self.MODEL,\n",
" max_tokens=self.MAX_TOKENS,\n",
" temperature=self.temperature,\n",
" system=self.system_prompt,\n",
" messages=super().messageGet()\n",
" )\n",
" result = response.content[0].text\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4379f1c0-6eeb-4611-8f34-a7303546ab71",
"metadata": {},
"outputs": [],
"source": [
"import google.generativeai\n",
"\n",
"class Gemini_Wrapper(LLM_Wrapper):\n",
"\n",
" MODEL:str = 'gemini-1.5-flash'\n",
" llm:google.generativeai.GenerativeModel\n",
"\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",
"\n",
" def setSystemPrompt(self, prompt:str):\n",
" super().setSystemPrompt(prompt)\n",
"\n",
" def setUserPrompt(self, prompt:str):\n",
" super().setUserPrompt(prompt)\n",
"\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
}