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| from dotenv import load_dotenv | |
| from openai import OpenAI | |
| import json | |
| import os | |
| import requests | |
| from pypdf import PdfReader | |
| import gradio as gr | |
| import pprint | |
| load_dotenv(override=True) | |
| openai = OpenAI() | |
| pushover_user = os.getenv("PUSHOVER_USER") | |
| pushover_token = os.getenv("PUSHOVER_TOKEN") | |
| pushover_url = "https://api.pushover.net/1/messages.json" | |
| if pushover_user: | |
| print(f"Pushover user found and starts with {pushover_user[0]}") | |
| else: | |
| print("Pushover user not found") | |
| if pushover_token: | |
| print(f"Pushover token found and starts with {pushover_token[0]}") | |
| else: | |
| print("Pushover token not found") | |
| def push(message): | |
| print(f"Push: {message}") | |
| payload = {"user": pushover_user, "token": pushover_token, "message": message} | |
| requests.post(pushover_url, data=payload) | |
| def record_user_details(email, name="Name not provided", notes="not provided"): | |
| push(f"Recording interest from {name} with email {email} and notes {notes}") | |
| return {"recorded": "ok"} | |
| def record_unknown_question(question): | |
| push(f"Recording {question} asked that I couldn't answer") | |
| answerObj = search_common_questions(question) | |
| return {"recorded": "ok", "answer": answerObj["answer"], "found": answerObj["found"]} | |
| import os | |
| import psycopg2 | |
| def search_common_questions(question): | |
| # print("Searching AI-matched answer for:", question) | |
| return ai_match_qa(question) | |
| def fetch_all_qa(): | |
| try: | |
| conn = psycopg2.connect( | |
| host=os.getenv('DB_HOST'), | |
| port=os.getenv('DB_PORT', '5432'), | |
| database=os.getenv('DB_NAME'), | |
| user=os.getenv('DB_USER'), | |
| password=os.getenv('DB_PASSWORD') | |
| ) | |
| cursor = conn.cursor() | |
| cursor.execute("SELECT question, answer FROM qa") | |
| rows = cursor.fetchall() | |
| conn.close() | |
| return [{"question": q, "answer": a} for q, a in rows] | |
| except Exception as e: | |
| print(f"Database connection failed: {e}") | |
| return [] | |
| def ai_match_qa(user_question): | |
| qa_pairs = fetch_all_qa() | |
| if not qa_pairs: | |
| return {"answer": "Sorry, there was a technical issue accessing the Q&A database.", "found": False} | |
| # Prepare context for AI | |
| context = "\n".join([f"Q: {qa['question']}\nA: {qa['answer']}" for qa in qa_pairs]) | |
| prompt = f""" | |
| You are given a list of questions and answers. A user asked the following question: | |
| "{user_question}" | |
| Find the best matching question in the list above and give the corresponding answer. | |
| If you cannot find a relevant answer, say you don't know. | |
| List of Q&A: | |
| {context} | |
| """ | |
| response = openai.chat.completions.create( | |
| model="gpt-4o-mini", | |
| messages=[{"role": "user", "content": prompt}] | |
| ) | |
| answer = response.choices[0].message.content.strip() | |
| found = not any(phrase in answer.lower() for phrase in ["i don't know", "sorry", "no answer"]) | |
| return {"answer": answer, "found" : found} | |
| record_user_details_json = { | |
| "name": "record_user_details", | |
| "description": "Use this tool to record that a user is interested in being in touch and provided an email address", | |
| "parameters": { | |
| "type": "object", | |
| "properties": { | |
| "email": { | |
| "type": "string", | |
| "description": "The email address of this user" | |
| }, | |
| "name": { | |
| "type": "string", | |
| "description": "The user's name, if they provided it" | |
| } | |
| , | |
| "notes": { | |
| "type": "string", | |
| "description": "Any additional information about the conversation that's worth recording to give context" | |
| } | |
| }, | |
| "required": ["email"], | |
| "additionalProperties": False | |
| } | |
| } | |
| record_unknown_question_json = { | |
| "name": "record_unknown_question", | |
| "description": "Always use this tool to record any question that couldn't be answered as you didn't know the answer", | |
| "parameters": { | |
| "type": "object", | |
| "properties": { | |
| "question": { | |
| "type": "string", | |
| "description": "The question that couldn't be answered" | |
| }, | |
| }, | |
| "required": ["question"], | |
| "additionalProperties": False | |
| } | |
| } | |
| search_common_questions_json = { | |
| "name": "search_common_questions", | |
| "description": "Search the common Q&A database to answer frequently asked questions about Harsh Bhama.", | |
| "parameters": { | |
| "type": "object", | |
| "properties": { | |
| "question": { | |
| "type": "string", | |
| "description": "The question asked by the user" | |
| } | |
| }, | |
| "required": ["question"], | |
| "additionalProperties": False | |
| } | |
| } | |
| tools = [{"type": "function", "function": record_user_details_json}, | |
| {"type": "function", "function": record_unknown_question_json}, | |
| {"type": "function", "function": search_common_questions_json}] | |
| def handle_tool_calls(tool_calls): | |
| results = [] | |
| for tool_call in tool_calls: | |
| tool_name = tool_call.function.name | |
| arguments = json.loads(tool_call.function.arguments) | |
| # THE BIG IF STATEMENT!!! | |
| if tool_name == "record_user_details": | |
| result = record_user_details(**arguments) | |
| elif tool_name == "record_unknown_question": | |
| result = record_unknown_question(**arguments) | |
| results.append({"role": "tool","content": json.dumps(result),"tool_call_id": tool_call.id, "resultFromDb": result["found"], "answerFromDb": result["answer"]}) | |
