311 lines
11 KiB
Plaintext
311 lines
11 KiB
Plaintext
{
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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",
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" e.g. json_object\n",
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" \"\"\"\n",
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" 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",
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" self.llm = anthropic.Anthropic()\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 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",
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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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" response = self.llm.messages.create(\n",
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" model=self.MODEL,\n",
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" 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": [
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"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",
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" llm:google.generativeai.GenerativeModel\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, \"GOOGLE_API_KEY\")\n",
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" self.llm = google.generativeai.GenerativeModel(\n",
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" model_name=self.MODEL,\n",
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" system_instruction=self.system_prompt\n",
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" )\n",
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" google.generativeai.configure(api_key=super().getKey())\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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"\n",
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" def setUserPrompt(self, prompt:str):\n",
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" super().setUserPrompt(prompt)\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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" response = self.llm.generate_content(self.user_prompt)\n",
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" result = response.text\n",
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" return result"
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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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