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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
}