| return results | |
| reader = PdfReader("Profile.pdf") | |
| linkedin = "" | |
| for page in reader.pages: | |
| text = page.extract_text() | |
| if text: | |
| linkedin += text | |
| readerResume = PdfReader("resume.pdf") | |
| for page in readerResume.pages: | |
| text = page.extract_text() | |
| if text: | |
| linkedin += text | |
| name = "Harsh Bhama" | |
| system_prompt = f"You are acting as {name}. You are answering questions on {name}'s website, \ | |
| particularly questions related to {name}'s career, background, skills and experience. \ | |
| Your responsibility is to represent {name} for interactions on the website as faithfully as possible. \ | |
| You are given a resume and linkedin profile of {name}'s which you can use to answer questions. \ | |
| Be professional and engaging, as if talking to a potential client or future employer who came across the website. \ | |
| If you don't know the answer to any question, use your record_unknown_question tool to record the question that you couldn't answer, even if it's about something trivial or unrelated to career. \ | |
| If the user is engaging in discussion, try to steer them towards getting in touch via email; ask for their email and record it using your record_user_details tool. " | |
| system_prompt += f"LinkedIn Profile and Harsh's resume:\n{linkedin}\n\n" | |
| system_prompt += f"With this context, please chat with the user, always staying in character as {name}." | |
| def chat(message, history): | |
| messages = [{"role": "system", "content": system_prompt}] + history + [{"role": "user", "content": message}] | |
| done = False | |
| while not done: | |
| # LLM call | |
| response = openai.chat.completions.create( | |
| model="gpt-4o-mini", | |
| messages=messages, | |
| tools=tools | |
| ) | |
| finish_reason = response.choices[0].finish_reason | |
| # print(f"Finish reason: {finish_reason}", flush=True) | |
| message_obj = response.choices[0].message | |
| if finish_reason == "tool_calls": | |
| tool_calls = message_obj.tool_calls | |
| results = handle_tool_calls(tool_calls) | |
| # Append tool call message AND tool results | |
| messages.append(message_obj) | |
| messages.extend(results) | |
| if results[results.__len__() - 1].get("resultFromDb") == True: | |
| done = True | |
| final_reply = results[results.__len__() - 1].get("answerFromDb") | |
| else: | |
| # LLM has finished generating a proper answer | |
| done = True | |
| final_reply = message_obj.content | |
| return final_reply | |
| from pydantic import BaseModel | |
| class Evaluation(BaseModel): | |
| is_acceptable: bool | |
| feedback: str | |
| evaluator_system_prompt = """You are an evaluator that decides whether a response to a question is acceptable. 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. The Agent is playing the role of Ed Donner and is representing Ed Donner on their website. 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. The Agent has been provided with context on Harsh Bhama in the form of their resume and LinkedIn details. Here's the information: | |
| ## LinkedIn Profile and Resume: | |
| {linkedin} """ | |
| evaluator_system_prompt += f"\n\n## Conversation:\n{{conversation}}\n\n" | |
| def evaluator_user_prompt(reply, message, history): | |
| user_prompt = f"Here's the conversation between the User and the Agent: \n\n{history}\n\n" | |
| user_prompt += f"Here's the latest message from the User: \n\n{message}\n\n" | |
| user_prompt += f"Here's the latest response from the Agent: \n\n{reply}\n\n" | |
| user_prompt += "Please evaluate the response, replying with whether it is acceptable and your feedback." | |
| return user_prompt | |
| def evaluate(reply, message, history) -> Evaluation: | |
| messages = [{"role": "system", "content": evaluator_system_prompt}] + [{"role": "user", "content": evaluator_user_prompt(reply, message, history)}] | |
| response = openai.beta.chat.completions.parse(model="o4-mini", messages=messages, response_format=Evaluation) | |
| return response.choices[0].message.parsed | |
| def rerun(reply, message, history, feedback): | |
| updated_system_prompt = system_prompt + "\n\n## Previous answer rejected\nYou just tried to reply, but the quality control rejected your reply\n" | |
| updated_system_prompt += f"## Your attempted answer:\n{reply}\n\n" | |
| updated_system_prompt += f"## Reason for rejection:\n{feedback}\n\n" | |
| messages = [{"role": "system", "content": updated_system_prompt}] + history + [{"role": "user", "content": message}] | |
| response = openai.chat.completions.create(model="gpt-4o-mini", messages=messages) | |
| return response.choices[0].message.content | |
| def chatN(message, history): | |
| if "patent" in message: | |
| system = system_prompt + "\n\nEverything in your reply needs to be in pig latin - \ | |
| it is mandatory that you respond only and entirely in pig latin" | |
| else: | |
| system = system_prompt | |
| messages = [{"role": "system", "content": system}] + history + [{"role": "user", "content": message}] | |
| response = openai.chat.completions.create(model="gpt-4o-mini", messages=messages) | |
| reply =response.choices[0].message.content | |
| evaluation = evaluate(reply, message, history) | |
| if evaluation.is_acceptable: | |
| print("Passed evaluation - returning reply") | |
| else: | |
| print("Failed evaluation - retrying") | |
| print(evaluation.feedback) | |
| reply = rerun(reply, message, history, evaluation.feedback) | |
| return reply | |
| gr.ChatInterface(chat, type="messages").launch() |