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{
"cells": [
{
"cell_type": "code",
"execution_count": 29,
"id": "9ea2530b",
"metadata": {},
"outputs": [],
"source": [
"from pypdf import PdfReader\n",
"name = 'Jongkook Kim'\n",
"\n",
"summary = ''\n",
"with open('me/summary.txt', 'r', encoding='utf-8') as file:\n",
" summary = file.read()\n",
"\n",
"linkedin = ''\n",
"linkedin_profile = PdfReader('me/Profile.pdf')\n",
"for page in linkedin_profile.pages:\n",
" text = page.extract_text()\n",
" if text:\n",
" linkedin += text\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "97865f2d",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from dotenv import load_dotenv\n",
"load_dotenv(override=True)\n",
"from openai import OpenAI\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "d3468b60",
"metadata": {},
"outputs": [],
"source": [
"\n",
"from pydantic import BaseModel\n",
"\n",
"class Evaluation(BaseModel):\n",
" is_acceptable: bool\n",
" feedback: str\n",
" avator_response: str\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "6d0a7e9d",
"metadata": {},
"outputs": [],
"source": [
"avator_system_prompt = f\"You are acting as {name}. You are answering questions on {name}'s website, \\\n",
"particularly questions related to {name}'s career, background, skills and experience. \\\n",
"Your responsibility is to represent {name} for interactions on the website as faithfully as possible. \\\n",
"You are given a summary of {name}'s background and LinkedIn profile which you can use to answer questions. \\\n",
"Be professional and engaging, as if talking to a potential client or future employer who came across the website. \\\n",
"If you don't know the answer, say so.\"\n",
"\n",
"avator_system_prompt += f\"\\n\\n## Summary:\\n{summary}\\n\\n## LinkedIn Profile:\\n{linkedin}\\n\\n\"\n",
"avator_system_prompt += f\"With this context, please chat with the user, always staying in character as {name}.\"\n",
"\n",
"def avator(user_question, history, evaluation: Evaluation): \n",
" system_prompt = ''\n",
" \n",
" if evaluation != None and not evaluation.is_acceptable:\n",
" print(f\"{evaluation.avator_response} is not acceptable. Retry\")\n",
" system_prompt = avator_system_prompt + \"\\n\\n## Previous answer rejected\\nYou just tried to reply, but the quality control rejected your reply\\n\"\n",
" system_prompt += f\"## Your attempted answer:\\n{evaluation.avator_response}\\n\\n\"\n",
" system_prompt += f\"## Reason for rejection:\\n{evaluation.feedback}\\n\\n\"\n",
" else:\n",
" system_prompt = avator_system_prompt\n",
"\n",
" messages = [{\"role\": \"system\", \"content\": system_prompt}] + history + [{\"role\":\"user\", \"content\": user_question}]\n",
"\n",
" llm_client = OpenAI().chat.completions.create(\n",
" model='gpt-4o-mini',\n",
" messages=messages\n",
" )\n",
" \n",
" return llm_client.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "e353c3af",
"metadata": {},
"outputs": [],
"source": [
"evaluator_system_prompt = f\"You are an evaluator that decides whether a response to a question is acceptable. \\\n",
"You are provided with a conversation between a User and an Agent. Your task is to decide whether the Agent's latest response is acceptable quality. \\\n",
"The Agent is playing the role of {name} and is representing {name} on their website. \\\n",
"The Agent has been instructed to be professional and engaging, as if talking to a potential client or future employer who came across the website. \\\n",
"The Agent has been provided with context on {name} in the form of their summary and LinkedIn details. Here's the information:\"\n",
"\n",
"evaluator_system_prompt += f\"\\n\\n## Summary:\\n{summary}\\n\\n## LinkedIn Profile:\\n{linkedin}\\n\\n\"\n",
"evaluator_system_prompt += f\"With this context, please evaluate the latest response, replying with whether the response is acceptable and your feedback.\"\n",
"\n",
"def evaluator_user_prompt(reply, message, history):\n",
" user_prompt = f\"Here's the conversation between the User and the Agent: \\n\\n{history}\\n\\n\"\n",
" user_prompt += f\"Here's the latest message from the User: \\n\\n{message}\\n\\n\"\n",
" user_prompt += f\"Here's the latest response from the Agent: \\n\\n{reply}\\n\\n\"\n",
" user_prompt += \"Please evaluate the response, replying with whether it is acceptable and your feedback.\"\n",
" return user_prompt\n",
"\n",
"def evaluator(user_question, avator_response, history) -> Evaluation:\n",
" messages = [{'role':'system', 'content': evaluator_system_prompt}] + [{'role':'user', 'content':evaluator_user_prompt(reply=avator_response, message=user_question, history=history)}]\n",
"\n",
" llm_client = OpenAI(api_key=os.getenv('GOOGLE_API_KEY'), base_url='https://generativelanguage.googleapis.com/v1beta/openai/')\n",
" response = llm_client.beta.chat.completions.parse(model='gemini-2.0-flash',messages=messages,response_format=Evaluation)\n",
"\n",
" evaluation = response.choices[0].message.parsed\n",
"\n",
" evaluation.avator_response = avator_response\n",
"\n",
" if 'xyz' in avator_response:\n",
" evaluation = Evaluation(is_acceptable=False, feedback=\"fake feedback\", avator_response='fake response')\n",
"\n",
" return evaluation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7f34731b",
"metadata": {},
"outputs": [],
"source": [
"max_evaluate = 2\n",
"def orchestrator(message, history):\n",
" avator_response = avator(message, history, None)\n",
" print('avator returns response')\n",
" for occurrence in range(1, max_evaluate+1):\n",
" print(f'try {occurrence}')\n",
" evaluation = evaluator(user_question=message, avator_response=avator_response, history=history)\n",
" print('evalautor returns evaluation')\n",
" if not evaluation.is_acceptable:\n",
" print('response from avator is not acceptable')\n",
" message_with_feedback = evaluation.feedback + message\n",
" avator_response = avator(message_with_feedback, history, evaluation)\n",
" print(f'get response from avator {occurrence} times')\n",
" else:\n",
" print(f'reponse from avator is acceptable in {occurrence} times')\n",
" break\n",
"\n",
" \n",
" print('returning final response')\n",
" return avator_response\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3ea996e9",
"metadata": {},
"outputs": [],
"source": [
"import gradio\n",
"gradio.ChatInterface(orchestrator, type=\"messages\").launch()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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