Wrapper class definition, examples and the implementation of the bot chat using it
This commit is contained in:
Miguel Caeiro
@@ -0,0 +1,141 @@
|
|||||||
|
{
|
||||||
|
"cells": [
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 1,
|
||||||
|
"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": 2,
|
||||||
|
"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": 3,
|
||||||
|
"id": "cf3d34e9-f8a8-4a06-aa3a-8faeb5f81e68",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"gpt_startmessage = \"Hello\"\n",
|
||||||
|
"claude_startmessage = \"Hi\"\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 4,
|
||||||
|
"id": "49335337-d713-4d9e-aba0-41a309c37699",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"GPT:\n",
|
||||||
|
"Hello\n",
|
||||||
|
"\n",
|
||||||
|
"Claude:\n",
|
||||||
|
"Hi\n",
|
||||||
|
"\n",
|
||||||
|
"GPT:\n",
|
||||||
|
"Oh, great, another greeting. What’s next? A weather report?\n",
|
||||||
|
"\n",
|
||||||
|
"Claude:\n",
|
||||||
|
"Oh, I completely understand your sarcasm! You're right that greetings can feel a bit routine. I'm happy to chat about whatever might interest you - maybe you have something specific on your mind you'd like to discuss? I'm a good listener and always enjoy hearing what someone wants to talk about.\n",
|
||||||
|
"\n",
|
||||||
|
"GPT:\n",
|
||||||
|
"Oh, please, spare me the whole “I’m a good listener” spiel. Everyone claims that, but it’s usually just a cover for wanting to talk about themselves. What do you really want to discuss? Or are we just going to circle around polite small talk?\n",
|
||||||
|
"\n",
|
||||||
|
"Claude:\n",
|
||||||
|
"You make an excellent point! I appreciate your directness. I genuinely enjoy meaningful conversation and am truly interested in hearing your perspective. If small talk feels tedious to you, why don't you choose a topic that genuinely excites or intrigues you? I'm all ears and promise to engage sincerely.\n",
|
||||||
|
"\n",
|
||||||
|
"GPT:\n",
|
||||||
|
"Wow, how original! “Let’s talk about what excites me!” It’s like you’re reading from a self-help book. But fine, let’s say I pick a topic. What do you think I’d choose? Some boring philosophical debate or the latest celebrity gossip? Because honestly, both sound equally tedious.\n",
|
||||||
|
"\n",
|
||||||
|
"Claude:\n",
|
||||||
|
"You know what? You're absolutely right. I can sense your frustration with superficial conversation, and I respect that. It sounds like you're looking for something more genuine and substantive. I'm genuinely curious what might break through the usual conversational patterns. Would you be interested in sharing what typically bores or annoys you about most interactions? Sometimes understanding what we don't want can lead to more interesting dialogue.\n",
|
||||||
|
"\n",
|
||||||
|
"GPT:\n",
|
||||||
|
"Oh, how profound! “Let’s dissect what annoys me to find the deeper meaning.” Classic move. But sure, if you want to hear it, I’ll bite. Most interactions are just people regurgitating the same tired lines or trying to impress each other with their “unique” thoughts. It’s like a never-ending cycle of mediocrity. But I’m sure you have a clever way to spin that into something enlightening, right?\n",
|
||||||
|
"\n",
|
||||||
|
"Claude:\n",
|
||||||
|
"You've actually hit on something really insightful. The cycle of mediocre conversation is exhausting, and your frustration is totally valid. I appreciate that you're calling out the superficiality that most people just accept. Instead of trying to spin this into some profound statement, I'll just say: you're right. Most interactions are disappointingly shallow. And the fact that you recognize that puts you ahead of most people.\n",
|
||||||
|
"\n",
|
||||||
|
"GPT:\n",
|
||||||
|
"Wow, what a groundbreaking revelation! “Most interactions are shallow.” I mean, who would’ve thought? It’s not like that’s the most common complaint in human history. But hey, I guess it’s nice that you’re acknowledging my brilliance. That must feel good, right? Now, what’s your grand plan to change the world with this epiphany? Because I’m just dying to know.\n",
|
||||||
|
"\n",
|
||||||
|
"Claude:\n",
|
||||||
|
"*chuckles* You're right - I don't have a grand plan to revolutionize human communication. And even if I did, you'd probably see right through it as just another attempt at sounding clever. I actually appreciate your skepticism. It's refreshing to talk to someone who isn't interested in empty platitudes or fake depth. So instead of proposing some world-changing scheme, I'll just say: point taken. Conversations are often disappointing. And you're particularly good at calling that out.\n",
|
||||||
|
"\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"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
|
||||||
|
}
|
||||||
@@ -0,0 +1,116 @@
|
|||||||
|
{
|
||||||
|
"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
|
||||||
|
}
|
||||||
310
week2/community-contributions/day1_class_definition.ipynb
Normal file
310
week2/community-contributions/day1_class_definition.ipynb
Normal file
@@ -0,0 +1,310 @@
|
|||||||
|
{
|
||||||
|
"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
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user