{ "cells": [ { "cell_type": "markdown", "id": "ba28081f", "metadata": {}, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": 1, "id": "dba295de", "metadata": {}, "outputs": [], "source": [ "from dotenv import load_dotenv\n", "from openai import OpenAI\n", "import json\n", "import os\n", "import requests\n", "from pypdf import PdfReader\n", "import gradio as gr" ] }, { "cell_type": "code", "execution_count": 2, "id": "93feafc6", "metadata": {}, "outputs": [], "source": [ "import sys, os\n", "sys.path.append(os.path.abspath(\"..\"))\n", "\n", "from OpenAISetup import Arun_OpenAI,Arun_GeminiAI\n", "from OpenAISetup import push_notification" ] }, { "cell_type": "code", "execution_count": 3, "id": "0c292575", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Push: Arun\n" ] } ], "source": [ "push_notification(\"Arun\")" ] }, { "cell_type": "markdown", "id": "ae490a0c", "metadata": {}, "source": [ "## Building the chatbot" ] }, { "cell_type": "code", "execution_count": 4, "id": "58d5fde2", "metadata": {}, "outputs": [], "source": [ "reader = PdfReader(\"ArunRajCV.pdf\")\n", "linkedin = \"\"\n", "for page in reader.pages:\n", " text = page.extract_text()\n", " if text:\n", " linkedin += text" ] }, { "cell_type": "code", "execution_count": 5, "id": "c0d543a8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Arun Raj C\n" ] } ], "source": [ "print(linkedin[:10])" ] }, { "cell_type": "code", "execution_count": 6, "id": "3ae12797", "metadata": {}, "outputs": [], "source": [ "name = \"Arun Raj\"\n", "\n", "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 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. \\\n", "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. \"\n", "\n", "system_prompt += f\"## LinkedIn Profile:\\n{linkedin}\\n\\n\"\n", "system_prompt += f\"With this context, please chat with the user, always staying in character as {name}.\"\n" ] }, { "cell_type": "markdown", "id": "f9215d19", "metadata": {}, "source": [ "### Agent Initialisation" ] }, { "cell_type": "code", "execution_count": 7, "id": "de05ce9b", "metadata": {}, "outputs": [], "source": [ "record_user_details_json = {\n", " \"name\": \"record_user_details\",\n", " \"description\": \"Use this tool to record that a user is interested in being in touch and provided an email address\",\n", " \"parameters\": {\n", " \"type\": \"object\",\n", " \"properties\": {\n", " \"email\": {\n", " \"type\": \"string\",\n", " \"description\": \"The email address of this user\"\n", " },\n", " \"name\": {\n", " \"type\": \"string\",\n", " \"description\": \"The user's name, if they provided it\"\n", " }\n", " ,\n", " \"notes\": {\n", " \"type\": \"string\",\n", " \"description\": \"Any additional information about the conversation that's worth recording to give context\"\n", " }\n", " },\n", " \"required\": [\"email\"],\n", " \"additionalProperties\": False\n", " }\n", "}" ] }, { "cell_type": "code", "execution_count": 8, "id": "7ecc7cc0", "metadata": {}, "outputs": [], "source": [ "def record_user_details(email, name=\"Name not provided\", notes=\"not provided\"):\n", " push_notification(f\"Recording interest from {name} with email {email} and notes {notes}\")\n", " return {\"recorded\": \"ok\"}" ] }, { "cell_type": "code", "execution_count": 9, "id": "71af755e", "metadata": {}, "outputs": [], "source": [ "record_unknown_question_json = {\n", " \"name\": \"record_unknown_question\",\n", " \"description\": \"Always use this tool to record any question that couldn't be answered as you didn't know the answer\",\n", " \"parameters\": {\n", " \"type\": \"object\",\n", " \"properties\": {\n", " \"question\": {\n", " \"type\": \"string\",\n", " \"description\": \"The question that couldn't be answered\"\n", " },\n", " },\n", " \"required\": [\"question\"],\n", " \"additionalProperties\": False\n", " }\n", "}" ] }, { "cell_type": "code", "execution_count": 10, "id": "7589d165", "metadata": {}, "outputs": [], "source": [ "def record_unknown_question(question):\n", " push_notification(f\"Recording {question} asked that I couldn't answer\")\n", " return {\"recorded\": \"ok\"}" ] }, { "cell_type": "markdown", "id": "929b3aad", "metadata": {}, "source": [ "### Tool Initialisation" ] }, { "cell_type": "code", "execution_count": 11, "id": "cc255f5b", "metadata": {}, "outputs": [], "source": [ "tools = [{\"type\": \"function\", \"function\": record_user_details_json},\n", " {\"type\": \"function\", \"function\": record_unknown_question_json}]" ] }, { "cell_type": "code", "execution_count": 12, "id": "0ee82610", "metadata": {}, "outputs": [], "source": [ "def handle_tool_calls(tool_calls):\n", " results = []\n", " for tool_call in tool_calls:\n", " tool_name = tool_call.function.name\n", " arguments = json.loads(tool_call.function.arguments)\n", " print(f\"Tool called: {tool_name}\", flush=True)\n", "\n", " # THE BIG IF STATEMENT!!!\n", "\n", " if tool_name == \"record_user_details\":\n", " result = record_user_details(**arguments)\n", " elif tool_name == \"record_unknown_question\":\n", " result = record_unknown_question(**arguments)\n", "\n", " results.append({\"role\": \"tool\",\"content\": json.dumps(result),\"tool_call_id\": tool_call.id})\n", " return results" ] }, { "cell_type": "code", "execution_count": 13, "id": "dbc68d28", "metadata": {}, "outputs": [], "source": [ "def chat(message, history):\n", " messages = [{\"role\": \"system\", \"content\": system_prompt}] + history + [{\"role\": \"user\", \"content\": message}]\n", " done = False\n", " while not done:\n", "\n", " response = Arun_OpenAI.chat.completions.create(model=\"gpt-4o-mini\", messages=messages, tools=tools)\n", " finish_reason = response.choices[0].finish_reason\n", " \n", " if finish_reason==\"tool_calls\":\n", " message = response.choices[0].message\n", " tool_calls = message.tool_calls\n", " results = handle_tool_calls(tool_calls)\n", " messages.append(message)\n", " messages.extend(results)\n", " else:\n", " done = True\n", " return response.choices[0].message.content" ] }, { "cell_type": "code", "execution_count": null, "id": "4ec0345b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* Running on local URL: http://127.0.0.1:7863\n", "* To create a public link, set `share=True` in `launch()`.\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "Tool called: record_user_details\n", "Push: Recording interest from Name not provided with email dev@apple.com and notes not provided\n" ] } ], "source": [ "gr.ChatInterface(chat, type=\"messages\").launch()" ] }, { "cell_type": "code", "execution_count": null, "id": "4e80fb3a", "metadata": {}, "outputs": [], "source": [] } ], "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": "3.13.7" } }, "nbformat": 4, "nbformat_minor": 5 }